Practice Management

More than 600,000 Americans have lost Medicaid coverage since pandemic protections ended on April 1. And a KFF Health News analysis of state data shows the vast majority were removed from state rolls for not completing paperwork.

Under normal circumstances, states review their Medicaid enrollment lists regularly to ensure every recipient qualifies for coverage. But because of a nationwide pause in those reviews during the pandemic, the health insurance program for low-income and disabled Americans kept people covered even if they no longer qualified.

Now, in what’s known as the Medicaid unwinding, states are combing through rolls and deciding who stays and who goes. People who are no longer eligible or don’t complete paperwork in time will be dropped.

The overwhelming majority of people who have lost coverage in most states were dropped because of technicalities, not because state officials determined they no longer meet Medicaid income limits. Four out of every five people dropped so far either never returned the paperwork or omitted required documents, according to a KFF Health News analysis of data from 11 states that provided details on recent cancellations. Now, lawmakers and advocates are expressing alarm over the volume of people losing coverage and, in some states, calling to pause the process.

KFF Health News sought data from the 19 states that started cancellations by May 1. Based on records from 14 states that provided detailed numbers, either in response to a public records request or by posting online, 36% of people whose eligibility was reviewed have been disenrolled.

In Indiana, 53,000 residents lost coverage in the first month of the unwinding, 89% for procedural reasons like not returning renewal forms. State Rep. Ed Clere, a Republican, expressed dismay at those “staggering numbers” in a May 24 Medicaid advisory group meeting, repeatedly questioning state officials about forms mailed to out-of-date addresses and urging them to give people more than 2 weeks’ notice before canceling their coverage.

Rep. Clere warned that the cancellations set in motion an avoidable revolving door. Some people dropped from Medicaid will have to forgo filling prescriptions and cancel doctor visits because they can’t afford care. Months down the line, after untreated chronic illnesses spiral out of control, they’ll end up in the emergency room where social workers will need to again help them join the program, he said.

Before the unwinding, more than one in four Americans – 93 million – were covered by Medicaid or CHIP, the Children’s Health Insurance Program, according to KFF Health News’ analysis of the latest enrollment data. Half of all kids are covered by the programs.

About 15 million people will be dropped over the next year as states review participants’ eligibility in monthly tranches.

Most people will find health coverage through new jobs or qualify for subsidized plans through the Affordable Care Act. But millions of others, including many children, will become uninsured and unable to afford basic prescriptions or preventive care. The uninsured rate among those under 65 is projected to rise from a historical low of 8.3% today to 9.3% next year, according to the Congressional Budget Office.

Because each state is handling the unwinding differently, the share of enrollees dropped in the first weeks varies widely.

Several states are first reviewing people officials believe are no longer eligible or who haven’t recently used their insurance. High cancellation rates in those states should level out as the agencies move on to people who likely still qualify.

In Utah, nearly 56% of people included in early reviews were dropped. In New Hampshire, 44% received cancellation letters within the first 2 months – almost all for procedural reasons, like not returning paperwork.

But New Hampshire officials found that thousands of people who didn’t fill out the forms indeed earn too much to qualify, according to Henry Lipman, the state’s Medicaid director. They would have been denied anyway. Even so, more people than he expected are not returning renewal forms. “That tells us that we need to change up our strategy,” said Mr. Lipman.

In other states, like Virginia and Nebraska, which aren’t prioritizing renewals by likely eligibility, about 90% have been renewed.

Because of the 3-year pause in renewals, many people on Medicaid have never been through the process or aren’t aware they may need to fill out long verification forms, as a recent KFF poll found. Some people moved and didn’t update their contact information.

And while agencies are required to assist enrollees who don’t speak English well, many are sending the forms in only a few common languages.

Tens of thousands of children are losing coverage, as researchers have warned, even though some may still qualify for Medicaid or CHIP. In its first month of reviews, South Dakota ended coverage for 10% of all Medicaid and CHIP enrollees in the state. More than half of them were children. In Arkansas, about 40% were kids.

Many parents don’t know that limits on household income are significantly higher for children than adults. Parents should fill out renewal forms even if they don’t qualify themselves, said Joan Alker, executive director of the Georgetown University Center for Children and Families, Washington.

New Hampshire has moved most families with children to the end of the review process. Mr. Lipman said his biggest worry is that a child will end up uninsured. Florida also planned to push kids with serious health conditions and other vulnerable groups to the end of the review line.

But according to Miriam Harmatz, advocacy director and founder of the Florida Health Justice Project, state officials sent cancellation letters to several clients with disabled children who probably still qualify. She’s helping those families appeal.

Nearly 250,000 Floridians reviewed in the first month of the unwinding lost coverage, 82% of them for reasons like incomplete paperwork, the state reported to federal authorities. House Democrats from the state petitioned Republican Gov. Ron DeSantis to pause the unwinding.

Advocacy coalitions in both Florida and Arkansas also have called for investigations into the review process and a pause on cancellations.

The state is contacting enrollees by phone, email, and text, and continues to process late applications, said Tori Cuddy, a spokesperson for the Florida Department of Children and Families. Ms. Cuddy did not respond to questions about issues raised in the petitions.

Federal officials are investigating those complaints and any other problems that emerge, said Dan Tsai, director of the Center for Medicaid & CHIP Services. “If we find that the rules are not being followed, we will take action.”

His agency has directed states to automatically reenroll residents using data from other government programs like unemployment and food assistance when possible. Anyone who can’t be approved through that process must act quickly.

“For the past 3 years, people have been told to ignore the mail around this, that the renewal was not going to lead to a termination.” Suddenly that mail matters, he said.

Federal law requires states to tell people why they’re losing Medicaid coverage and how to appeal the decision.

Ms. Harmatz said some cancellation notices in Florida are vague and could violate due process rules. Letters that she’s seen say “your Medicaid for this period is ending” rather than providing a specific reason for disenrollment, like having too high an income or incomplete paperwork.
If a person requests a hearing before their cancellation takes effect, they can stay covered during the appeals process. Even after being disenrolled, many still have a 90-day window to restore coverage.

In New Hampshire, 13% of people deemed ineligible in the first month have asked for extra time to provide the necessary records. “If you’re eligible for Medicaid, we don’t want you to lose it,” said Mr. Lipman.

Rep. Clere pushed Indiana’s Medicaid officials during the May meeting to immediately make changes to avoid people unnecessarily becoming uninsured. One official responded that they’ll learn and improve over time.

“I’m just concerned that we’re going to be ‘learning’ as a result of people losing coverage,” Rep. Clere replied. “So I don’t want to learn at their expense.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

More than 600,000 Americans have lost Medicaid coverage since pandemic protections ended on April 1. And a KFF Health News analysis of state data shows the vast majority were removed from state rolls for not completing paperwork.

Under normal circumstances, states review their Medicaid enrollment lists regularly to ensure every recipient qualifies for coverage. But because of a nationwide pause in those reviews during the pandemic, the health insurance program for low-income and disabled Americans kept people covered even if they no longer qualified.

Now, in what’s known as the Medicaid unwinding, states are combing through rolls and deciding who stays and who goes. People who are no longer eligible or don’t complete paperwork in time will be dropped.

The overwhelming majority of people who have lost coverage in most states were dropped because of technicalities, not because state officials determined they no longer meet Medicaid income limits. Four out of every five people dropped so far either never returned the paperwork or omitted required documents, according to a KFF Health News analysis of data from 11 states that provided details on recent cancellations. Now, lawmakers and advocates are expressing alarm over the volume of people losing coverage and, in some states, calling to pause the process.

KFF Health News sought data from the 19 states that started cancellations by May 1. Based on records from 14 states that provided detailed numbers, either in response to a public records request or by posting online, 36% of people whose eligibility was reviewed have been disenrolled.

In Indiana, 53,000 residents lost coverage in the first month of the unwinding, 89% for procedural reasons like not returning renewal forms. State Rep. Ed Clere, a Republican, expressed dismay at those “staggering numbers” in a May 24 Medicaid advisory group meeting, repeatedly questioning state officials about forms mailed to out-of-date addresses and urging them to give people more than 2 weeks’ notice before canceling their coverage.

Rep. Clere warned that the cancellations set in motion an avoidable revolving door. Some people dropped from Medicaid will have to forgo filling prescriptions and cancel doctor visits because they can’t afford care. Months down the line, after untreated chronic illnesses spiral out of control, they’ll end up in the emergency room where social workers will need to again help them join the program, he said.

Before the unwinding, more than one in four Americans – 93 million – were covered by Medicaid or CHIP, the Children’s Health Insurance Program, according to KFF Health News’ analysis of the latest enrollment data. Half of all kids are covered by the programs.

About 15 million people will be dropped over the next year as states review participants’ eligibility in monthly tranches.

Most people will find health coverage through new jobs or qualify for subsidized plans through the Affordable Care Act. But millions of others, including many children, will become uninsured and unable to afford basic prescriptions or preventive care. The uninsured rate among those under 65 is projected to rise from a historical low of 8.3% today to 9.3% next year, according to the Congressional Budget Office.

Because each state is handling the unwinding differently, the share of enrollees dropped in the first weeks varies widely.

Several states are first reviewing people officials believe are no longer eligible or who haven’t recently used their insurance. High cancellation rates in those states should level out as the agencies move on to people who likely still qualify.

In Utah, nearly 56% of people included in early reviews were dropped. In New Hampshire, 44% received cancellation letters within the first 2 months – almost all for procedural reasons, like not returning paperwork.

But New Hampshire officials found that thousands of people who didn’t fill out the forms indeed earn too much to qualify, according to Henry Lipman, the state’s Medicaid director. They would have been denied anyway. Even so, more people than he expected are not returning renewal forms. “That tells us that we need to change up our strategy,” said Mr. Lipman.

In other states, like Virginia and Nebraska, which aren’t prioritizing renewals by likely eligibility, about 90% have been renewed.

Because of the 3-year pause in renewals, many people on Medicaid have never been through the process or aren’t aware they may need to fill out long verification forms, as a recent KFF poll found. Some people moved and didn’t update their contact information.

And while agencies are required to assist enrollees who don’t speak English well, many are sending the forms in only a few common languages.

Tens of thousands of children are losing coverage, as researchers have warned, even though some may still qualify for Medicaid or CHIP. In its first month of reviews, South Dakota ended coverage for 10% of all Medicaid and CHIP enrollees in the state. More than half of them were children. In Arkansas, about 40% were kids.

Many parents don’t know that limits on household income are significantly higher for children than adults. Parents should fill out renewal forms even if they don’t qualify themselves, said Joan Alker, executive director of the Georgetown University Center for Children and Families, Washington.

New Hampshire has moved most families with children to the end of the review process. Mr. Lipman said his biggest worry is that a child will end up uninsured. Florida also planned to push kids with serious health conditions and other vulnerable groups to the end of the review line.

But according to Miriam Harmatz, advocacy director and founder of the Florida Health Justice Project, state officials sent cancellation letters to several clients with disabled children who probably still qualify. She’s helping those families appeal.

Nearly 250,000 Floridians reviewed in the first month of the unwinding lost coverage, 82% of them for reasons like incomplete paperwork, the state reported to federal authorities. House Democrats from the state petitioned Republican Gov. Ron DeSantis to pause the unwinding.

Advocacy coalitions in both Florida and Arkansas also have called for investigations into the review process and a pause on cancellations.

The state is contacting enrollees by phone, email, and text, and continues to process late applications, said Tori Cuddy, a spokesperson for the Florida Department of Children and Families. Ms. Cuddy did not respond to questions about issues raised in the petitions.

Federal officials are investigating those complaints and any other problems that emerge, said Dan Tsai, director of the Center for Medicaid & CHIP Services. “If we find that the rules are not being followed, we will take action.”

His agency has directed states to automatically reenroll residents using data from other government programs like unemployment and food assistance when possible. Anyone who can’t be approved through that process must act quickly.

“For the past 3 years, people have been told to ignore the mail around this, that the renewal was not going to lead to a termination.” Suddenly that mail matters, he said.

Federal law requires states to tell people why they’re losing Medicaid coverage and how to appeal the decision.

Ms. Harmatz said some cancellation notices in Florida are vague and could violate due process rules. Letters that she’s seen say “your Medicaid for this period is ending” rather than providing a specific reason for disenrollment, like having too high an income or incomplete paperwork.
If a person requests a hearing before their cancellation takes effect, they can stay covered during the appeals process. Even after being disenrolled, many still have a 90-day window to restore coverage.

In New Hampshire, 13% of people deemed ineligible in the first month have asked for extra time to provide the necessary records. “If you’re eligible for Medicaid, we don’t want you to lose it,” said Mr. Lipman.

Rep. Clere pushed Indiana’s Medicaid officials during the May meeting to immediately make changes to avoid people unnecessarily becoming uninsured. One official responded that they’ll learn and improve over time.

“I’m just concerned that we’re going to be ‘learning’ as a result of people losing coverage,” Rep. Clere replied. “So I don’t want to learn at their expense.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

More than 600,000 Americans have lost Medicaid coverage since pandemic protections ended on April 1. And a KFF Health News analysis of state data shows the vast majority were removed from state rolls for not completing paperwork.

Under normal circumstances, states review their Medicaid enrollment lists regularly to ensure every recipient qualifies for coverage. But because of a nationwide pause in those reviews during the pandemic, the health insurance program for low-income and disabled Americans kept people covered even if they no longer qualified.

Now, in what’s known as the Medicaid unwinding, states are combing through rolls and deciding who stays and who goes. People who are no longer eligible or don’t complete paperwork in time will be dropped.

The overwhelming majority of people who have lost coverage in most states were dropped because of technicalities, not because state officials determined they no longer meet Medicaid income limits. Four out of every five people dropped so far either never returned the paperwork or omitted required documents, according to a KFF Health News analysis of data from 11 states that provided details on recent cancellations. Now, lawmakers and advocates are expressing alarm over the volume of people losing coverage and, in some states, calling to pause the process.

KFF Health News sought data from the 19 states that started cancellations by May 1. Based on records from 14 states that provided detailed numbers, either in response to a public records request or by posting online, 36% of people whose eligibility was reviewed have been disenrolled.

In Indiana, 53,000 residents lost coverage in the first month of the unwinding, 89% for procedural reasons like not returning renewal forms. State Rep. Ed Clere, a Republican, expressed dismay at those “staggering numbers” in a May 24 Medicaid advisory group meeting, repeatedly questioning state officials about forms mailed to out-of-date addresses and urging them to give people more than 2 weeks’ notice before canceling their coverage.

Rep. Clere warned that the cancellations set in motion an avoidable revolving door. Some people dropped from Medicaid will have to forgo filling prescriptions and cancel doctor visits because they can’t afford care. Months down the line, after untreated chronic illnesses spiral out of control, they’ll end up in the emergency room where social workers will need to again help them join the program, he said.

Before the unwinding, more than one in four Americans – 93 million – were covered by Medicaid or CHIP, the Children’s Health Insurance Program, according to KFF Health News’ analysis of the latest enrollment data. Half of all kids are covered by the programs.

About 15 million people will be dropped over the next year as states review participants’ eligibility in monthly tranches.

Most people will find health coverage through new jobs or qualify for subsidized plans through the Affordable Care Act. But millions of others, including many children, will become uninsured and unable to afford basic prescriptions or preventive care. The uninsured rate among those under 65 is projected to rise from a historical low of 8.3% today to 9.3% next year, according to the Congressional Budget Office.

Because each state is handling the unwinding differently, the share of enrollees dropped in the first weeks varies widely.

Several states are first reviewing people officials believe are no longer eligible or who haven’t recently used their insurance. High cancellation rates in those states should level out as the agencies move on to people who likely still qualify.

In Utah, nearly 56% of people included in early reviews were dropped. In New Hampshire, 44% received cancellation letters within the first 2 months – almost all for procedural reasons, like not returning paperwork.

But New Hampshire officials found that thousands of people who didn’t fill out the forms indeed earn too much to qualify, according to Henry Lipman, the state’s Medicaid director. They would have been denied anyway. Even so, more people than he expected are not returning renewal forms. “That tells us that we need to change up our strategy,” said Mr. Lipman.

In other states, like Virginia and Nebraska, which aren’t prioritizing renewals by likely eligibility, about 90% have been renewed.

Because of the 3-year pause in renewals, many people on Medicaid have never been through the process or aren’t aware they may need to fill out long verification forms, as a recent KFF poll found. Some people moved and didn’t update their contact information.

And while agencies are required to assist enrollees who don’t speak English well, many are sending the forms in only a few common languages.

Tens of thousands of children are losing coverage, as researchers have warned, even though some may still qualify for Medicaid or CHIP. In its first month of reviews, South Dakota ended coverage for 10% of all Medicaid and CHIP enrollees in the state. More than half of them were children. In Arkansas, about 40% were kids.

Many parents don’t know that limits on household income are significantly higher for children than adults. Parents should fill out renewal forms even if they don’t qualify themselves, said Joan Alker, executive director of the Georgetown University Center for Children and Families, Washington.

New Hampshire has moved most families with children to the end of the review process. Mr. Lipman said his biggest worry is that a child will end up uninsured. Florida also planned to push kids with serious health conditions and other vulnerable groups to the end of the review line.

But according to Miriam Harmatz, advocacy director and founder of the Florida Health Justice Project, state officials sent cancellation letters to several clients with disabled children who probably still qualify. She’s helping those families appeal.

Nearly 250,000 Floridians reviewed in the first month of the unwinding lost coverage, 82% of them for reasons like incomplete paperwork, the state reported to federal authorities. House Democrats from the state petitioned Republican Gov. Ron DeSantis to pause the unwinding.

Advocacy coalitions in both Florida and Arkansas also have called for investigations into the review process and a pause on cancellations.

The state is contacting enrollees by phone, email, and text, and continues to process late applications, said Tori Cuddy, a spokesperson for the Florida Department of Children and Families. Ms. Cuddy did not respond to questions about issues raised in the petitions.

Federal officials are investigating those complaints and any other problems that emerge, said Dan Tsai, director of the Center for Medicaid & CHIP Services. “If we find that the rules are not being followed, we will take action.”

His agency has directed states to automatically reenroll residents using data from other government programs like unemployment and food assistance when possible. Anyone who can’t be approved through that process must act quickly.

“For the past 3 years, people have been told to ignore the mail around this, that the renewal was not going to lead to a termination.” Suddenly that mail matters, he said.

Federal law requires states to tell people why they’re losing Medicaid coverage and how to appeal the decision.

Ms. Harmatz said some cancellation notices in Florida are vague and could violate due process rules. Letters that she’s seen say “your Medicaid for this period is ending” rather than providing a specific reason for disenrollment, like having too high an income or incomplete paperwork.
If a person requests a hearing before their cancellation takes effect, they can stay covered during the appeals process. Even after being disenrolled, many still have a 90-day window to restore coverage.

In New Hampshire, 13% of people deemed ineligible in the first month have asked for extra time to provide the necessary records. “If you’re eligible for Medicaid, we don’t want you to lose it,” said Mr. Lipman.

Rep. Clere pushed Indiana’s Medicaid officials during the May meeting to immediately make changes to avoid people unnecessarily becoming uninsured. One official responded that they’ll learn and improve over time.

“I’m just concerned that we’re going to be ‘learning’ as a result of people losing coverage,” Rep. Clere replied. “So I don’t want to learn at their expense.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

The federal government is reconsidering a decision that breast cancer patients, plastic surgeons, and members of Congress have protested would limit women’s options for reconstructive surgery.

On June 1, the Centers for Medicare & Medicaid Services plans to reexamine how doctors are paid for a type of breast reconstruction known as DIEP flap, in which skin, fat, and blood vessels are harvested from a woman’s abdomen to create a new breast.

The procedure offers potential advantages over implants and operations that take muscle from the abdomen. But it’s also more expensive. If patients go outside an insurance network for the operation, it can cost more than $50,000. And, if insurers pay significantly less for the surgery as a result of the government’s decision, some in-network surgeons would stop offering it, a plastic surgeons group has argued.

The DIEP flap controversy, spotlighted by CBS News in January, illustrates arcane and indirect ways the federal government can influence which medical options are available – even to people with private insurance. Often, the answers come down to billing codes – which identify specific medical services on forms doctors submit for reimbursement – and the competing pleas of groups whose interests are riding on them.

Medical coding is the backbone for “how business gets done in medicine,” said Karen Joynt Maddox, MD, MPH, a physician at Washington University in St. Louis who researches health economics and policy.

CMS, the agency overseeing Medicare and Medicaid, maintains a list of codes representing thousands of medical services and products. It regularly evaluates whether to add codes or revise or remove existing ones. In 2022, it decided to eliminate a code that has enabled doctors to collect much more money for DIEP flap operations than for simpler types of breast reconstruction.

In 2006, CMS established an “S” code – S2068 – for what was then a relatively new procedure: breast reconstructions with deep inferior epigastric perforator flap (DIEP flap). S codes temporarily fill gaps in a parallel system of billing codes known as CPT codes, which are maintained by the American Medical Association.

Codes don’t dictate the amounts private insurers pay for medical services; those reimbursements are generally worked out between insurance companies and medical providers. However, using the narrowly targeted S code, doctors and hospitals have been able to distinguish DIEP flap surgeries, which require complex microsurgical skills, from other forms of breast reconstruction that take less time to perform and generally yield lower insurance reimbursements.

CMS announced in 2022 that it planned to eliminate the S code at the end of 2024 – a move some doctors say would slash the amount surgeons are paid. (To be precise, CMS announced it would eliminate a series of three S codes for similar procedures, but some of the more outspoken critics have focused on one of them, S2068.) The agency’s decision is already changing the landscape of reconstructive surgery and creating anxiety for breast cancer patients.

Kate Getz, a single mother in Morton, Ill., learned she had cancer in January at age 30. As she grappled with her diagnosis, it was overwhelming to think about what her body would look like over the long term. She pictured herself getting married one day and wondered “how on earth I would be able to wear a wedding dress with only having one breast left,” she said.

She thought a DIEP flap was her best option and worried about having to undergo repeated surgeries if she got implants instead. Implants generally need to be replaced every 10 years or so. But after she spent more than a month trying to get answers about how her DIEP flap surgery would be covered, Ms. Getz’s insurer, Cigna, informed her it would use a lower-paying CPT code to reimburse her physician, Ms. Getz said. As far as she could see, that would have made it impossible for Ms. Getz to obtain the surgery.

Paying out of pocket was “not even an option.”

“I’m a single mom. We get by, right? But I’m not, not wealthy by any means,” she said.

Cost is not necessarily the only hurdle patients seeking DIEP flaps must overcome. Citing the complexity of the procedure, Ms. Getz said, a local plastic surgeon told her it would be difficult for him to perform. She ended up traveling from Illinois to Texas for the surgery.

The government’s plan to eliminate the three S codes was driven by the Blue Cross Blue Shield Association, a major lobbying organization for health insurance companies. In 2021, the group asked CMS to discontinue the codes, arguing that they were no longer needed because the AMA had updated a CPT code to explicitly include DIEP flap surgery and the related operations, according to a CMS document.

For years, the AMA advised doctors that the CPT code was appropriate for DIEP flap procedures. But after the government’s decision, at least two major insurance companies told doctors they would no longer reimburse them under the higher-paying codes, prompting a backlash.

Physicians and advocacy groups for breast cancer patients, such as the nonprofit organization Susan G. Komen, have argued that many plastic surgeons would stop providing DIEP flap procedures for women with private insurance because they wouldn’t get paid enough.

Lawmakers from both parties have asked the agency to keep the S code, including Rep. Debbie Wasserman Schultz (D-Fla.) and Sen. Amy Klobuchar (D-Minn.), who have had breast cancer, and Sen. Marsha Blackburn (R-Tenn.).

CMS at its June 1 meeting will consider whether to keep the three S codes or delay their expiration.

In a May 30 statement, Blue Cross Blue Shield Association spokesperson Kelly Parsons reiterated the organization’s view that “there is no longer a need to keep the S codes.”

In a profit-driven health care system, there’s a tug of war over reimbursements between providers and insurance companies, often at the expense of patients, said Dr. Joynt Maddox.

“We’re in this sort of constant battle” between hospital chains and insurance companies “about who’s going to wield more power at the bargaining table,” Dr. Joynt Maddox said. “And the clinical piece of that often gets lost, because it’s not often the clinical benefit and the clinical priority and the patient centeredness that’s at the middle of these conversations.”

Elisabeth Potter, MD, a plastic surgeon who specializes in DIEP flap surgeries, decided to perform Ms. Getz’s surgery at whatever price Cigna would pay.

According to Fair Health, a nonprofit that provides information on health care costs, in Austin, Tex. – where Dr. Potter is based – an insurer might pay an in-network doctor $9,323 for the surgery when it’s billed using the CPT code and $18,037 under the S code. Those amounts are not averages; rather, Fair Health estimated that 80% of payment rates are lower than or equal to those amounts.

Dr. Potter said her Cigna reimbursement “is significantly lower.”

Weeks before her May surgery, Ms. Getz received big news – Cigna had reversed itself and would cover her surgery under the S code. It “felt like a real victory,” she said.

But she still fears for other patients.

“I’m still asking these companies to do right by women,” Ms. Getz said. “I’m still asking them to provide the procedures we need to reimburse them at rates where women have access to them regardless of their wealth.”

In a statement, Cigna spokesperson Justine Sessions said the insurer remains “committed to ensuring that our customers have affordable coverage and access to the full range of breast reconstruction procedures and to quality surgeons who perform these complex surgeries.”

Medical costs that health insurers cover generally are passed along to consumers in the form of premiums, deductibles, and other out-of-pocket expenses.

For any type of breast reconstruction, there are benefits, risks, and trade-offs. A 2018 paper published in JAMA Surgery found that women who underwent DIEP flap surgery had higher odds of developing “reoperative complications” within 2 years than those who received artificial implants. However, DIEP flaps had lower odds of infection than implants.

Implants carry risks of additional surgery, pain, rupture, and even an uncommon type of immune system cancer.

Other flap procedures that take muscle from the abdomen can leave women with weakened abdominal walls and increase their risk of developing a hernia.

Academic research shows that insurance reimbursement affects which women can access DIEP flap breast reconstruction, creating a two-tiered system for private health insurance versus government programs like Medicare and Medicaid. Private insurance generally pays physicians more than government coverage, and Medicare doesn’t use S codes.

Lynn Damitz, a physician and board vice president of health policy and advocacy for the American Society of Plastic Surgeons, said the group supports continuing the S code temporarily or indefinitely. If reimbursements drop, some doctors won’t perform DIEP flaps anymore.

A study published in February found that, of patients who used their own tissue for breast reconstruction, privately insured patients were more likely than publicly insured patients to receive DIEP flap reconstruction.

To Dr. Potter, that shows what will happen if private insurance payments plummet. “If you’re a Medicare provider and you’re not paid to do DIEP flaps, you never tell a patient that it’s an option. You won’t perform it,” Dr. Potter said. “If you take private insurance and all of a sudden your reimbursement rate is cut from $15,000 down to $3,500, you’re not going to do that surgery. And I’m not saying that that’s the right thing to do, but that’s what happens.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

The federal government is reconsidering a decision that breast cancer patients, plastic surgeons, and members of Congress have protested would limit women’s options for reconstructive surgery.

On June 1, the Centers for Medicare & Medicaid Services plans to reexamine how doctors are paid for a type of breast reconstruction known as DIEP flap, in which skin, fat, and blood vessels are harvested from a woman’s abdomen to create a new breast.

The procedure offers potential advantages over implants and operations that take muscle from the abdomen. But it’s also more expensive. If patients go outside an insurance network for the operation, it can cost more than $50,000. And, if insurers pay significantly less for the surgery as a result of the government’s decision, some in-network surgeons would stop offering it, a plastic surgeons group has argued.

The DIEP flap controversy, spotlighted by CBS News in January, illustrates arcane and indirect ways the federal government can influence which medical options are available – even to people with private insurance. Often, the answers come down to billing codes – which identify specific medical services on forms doctors submit for reimbursement – and the competing pleas of groups whose interests are riding on them.

Medical coding is the backbone for “how business gets done in medicine,” said Karen Joynt Maddox, MD, MPH, a physician at Washington University in St. Louis who researches health economics and policy.

CMS, the agency overseeing Medicare and Medicaid, maintains a list of codes representing thousands of medical services and products. It regularly evaluates whether to add codes or revise or remove existing ones. In 2022, it decided to eliminate a code that has enabled doctors to collect much more money for DIEP flap operations than for simpler types of breast reconstruction.

In 2006, CMS established an “S” code – S2068 – for what was then a relatively new procedure: breast reconstructions with deep inferior epigastric perforator flap (DIEP flap). S codes temporarily fill gaps in a parallel system of billing codes known as CPT codes, which are maintained by the American Medical Association.

Codes don’t dictate the amounts private insurers pay for medical services; those reimbursements are generally worked out between insurance companies and medical providers. However, using the narrowly targeted S code, doctors and hospitals have been able to distinguish DIEP flap surgeries, which require complex microsurgical skills, from other forms of breast reconstruction that take less time to perform and generally yield lower insurance reimbursements.

CMS announced in 2022 that it planned to eliminate the S code at the end of 2024 – a move some doctors say would slash the amount surgeons are paid. (To be precise, CMS announced it would eliminate a series of three S codes for similar procedures, but some of the more outspoken critics have focused on one of them, S2068.) The agency’s decision is already changing the landscape of reconstructive surgery and creating anxiety for breast cancer patients.

Kate Getz, a single mother in Morton, Ill., learned she had cancer in January at age 30. As she grappled with her diagnosis, it was overwhelming to think about what her body would look like over the long term. She pictured herself getting married one day and wondered “how on earth I would be able to wear a wedding dress with only having one breast left,” she said.

She thought a DIEP flap was her best option and worried about having to undergo repeated surgeries if she got implants instead. Implants generally need to be replaced every 10 years or so. But after she spent more than a month trying to get answers about how her DIEP flap surgery would be covered, Ms. Getz’s insurer, Cigna, informed her it would use a lower-paying CPT code to reimburse her physician, Ms. Getz said. As far as she could see, that would have made it impossible for Ms. Getz to obtain the surgery.

Paying out of pocket was “not even an option.”

“I’m a single mom. We get by, right? But I’m not, not wealthy by any means,” she said.

Cost is not necessarily the only hurdle patients seeking DIEP flaps must overcome. Citing the complexity of the procedure, Ms. Getz said, a local plastic surgeon told her it would be difficult for him to perform. She ended up traveling from Illinois to Texas for the surgery.

The government’s plan to eliminate the three S codes was driven by the Blue Cross Blue Shield Association, a major lobbying organization for health insurance companies. In 2021, the group asked CMS to discontinue the codes, arguing that they were no longer needed because the AMA had updated a CPT code to explicitly include DIEP flap surgery and the related operations, according to a CMS document.

For years, the AMA advised doctors that the CPT code was appropriate for DIEP flap procedures. But after the government’s decision, at least two major insurance companies told doctors they would no longer reimburse them under the higher-paying codes, prompting a backlash.

Physicians and advocacy groups for breast cancer patients, such as the nonprofit organization Susan G. Komen, have argued that many plastic surgeons would stop providing DIEP flap procedures for women with private insurance because they wouldn’t get paid enough.

Lawmakers from both parties have asked the agency to keep the S code, including Rep. Debbie Wasserman Schultz (D-Fla.) and Sen. Amy Klobuchar (D-Minn.), who have had breast cancer, and Sen. Marsha Blackburn (R-Tenn.).

CMS at its June 1 meeting will consider whether to keep the three S codes or delay their expiration.

In a May 30 statement, Blue Cross Blue Shield Association spokesperson Kelly Parsons reiterated the organization’s view that “there is no longer a need to keep the S codes.”

In a profit-driven health care system, there’s a tug of war over reimbursements between providers and insurance companies, often at the expense of patients, said Dr. Joynt Maddox.

“We’re in this sort of constant battle” between hospital chains and insurance companies “about who’s going to wield more power at the bargaining table,” Dr. Joynt Maddox said. “And the clinical piece of that often gets lost, because it’s not often the clinical benefit and the clinical priority and the patient centeredness that’s at the middle of these conversations.”

Elisabeth Potter, MD, a plastic surgeon who specializes in DIEP flap surgeries, decided to perform Ms. Getz’s surgery at whatever price Cigna would pay.

According to Fair Health, a nonprofit that provides information on health care costs, in Austin, Tex. – where Dr. Potter is based – an insurer might pay an in-network doctor $9,323 for the surgery when it’s billed using the CPT code and $18,037 under the S code. Those amounts are not averages; rather, Fair Health estimated that 80% of payment rates are lower than or equal to those amounts.

Dr. Potter said her Cigna reimbursement “is significantly lower.”

Weeks before her May surgery, Ms. Getz received big news – Cigna had reversed itself and would cover her surgery under the S code. It “felt like a real victory,” she said.

But she still fears for other patients.

“I’m still asking these companies to do right by women,” Ms. Getz said. “I’m still asking them to provide the procedures we need to reimburse them at rates where women have access to them regardless of their wealth.”

In a statement, Cigna spokesperson Justine Sessions said the insurer remains “committed to ensuring that our customers have affordable coverage and access to the full range of breast reconstruction procedures and to quality surgeons who perform these complex surgeries.”

Medical costs that health insurers cover generally are passed along to consumers in the form of premiums, deductibles, and other out-of-pocket expenses.

For any type of breast reconstruction, there are benefits, risks, and trade-offs. A 2018 paper published in JAMA Surgery found that women who underwent DIEP flap surgery had higher odds of developing “reoperative complications” within 2 years than those who received artificial implants. However, DIEP flaps had lower odds of infection than implants.

Implants carry risks of additional surgery, pain, rupture, and even an uncommon type of immune system cancer.

Other flap procedures that take muscle from the abdomen can leave women with weakened abdominal walls and increase their risk of developing a hernia.

Academic research shows that insurance reimbursement affects which women can access DIEP flap breast reconstruction, creating a two-tiered system for private health insurance versus government programs like Medicare and Medicaid. Private insurance generally pays physicians more than government coverage, and Medicare doesn’t use S codes.

Lynn Damitz, a physician and board vice president of health policy and advocacy for the American Society of Plastic Surgeons, said the group supports continuing the S code temporarily or indefinitely. If reimbursements drop, some doctors won’t perform DIEP flaps anymore.

A study published in February found that, of patients who used their own tissue for breast reconstruction, privately insured patients were more likely than publicly insured patients to receive DIEP flap reconstruction.

To Dr. Potter, that shows what will happen if private insurance payments plummet. “If you’re a Medicare provider and you’re not paid to do DIEP flaps, you never tell a patient that it’s an option. You won’t perform it,” Dr. Potter said. “If you take private insurance and all of a sudden your reimbursement rate is cut from $15,000 down to $3,500, you’re not going to do that surgery. And I’m not saying that that’s the right thing to do, but that’s what happens.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

The federal government is reconsidering a decision that breast cancer patients, plastic surgeons, and members of Congress have protested would limit women’s options for reconstructive surgery.

On June 1, the Centers for Medicare & Medicaid Services plans to reexamine how doctors are paid for a type of breast reconstruction known as DIEP flap, in which skin, fat, and blood vessels are harvested from a woman’s abdomen to create a new breast.

The procedure offers potential advantages over implants and operations that take muscle from the abdomen. But it’s also more expensive. If patients go outside an insurance network for the operation, it can cost more than $50,000. And, if insurers pay significantly less for the surgery as a result of the government’s decision, some in-network surgeons would stop offering it, a plastic surgeons group has argued.

The DIEP flap controversy, spotlighted by CBS News in January, illustrates arcane and indirect ways the federal government can influence which medical options are available – even to people with private insurance. Often, the answers come down to billing codes – which identify specific medical services on forms doctors submit for reimbursement – and the competing pleas of groups whose interests are riding on them.

Medical coding is the backbone for “how business gets done in medicine,” said Karen Joynt Maddox, MD, MPH, a physician at Washington University in St. Louis who researches health economics and policy.

CMS, the agency overseeing Medicare and Medicaid, maintains a list of codes representing thousands of medical services and products. It regularly evaluates whether to add codes or revise or remove existing ones. In 2022, it decided to eliminate a code that has enabled doctors to collect much more money for DIEP flap operations than for simpler types of breast reconstruction.

In 2006, CMS established an “S” code – S2068 – for what was then a relatively new procedure: breast reconstructions with deep inferior epigastric perforator flap (DIEP flap). S codes temporarily fill gaps in a parallel system of billing codes known as CPT codes, which are maintained by the American Medical Association.

Codes don’t dictate the amounts private insurers pay for medical services; those reimbursements are generally worked out between insurance companies and medical providers. However, using the narrowly targeted S code, doctors and hospitals have been able to distinguish DIEP flap surgeries, which require complex microsurgical skills, from other forms of breast reconstruction that take less time to perform and generally yield lower insurance reimbursements.

CMS announced in 2022 that it planned to eliminate the S code at the end of 2024 – a move some doctors say would slash the amount surgeons are paid. (To be precise, CMS announced it would eliminate a series of three S codes for similar procedures, but some of the more outspoken critics have focused on one of them, S2068.) The agency’s decision is already changing the landscape of reconstructive surgery and creating anxiety for breast cancer patients.

Kate Getz, a single mother in Morton, Ill., learned she had cancer in January at age 30. As she grappled with her diagnosis, it was overwhelming to think about what her body would look like over the long term. She pictured herself getting married one day and wondered “how on earth I would be able to wear a wedding dress with only having one breast left,” she said.

She thought a DIEP flap was her best option and worried about having to undergo repeated surgeries if she got implants instead. Implants generally need to be replaced every 10 years or so. But after she spent more than a month trying to get answers about how her DIEP flap surgery would be covered, Ms. Getz’s insurer, Cigna, informed her it would use a lower-paying CPT code to reimburse her physician, Ms. Getz said. As far as she could see, that would have made it impossible for Ms. Getz to obtain the surgery.

Paying out of pocket was “not even an option.”

“I’m a single mom. We get by, right? But I’m not, not wealthy by any means,” she said.

Cost is not necessarily the only hurdle patients seeking DIEP flaps must overcome. Citing the complexity of the procedure, Ms. Getz said, a local plastic surgeon told her it would be difficult for him to perform. She ended up traveling from Illinois to Texas for the surgery.

The government’s plan to eliminate the three S codes was driven by the Blue Cross Blue Shield Association, a major lobbying organization for health insurance companies. In 2021, the group asked CMS to discontinue the codes, arguing that they were no longer needed because the AMA had updated a CPT code to explicitly include DIEP flap surgery and the related operations, according to a CMS document.

For years, the AMA advised doctors that the CPT code was appropriate for DIEP flap procedures. But after the government’s decision, at least two major insurance companies told doctors they would no longer reimburse them under the higher-paying codes, prompting a backlash.

Physicians and advocacy groups for breast cancer patients, such as the nonprofit organization Susan G. Komen, have argued that many plastic surgeons would stop providing DIEP flap procedures for women with private insurance because they wouldn’t get paid enough.

Lawmakers from both parties have asked the agency to keep the S code, including Rep. Debbie Wasserman Schultz (D-Fla.) and Sen. Amy Klobuchar (D-Minn.), who have had breast cancer, and Sen. Marsha Blackburn (R-Tenn.).

CMS at its June 1 meeting will consider whether to keep the three S codes or delay their expiration.

In a May 30 statement, Blue Cross Blue Shield Association spokesperson Kelly Parsons reiterated the organization’s view that “there is no longer a need to keep the S codes.”

In a profit-driven health care system, there’s a tug of war over reimbursements between providers and insurance companies, often at the expense of patients, said Dr. Joynt Maddox.

“We’re in this sort of constant battle” between hospital chains and insurance companies “about who’s going to wield more power at the bargaining table,” Dr. Joynt Maddox said. “And the clinical piece of that often gets lost, because it’s not often the clinical benefit and the clinical priority and the patient centeredness that’s at the middle of these conversations.”

Elisabeth Potter, MD, a plastic surgeon who specializes in DIEP flap surgeries, decided to perform Ms. Getz’s surgery at whatever price Cigna would pay.

According to Fair Health, a nonprofit that provides information on health care costs, in Austin, Tex. – where Dr. Potter is based – an insurer might pay an in-network doctor $9,323 for the surgery when it’s billed using the CPT code and $18,037 under the S code. Those amounts are not averages; rather, Fair Health estimated that 80% of payment rates are lower than or equal to those amounts.

Dr. Potter said her Cigna reimbursement “is significantly lower.”

Weeks before her May surgery, Ms. Getz received big news – Cigna had reversed itself and would cover her surgery under the S code. It “felt like a real victory,” she said.

But she still fears for other patients.

“I’m still asking these companies to do right by women,” Ms. Getz said. “I’m still asking them to provide the procedures we need to reimburse them at rates where women have access to them regardless of their wealth.”

In a statement, Cigna spokesperson Justine Sessions said the insurer remains “committed to ensuring that our customers have affordable coverage and access to the full range of breast reconstruction procedures and to quality surgeons who perform these complex surgeries.”

Medical costs that health insurers cover generally are passed along to consumers in the form of premiums, deductibles, and other out-of-pocket expenses.

For any type of breast reconstruction, there are benefits, risks, and trade-offs. A 2018 paper published in JAMA Surgery found that women who underwent DIEP flap surgery had higher odds of developing “reoperative complications” within 2 years than those who received artificial implants. However, DIEP flaps had lower odds of infection than implants.

Implants carry risks of additional surgery, pain, rupture, and even an uncommon type of immune system cancer.

Other flap procedures that take muscle from the abdomen can leave women with weakened abdominal walls and increase their risk of developing a hernia.

Academic research shows that insurance reimbursement affects which women can access DIEP flap breast reconstruction, creating a two-tiered system for private health insurance versus government programs like Medicare and Medicaid. Private insurance generally pays physicians more than government coverage, and Medicare doesn’t use S codes.

Lynn Damitz, a physician and board vice president of health policy and advocacy for the American Society of Plastic Surgeons, said the group supports continuing the S code temporarily or indefinitely. If reimbursements drop, some doctors won’t perform DIEP flaps anymore.

A study published in February found that, of patients who used their own tissue for breast reconstruction, privately insured patients were more likely than publicly insured patients to receive DIEP flap reconstruction.

To Dr. Potter, that shows what will happen if private insurance payments plummet. “If you’re a Medicare provider and you’re not paid to do DIEP flaps, you never tell a patient that it’s an option. You won’t perform it,” Dr. Potter said. “If you take private insurance and all of a sudden your reimbursement rate is cut from $15,000 down to $3,500, you’re not going to do that surgery. And I’m not saying that that’s the right thing to do, but that’s what happens.”

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation.

Amid growing criticism of health insurers’ onerous prior authorization practices, lawmakers in 30 states have introduced bills this year that aim to rein in insurer gatekeeping and improve patient care.

“This is something that goes on in every doctor’s office every day; the frustrations, the delays, and the use of office staff time are just unbelievable,” said Steven Orland, MD, a board-certified urologist and president of the Medical Society of New Jersey.

The bills, which cover private health plans and insurers that states regulate, may provide some relief for physicians as federal efforts to streamline prior authorization for some Medicare patients have lagged.

Last year, Congress failed to pass the Improving Seniors’ Timely Access to Care Act of 2021, despite 326 co-sponsors. The bill would have compelled insurers covering Medicare Advantage enrollees to speed up prior authorizations, make the process more transparent, and remove obstacles such as requiring fax machine submissions.

Last month, however, the Centers for Medicare & Medicaid Services issued a final rule that will improve some aspects of prior authorizations in Medicare Advantage insurance plans and ensure that enrollees have the same access to necessary care as traditional Medicare enrollees.

The insurance industry has long defended prior authorization requirements and opposed legislation that would limit them.

America’s Health Insurance Plans (AHIP) and the Blue Cross Blue Shield Association said in a 2019 letter to a congressional committee when the federal legislation was first introduced, “Prior authorizations enforce best practices and guidelines for care management and help physicians identify and avoid care techniques that would harm patient outcomes, such as designating prescriptions that could feed into an opioid addiction.” AHIP didn’t respond to repeated requests for comment.

But some major insurers now appear willing to compromise and voluntarily reduce the volume of prior authorizations they require. Days before the federal final rule was released, three major insurers – United HealthCare, Cigna, and Aetna CVS Health – announced they plan to drop some prior authorization requirements and automate processes.

United HealthCare said it will eliminate almost 20% of its prior authorizations for some nonurgent surgeries and procedures starting this summer. It also will create a national Gold Card program in 2024 for physicians who meet its eligibility requirements, which would eliminate prior authorization requirements for most procedures. Both initiatives will apply to commercial, Medicare Advantage, and Medicaid businesses, said the insurer in a statement.

However, United HealthCare also announced that in June it will start requiring prior authorization for diagnostic (not screening) gastrointestinal endoscopies for its nearly 27 million privately insured patients, citing data it says shows potentially harmful overuse of scopes. Physician groups have publicly criticized the move, saying it could delay lifesaving treatment, and have asked the insurer to reconsider.

Cigna and Aetna also have moved to pare back prior authorization processes. Scott Josephs, national medical officer for Cigna, told Healthcare Dive that Cigna has removed prior authorization reviews from nearly 500 services since 2020.

An Aetna spokesperson told Healthcare Dive that the CVS-owned payer has implemented a gold card program and rolled back prior authorization requirements on cataract surgeries, video EEGs, and home infusion for some drugs, according to Healthcare Dive.

Cigna has faced increased scrutiny from some state regulators since a ProPublica/The Capitol Forum article revealed in March that its doctors were denying claims without opening patients’ files, contrary to what insurance laws and regulations require in many states.

Over a period of 2 months last year, Cigna doctors denied over 300,000 requests for payments using this method, spending an average of 1.2 seconds on each case, the investigation found. In a written response, Cigna said the reporting by ProPublica and The Capitol Forum was “biased and incomplete.”
 

States aim to reduce prior authorization volume

The American Medical Association said it has been tracking nearly 90 prior authorization reform bills in 30 states. More than a dozen bills are still being considered in this legislative session, including in Arkansas, California, New Jersey, North Carolina, Maryland, and Washington, D.C.

“The groundswell of activity in the states reflects how big a problem this is,” said an AMA legislative expert. “The issue used to be ‘how can we automate and streamline processes’; now the issue is focused on reducing the volume of prior authorizations and the harm that can cause patients.”

The state bills use different strategies to reduce excessive prior authorization requirements. Maryland’s proposed bill, for example, would require just one prior authorization to stay on a prescription drug, if the insurer has previously approved the drug and the patient continues to successfully be treated by the drug.

Washington, D.C. and New Jersey have introduced comprehensive reform bills that include a “grace period” of 60 days, to ensure continuity of care when a patient switches health plans. They also would eliminate repeat authorizations for chronic and long-term conditions, set explicit timelines for insurers to respond to prior authorization requests and appeals, and require that practicing physicians review denials that are appealed.

Many state bills also would require insurers to be more transparent by posting information on their websites about which services and drugs require prior authorization and what their approval rates are for them, said AMA’s legislative expert.

“There’s a black hole of information that insurers have access to. We would really like to know how many prior authorization requests are denied, the time it takes to deny them, and the reasons for denial,” said Josh Bengal, JD, the director of government relations for the Medical Society of New Jersey.

The legislation in New Jersey and other states faces stiff opposition from the insurance lobby, especially state associations of health plans affiliated with AHIP. The California Association of Health Plans, for example, opposes a “gold card” bill (SB 598), introduced in February, that would allow a select group of high-performing doctors to skip prior authorizations for 1 year.

The CAHP states, “Californians deserve safe, high quality, high-value health care. Yet SB 598 will derail the progress we have made in our health care system by lowering the value and safety that Californians should expect from their health care providers,” according to a fact sheet.

The fact-sheet defines “low-value care” as medical services for which there is little to no benefit and poses potential physical or financial harm to patients, such as unnecessary CT scans or MRIs for uncomplicated conditions.

California is one of about a dozen states that have introduced gold card legislation this year. If enacted, they would join five states with gold card laws: West Virginia, Texas, Vermont, Michigan, and Louisiana.
 

How do gold cards work?

Physicians who achieve a high approval rate of prior authorizations from insurers for 1 year are eligible to be exempted from obtaining prior authorizations the following year.

The approval rate is at least 90% for a certain number of eligible health services, but the number of prior authorizations required to qualify can range from 5 to 30, depending on the state law.

Gold card legislation typically also gives the treating physician the right to have an appeal of a prior authorization denial by a physician peer of the same or similar specialty.

California’s bill would also apply to all covered health services, which is broader than what United HealthCare has proposed for its gold card exemption. The bill would also require a plan or insurer to annually monitor rates of prior authorization approval, modification, appeal, and denial, and to discontinue services, items, and supplies that are approved 95% of the time.

“These are important reforms that will help ensure that patients can receive the care they need, when they need it,” said CMA president Donaldo Hernandez, MD.

However, it’s not clear how many physicians will meet “gold card” status based on Texas’ recent experience with its own “gold card” law.

The Texas Department of Insurance estimated that only 3.3% of licensed physicians in the state have met “gold card” status since the bill became law in 2021, said Zeke Silva, MD, an interventional radiologist who serves on the Council of Legislation for the Texas Medical Association.

He noted that the legislation has had a limited effect for several reasons. Commercial health plans only make up only about 20% of all health plans in Texas. Also, the final regulations didn’t go into effect until last May and physicians are evaluated by health plans for “gold card” status every 6 months, said Dr. Silva.

In addition, physicians must have at least five prior authorizations approved for the same health service, which the law left up to the health plans to define, said Dr. Silva.

Now, the Texas Medical Association is lobbying for legislative improvements. “We want to reduce the number of eligible services that health plans require for prior authorizations and have more oversight of prior authorization denials by the Texas Department of Insurance and the Texas Medical Board,” said Dr. Silva.

He’s optimistic that if the bill becomes law, the number of physicians eligible for gold cards may increase.

Meanwhile, the AMA’s legislative expert, who declined to be identified because of organization policy, acknowledged the possibility that some prior authorization bills will die in state legislatures this year.

“We remain hopeful, but it’s an uphill battle. The state medical associations face a lot of opposition from health plans who don’t want to see these reforms become law.”

A version of this article originally appeared on Medscape.com.

Amid growing criticism of health insurers’ onerous prior authorization practices, lawmakers in 30 states have introduced bills this year that aim to rein in insurer gatekeeping and improve patient care.

“This is something that goes on in every doctor’s office every day; the frustrations, the delays, and the use of office staff time are just unbelievable,” said Steven Orland, MD, a board-certified urologist and president of the Medical Society of New Jersey.

The bills, which cover private health plans and insurers that states regulate, may provide some relief for physicians as federal efforts to streamline prior authorization for some Medicare patients have lagged.

Last year, Congress failed to pass the Improving Seniors’ Timely Access to Care Act of 2021, despite 326 co-sponsors. The bill would have compelled insurers covering Medicare Advantage enrollees to speed up prior authorizations, make the process more transparent, and remove obstacles such as requiring fax machine submissions.

Last month, however, the Centers for Medicare & Medicaid Services issued a final rule that will improve some aspects of prior authorizations in Medicare Advantage insurance plans and ensure that enrollees have the same access to necessary care as traditional Medicare enrollees.

The insurance industry has long defended prior authorization requirements and opposed legislation that would limit them.

America’s Health Insurance Plans (AHIP) and the Blue Cross Blue Shield Association said in a 2019 letter to a congressional committee when the federal legislation was first introduced, “Prior authorizations enforce best practices and guidelines for care management and help physicians identify and avoid care techniques that would harm patient outcomes, such as designating prescriptions that could feed into an opioid addiction.” AHIP didn’t respond to repeated requests for comment.

But some major insurers now appear willing to compromise and voluntarily reduce the volume of prior authorizations they require. Days before the federal final rule was released, three major insurers – United HealthCare, Cigna, and Aetna CVS Health – announced they plan to drop some prior authorization requirements and automate processes.

United HealthCare said it will eliminate almost 20% of its prior authorizations for some nonurgent surgeries and procedures starting this summer. It also will create a national Gold Card program in 2024 for physicians who meet its eligibility requirements, which would eliminate prior authorization requirements for most procedures. Both initiatives will apply to commercial, Medicare Advantage, and Medicaid businesses, said the insurer in a statement.

However, United HealthCare also announced that in June it will start requiring prior authorization for diagnostic (not screening) gastrointestinal endoscopies for its nearly 27 million privately insured patients, citing data it says shows potentially harmful overuse of scopes. Physician groups have publicly criticized the move, saying it could delay lifesaving treatment, and have asked the insurer to reconsider.

Cigna and Aetna also have moved to pare back prior authorization processes. Scott Josephs, national medical officer for Cigna, told Healthcare Dive that Cigna has removed prior authorization reviews from nearly 500 services since 2020.

An Aetna spokesperson told Healthcare Dive that the CVS-owned payer has implemented a gold card program and rolled back prior authorization requirements on cataract surgeries, video EEGs, and home infusion for some drugs, according to Healthcare Dive.

Cigna has faced increased scrutiny from some state regulators since a ProPublica/The Capitol Forum article revealed in March that its doctors were denying claims without opening patients’ files, contrary to what insurance laws and regulations require in many states.

Over a period of 2 months last year, Cigna doctors denied over 300,000 requests for payments using this method, spending an average of 1.2 seconds on each case, the investigation found. In a written response, Cigna said the reporting by ProPublica and The Capitol Forum was “biased and incomplete.”
 

States aim to reduce prior authorization volume

The American Medical Association said it has been tracking nearly 90 prior authorization reform bills in 30 states. More than a dozen bills are still being considered in this legislative session, including in Arkansas, California, New Jersey, North Carolina, Maryland, and Washington, D.C.

“The groundswell of activity in the states reflects how big a problem this is,” said an AMA legislative expert. “The issue used to be ‘how can we automate and streamline processes’; now the issue is focused on reducing the volume of prior authorizations and the harm that can cause patients.”

The state bills use different strategies to reduce excessive prior authorization requirements. Maryland’s proposed bill, for example, would require just one prior authorization to stay on a prescription drug, if the insurer has previously approved the drug and the patient continues to successfully be treated by the drug.

Washington, D.C. and New Jersey have introduced comprehensive reform bills that include a “grace period” of 60 days, to ensure continuity of care when a patient switches health plans. They also would eliminate repeat authorizations for chronic and long-term conditions, set explicit timelines for insurers to respond to prior authorization requests and appeals, and require that practicing physicians review denials that are appealed.

Many state bills also would require insurers to be more transparent by posting information on their websites about which services and drugs require prior authorization and what their approval rates are for them, said AMA’s legislative expert.

“There’s a black hole of information that insurers have access to. We would really like to know how many prior authorization requests are denied, the time it takes to deny them, and the reasons for denial,” said Josh Bengal, JD, the director of government relations for the Medical Society of New Jersey.

The legislation in New Jersey and other states faces stiff opposition from the insurance lobby, especially state associations of health plans affiliated with AHIP. The California Association of Health Plans, for example, opposes a “gold card” bill (SB 598), introduced in February, that would allow a select group of high-performing doctors to skip prior authorizations for 1 year.

The CAHP states, “Californians deserve safe, high quality, high-value health care. Yet SB 598 will derail the progress we have made in our health care system by lowering the value and safety that Californians should expect from their health care providers,” according to a fact sheet.

The fact-sheet defines “low-value care” as medical services for which there is little to no benefit and poses potential physical or financial harm to patients, such as unnecessary CT scans or MRIs for uncomplicated conditions.

California is one of about a dozen states that have introduced gold card legislation this year. If enacted, they would join five states with gold card laws: West Virginia, Texas, Vermont, Michigan, and Louisiana.
 

How do gold cards work?

Physicians who achieve a high approval rate of prior authorizations from insurers for 1 year are eligible to be exempted from obtaining prior authorizations the following year.

The approval rate is at least 90% for a certain number of eligible health services, but the number of prior authorizations required to qualify can range from 5 to 30, depending on the state law.

Gold card legislation typically also gives the treating physician the right to have an appeal of a prior authorization denial by a physician peer of the same or similar specialty.

California’s bill would also apply to all covered health services, which is broader than what United HealthCare has proposed for its gold card exemption. The bill would also require a plan or insurer to annually monitor rates of prior authorization approval, modification, appeal, and denial, and to discontinue services, items, and supplies that are approved 95% of the time.

“These are important reforms that will help ensure that patients can receive the care they need, when they need it,” said CMA president Donaldo Hernandez, MD.

However, it’s not clear how many physicians will meet “gold card” status based on Texas’ recent experience with its own “gold card” law.

The Texas Department of Insurance estimated that only 3.3% of licensed physicians in the state have met “gold card” status since the bill became law in 2021, said Zeke Silva, MD, an interventional radiologist who serves on the Council of Legislation for the Texas Medical Association.

He noted that the legislation has had a limited effect for several reasons. Commercial health plans only make up only about 20% of all health plans in Texas. Also, the final regulations didn’t go into effect until last May and physicians are evaluated by health plans for “gold card” status every 6 months, said Dr. Silva.

In addition, physicians must have at least five prior authorizations approved for the same health service, which the law left up to the health plans to define, said Dr. Silva.

Now, the Texas Medical Association is lobbying for legislative improvements. “We want to reduce the number of eligible services that health plans require for prior authorizations and have more oversight of prior authorization denials by the Texas Department of Insurance and the Texas Medical Board,” said Dr. Silva.

He’s optimistic that if the bill becomes law, the number of physicians eligible for gold cards may increase.

Meanwhile, the AMA’s legislative expert, who declined to be identified because of organization policy, acknowledged the possibility that some prior authorization bills will die in state legislatures this year.

“We remain hopeful, but it’s an uphill battle. The state medical associations face a lot of opposition from health plans who don’t want to see these reforms become law.”

A version of this article originally appeared on Medscape.com.

Amid growing criticism of health insurers’ onerous prior authorization practices, lawmakers in 30 states have introduced bills this year that aim to rein in insurer gatekeeping and improve patient care.

“This is something that goes on in every doctor’s office every day; the frustrations, the delays, and the use of office staff time are just unbelievable,” said Steven Orland, MD, a board-certified urologist and president of the Medical Society of New Jersey.

The bills, which cover private health plans and insurers that states regulate, may provide some relief for physicians as federal efforts to streamline prior authorization for some Medicare patients have lagged.

Last year, Congress failed to pass the Improving Seniors’ Timely Access to Care Act of 2021, despite 326 co-sponsors. The bill would have compelled insurers covering Medicare Advantage enrollees to speed up prior authorizations, make the process more transparent, and remove obstacles such as requiring fax machine submissions.

Last month, however, the Centers for Medicare & Medicaid Services issued a final rule that will improve some aspects of prior authorizations in Medicare Advantage insurance plans and ensure that enrollees have the same access to necessary care as traditional Medicare enrollees.

The insurance industry has long defended prior authorization requirements and opposed legislation that would limit them.

America’s Health Insurance Plans (AHIP) and the Blue Cross Blue Shield Association said in a 2019 letter to a congressional committee when the federal legislation was first introduced, “Prior authorizations enforce best practices and guidelines for care management and help physicians identify and avoid care techniques that would harm patient outcomes, such as designating prescriptions that could feed into an opioid addiction.” AHIP didn’t respond to repeated requests for comment.

But some major insurers now appear willing to compromise and voluntarily reduce the volume of prior authorizations they require. Days before the federal final rule was released, three major insurers – United HealthCare, Cigna, and Aetna CVS Health – announced they plan to drop some prior authorization requirements and automate processes.

United HealthCare said it will eliminate almost 20% of its prior authorizations for some nonurgent surgeries and procedures starting this summer. It also will create a national Gold Card program in 2024 for physicians who meet its eligibility requirements, which would eliminate prior authorization requirements for most procedures. Both initiatives will apply to commercial, Medicare Advantage, and Medicaid businesses, said the insurer in a statement.

However, United HealthCare also announced that in June it will start requiring prior authorization for diagnostic (not screening) gastrointestinal endoscopies for its nearly 27 million privately insured patients, citing data it says shows potentially harmful overuse of scopes. Physician groups have publicly criticized the move, saying it could delay lifesaving treatment, and have asked the insurer to reconsider.

Cigna and Aetna also have moved to pare back prior authorization processes. Scott Josephs, national medical officer for Cigna, told Healthcare Dive that Cigna has removed prior authorization reviews from nearly 500 services since 2020.

An Aetna spokesperson told Healthcare Dive that the CVS-owned payer has implemented a gold card program and rolled back prior authorization requirements on cataract surgeries, video EEGs, and home infusion for some drugs, according to Healthcare Dive.

Cigna has faced increased scrutiny from some state regulators since a ProPublica/The Capitol Forum article revealed in March that its doctors were denying claims without opening patients’ files, contrary to what insurance laws and regulations require in many states.

Over a period of 2 months last year, Cigna doctors denied over 300,000 requests for payments using this method, spending an average of 1.2 seconds on each case, the investigation found. In a written response, Cigna said the reporting by ProPublica and The Capitol Forum was “biased and incomplete.”
 

States aim to reduce prior authorization volume

The American Medical Association said it has been tracking nearly 90 prior authorization reform bills in 30 states. More than a dozen bills are still being considered in this legislative session, including in Arkansas, California, New Jersey, North Carolina, Maryland, and Washington, D.C.

“The groundswell of activity in the states reflects how big a problem this is,” said an AMA legislative expert. “The issue used to be ‘how can we automate and streamline processes’; now the issue is focused on reducing the volume of prior authorizations and the harm that can cause patients.”

The state bills use different strategies to reduce excessive prior authorization requirements. Maryland’s proposed bill, for example, would require just one prior authorization to stay on a prescription drug, if the insurer has previously approved the drug and the patient continues to successfully be treated by the drug.

Washington, D.C. and New Jersey have introduced comprehensive reform bills that include a “grace period” of 60 days, to ensure continuity of care when a patient switches health plans. They also would eliminate repeat authorizations for chronic and long-term conditions, set explicit timelines for insurers to respond to prior authorization requests and appeals, and require that practicing physicians review denials that are appealed.

Many state bills also would require insurers to be more transparent by posting information on their websites about which services and drugs require prior authorization and what their approval rates are for them, said AMA’s legislative expert.

“There’s a black hole of information that insurers have access to. We would really like to know how many prior authorization requests are denied, the time it takes to deny them, and the reasons for denial,” said Josh Bengal, JD, the director of government relations for the Medical Society of New Jersey.

The legislation in New Jersey and other states faces stiff opposition from the insurance lobby, especially state associations of health plans affiliated with AHIP. The California Association of Health Plans, for example, opposes a “gold card” bill (SB 598), introduced in February, that would allow a select group of high-performing doctors to skip prior authorizations for 1 year.

The CAHP states, “Californians deserve safe, high quality, high-value health care. Yet SB 598 will derail the progress we have made in our health care system by lowering the value and safety that Californians should expect from their health care providers,” according to a fact sheet.

The fact-sheet defines “low-value care” as medical services for which there is little to no benefit and poses potential physical or financial harm to patients, such as unnecessary CT scans or MRIs for uncomplicated conditions.

California is one of about a dozen states that have introduced gold card legislation this year. If enacted, they would join five states with gold card laws: West Virginia, Texas, Vermont, Michigan, and Louisiana.
 

How do gold cards work?

Physicians who achieve a high approval rate of prior authorizations from insurers for 1 year are eligible to be exempted from obtaining prior authorizations the following year.

The approval rate is at least 90% for a certain number of eligible health services, but the number of prior authorizations required to qualify can range from 5 to 30, depending on the state law.

Gold card legislation typically also gives the treating physician the right to have an appeal of a prior authorization denial by a physician peer of the same or similar specialty.

California’s bill would also apply to all covered health services, which is broader than what United HealthCare has proposed for its gold card exemption. The bill would also require a plan or insurer to annually monitor rates of prior authorization approval, modification, appeal, and denial, and to discontinue services, items, and supplies that are approved 95% of the time.

“These are important reforms that will help ensure that patients can receive the care they need, when they need it,” said CMA president Donaldo Hernandez, MD.

However, it’s not clear how many physicians will meet “gold card” status based on Texas’ recent experience with its own “gold card” law.

The Texas Department of Insurance estimated that only 3.3% of licensed physicians in the state have met “gold card” status since the bill became law in 2021, said Zeke Silva, MD, an interventional radiologist who serves on the Council of Legislation for the Texas Medical Association.

He noted that the legislation has had a limited effect for several reasons. Commercial health plans only make up only about 20% of all health plans in Texas. Also, the final regulations didn’t go into effect until last May and physicians are evaluated by health plans for “gold card” status every 6 months, said Dr. Silva.

In addition, physicians must have at least five prior authorizations approved for the same health service, which the law left up to the health plans to define, said Dr. Silva.

Now, the Texas Medical Association is lobbying for legislative improvements. “We want to reduce the number of eligible services that health plans require for prior authorizations and have more oversight of prior authorization denials by the Texas Department of Insurance and the Texas Medical Board,” said Dr. Silva.

He’s optimistic that if the bill becomes law, the number of physicians eligible for gold cards may increase.

Meanwhile, the AMA’s legislative expert, who declined to be identified because of organization policy, acknowledged the possibility that some prior authorization bills will die in state legislatures this year.

“We remain hopeful, but it’s an uphill battle. The state medical associations face a lot of opposition from health plans who don’t want to see these reforms become law.”

A version of this article originally appeared on Medscape.com.

A new study finds that racial disparities in male breast cancer are persisting in the United States.

From 2000 to 2019, Black men were diagnosed at later ages than White males (median ages, 69 and 63 years, respectively) and were more likely to die from the disease (22.4% vs. 16.8%, respectively). Male breast cancer (MBC) was more likely to kill Black men in rural vs. urban areas (hazard ratio = 1.4; 95% confidence interval, 1.0-2.1; P less than .05). Among White males, in contrast, there was no difference on that front, according to the research, which was presented in a poster (Abstract No. 87P) at the European Society for Medical Oncology (ESMO) Breast Cancer annual congress.

It’s not clear why the disparities exist, said lead author Lekha Yadukumar, MBBS, an internal medicine resident at the Wright Center for Graduate Medical Education in Scranton, Penn., in an interview.

“Several potential factors may contribute to the higher rate of breast cancer diagnosis in older [Black] men, including the pathology of the disease, limited awareness about breast cancer, and potential barriers to accessibility,” she said. “The increased mortality among [Black men] may be linked to variations in tumor pathology and molecular biology. Social factors may also potentially impact survival rates, including [having] limited access to health care in rural areas and inadequate social support.”

Male breast cancer is rare, accounting for less than 1% of all breast cancer cases in the United States, according to the Breast Cancer Research Foundation. An estimated 2,700 men are diagnosed each year, and about 530 will die. Previous research has suggested Black men have worse outcomes than White men, but the data covered earlier years than the new study.
 

Methods and results

Dr. Yadukumar and colleagues retrospectively analyzed statistics from the Surveillance, Epidemiology, and End Results database for patients diagnosed with primary male breast cancer from 2000 to 2019 (n = 8,373; Black men, 1,111 [13.26%]; White men, 6,817 [81.41%]).

Median income didn’t affect mortality, whereas men in both racial groups were less likely to die if they were married vs. single/divorced (hazard ratio = 0.6; P less than .05).

Other studies have shown that “[Black American] men diagnosed with breast cancer experience longer time intervals before receiving treatment, encounter more severe disease manifestations, and exhibit lower rates of survivorship,” Dr. Yadukumar said. “Despite these findings, there remains a scarcity of genetic studies aimed at comprehending the underlying causes of these disparities. Moreover, there is a dearth of research investigating other factors that may influence survival outcomes among men with breast cancer.”
 

Findings reflect the disparities in female breast cancer

In an interview, Duke University, Durham, N.C., oncologist Arif Kamal, MD, MBA, MHS, the chief patient officer at the American Cancer Society, said the study is impressive since the number of patients is large for a rare cancer and the population is diverse. Plus, the findings reflect the disparities in female breast cancer, he noted.

“We know that Black women’s mortality is worse vs. White women in breast cancer, and we believe that most of that has nothing to do with cancer screening,” said Dr. Kamal, who was not involved in the new study. “When the clock starts from diagnosis onwards, you start to see less introduction to clinical trials and standard care medications and more time to treatment, surgery, and radiation,” he said.

“You see similar disparities as related to mortality in Black vs. White men,” he noted.

The new findings about higher death rates for Black men, especially in rural areas, suggest that “distance matters, and race matters,” he said. In rural areas, it can be hard to access pathologists, radiologists, and surgeons with more experience with breast cancer, he said.

But, he noted, the study finds that income doesn’t appear to be a factor.

In the big picture, he said, the results suggest that when it comes to barriers to better outcomes, “things that are systemic don’t make exceptions because you are a man vs. a woman.”

No study funding was reported. The study authors and Dr. Kamal have no relevant financial disclosures.
 

A new study finds that racial disparities in male breast cancer are persisting in the United States.

From 2000 to 2019, Black men were diagnosed at later ages than White males (median ages, 69 and 63 years, respectively) and were more likely to die from the disease (22.4% vs. 16.8%, respectively). Male breast cancer (MBC) was more likely to kill Black men in rural vs. urban areas (hazard ratio = 1.4; 95% confidence interval, 1.0-2.1; P less than .05). Among White males, in contrast, there was no difference on that front, according to the research, which was presented in a poster (Abstract No. 87P) at the European Society for Medical Oncology (ESMO) Breast Cancer annual congress.

It’s not clear why the disparities exist, said lead author Lekha Yadukumar, MBBS, an internal medicine resident at the Wright Center for Graduate Medical Education in Scranton, Penn., in an interview.

“Several potential factors may contribute to the higher rate of breast cancer diagnosis in older [Black] men, including the pathology of the disease, limited awareness about breast cancer, and potential barriers to accessibility,” she said. “The increased mortality among [Black men] may be linked to variations in tumor pathology and molecular biology. Social factors may also potentially impact survival rates, including [having] limited access to health care in rural areas and inadequate social support.”

Male breast cancer is rare, accounting for less than 1% of all breast cancer cases in the United States, according to the Breast Cancer Research Foundation. An estimated 2,700 men are diagnosed each year, and about 530 will die. Previous research has suggested Black men have worse outcomes than White men, but the data covered earlier years than the new study.
 

Methods and results

Dr. Yadukumar and colleagues retrospectively analyzed statistics from the Surveillance, Epidemiology, and End Results database for patients diagnosed with primary male breast cancer from 2000 to 2019 (n = 8,373; Black men, 1,111 [13.26%]; White men, 6,817 [81.41%]).

Median income didn’t affect mortality, whereas men in both racial groups were less likely to die if they were married vs. single/divorced (hazard ratio = 0.6; P less than .05).

Other studies have shown that “[Black American] men diagnosed with breast cancer experience longer time intervals before receiving treatment, encounter more severe disease manifestations, and exhibit lower rates of survivorship,” Dr. Yadukumar said. “Despite these findings, there remains a scarcity of genetic studies aimed at comprehending the underlying causes of these disparities. Moreover, there is a dearth of research investigating other factors that may influence survival outcomes among men with breast cancer.”
 

Findings reflect the disparities in female breast cancer

In an interview, Duke University, Durham, N.C., oncologist Arif Kamal, MD, MBA, MHS, the chief patient officer at the American Cancer Society, said the study is impressive since the number of patients is large for a rare cancer and the population is diverse. Plus, the findings reflect the disparities in female breast cancer, he noted.

“We know that Black women’s mortality is worse vs. White women in breast cancer, and we believe that most of that has nothing to do with cancer screening,” said Dr. Kamal, who was not involved in the new study. “When the clock starts from diagnosis onwards, you start to see less introduction to clinical trials and standard care medications and more time to treatment, surgery, and radiation,” he said.

“You see similar disparities as related to mortality in Black vs. White men,” he noted.

The new findings about higher death rates for Black men, especially in rural areas, suggest that “distance matters, and race matters,” he said. In rural areas, it can be hard to access pathologists, radiologists, and surgeons with more experience with breast cancer, he said.

But, he noted, the study finds that income doesn’t appear to be a factor.

In the big picture, he said, the results suggest that when it comes to barriers to better outcomes, “things that are systemic don’t make exceptions because you are a man vs. a woman.”

No study funding was reported. The study authors and Dr. Kamal have no relevant financial disclosures.
 

A new study finds that racial disparities in male breast cancer are persisting in the United States.

From 2000 to 2019, Black men were diagnosed at later ages than White males (median ages, 69 and 63 years, respectively) and were more likely to die from the disease (22.4% vs. 16.8%, respectively). Male breast cancer (MBC) was more likely to kill Black men in rural vs. urban areas (hazard ratio = 1.4; 95% confidence interval, 1.0-2.1; P less than .05). Among White males, in contrast, there was no difference on that front, according to the research, which was presented in a poster (Abstract No. 87P) at the European Society for Medical Oncology (ESMO) Breast Cancer annual congress.

It’s not clear why the disparities exist, said lead author Lekha Yadukumar, MBBS, an internal medicine resident at the Wright Center for Graduate Medical Education in Scranton, Penn., in an interview.

“Several potential factors may contribute to the higher rate of breast cancer diagnosis in older [Black] men, including the pathology of the disease, limited awareness about breast cancer, and potential barriers to accessibility,” she said. “The increased mortality among [Black men] may be linked to variations in tumor pathology and molecular biology. Social factors may also potentially impact survival rates, including [having] limited access to health care in rural areas and inadequate social support.”

Male breast cancer is rare, accounting for less than 1% of all breast cancer cases in the United States, according to the Breast Cancer Research Foundation. An estimated 2,700 men are diagnosed each year, and about 530 will die. Previous research has suggested Black men have worse outcomes than White men, but the data covered earlier years than the new study.
 

Methods and results

Dr. Yadukumar and colleagues retrospectively analyzed statistics from the Surveillance, Epidemiology, and End Results database for patients diagnosed with primary male breast cancer from 2000 to 2019 (n = 8,373; Black men, 1,111 [13.26%]; White men, 6,817 [81.41%]).

Median income didn’t affect mortality, whereas men in both racial groups were less likely to die if they were married vs. single/divorced (hazard ratio = 0.6; P less than .05).

Other studies have shown that “[Black American] men diagnosed with breast cancer experience longer time intervals before receiving treatment, encounter more severe disease manifestations, and exhibit lower rates of survivorship,” Dr. Yadukumar said. “Despite these findings, there remains a scarcity of genetic studies aimed at comprehending the underlying causes of these disparities. Moreover, there is a dearth of research investigating other factors that may influence survival outcomes among men with breast cancer.”
 

Findings reflect the disparities in female breast cancer

In an interview, Duke University, Durham, N.C., oncologist Arif Kamal, MD, MBA, MHS, the chief patient officer at the American Cancer Society, said the study is impressive since the number of patients is large for a rare cancer and the population is diverse. Plus, the findings reflect the disparities in female breast cancer, he noted.

“We know that Black women’s mortality is worse vs. White women in breast cancer, and we believe that most of that has nothing to do with cancer screening,” said Dr. Kamal, who was not involved in the new study. “When the clock starts from diagnosis onwards, you start to see less introduction to clinical trials and standard care medications and more time to treatment, surgery, and radiation,” he said.

“You see similar disparities as related to mortality in Black vs. White men,” he noted.

The new findings about higher death rates for Black men, especially in rural areas, suggest that “distance matters, and race matters,” he said. In rural areas, it can be hard to access pathologists, radiologists, and surgeons with more experience with breast cancer, he said.

But, he noted, the study finds that income doesn’t appear to be a factor.

In the big picture, he said, the results suggest that when it comes to barriers to better outcomes, “things that are systemic don’t make exceptions because you are a man vs. a woman.”

No study funding was reported. The study authors and Dr. Kamal have no relevant financial disclosures.
 

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; [email protected]

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; [email protected]

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; [email protected]

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; [email protected]

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; [email protected]

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; [email protected]

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]