Suzanne Bopp

As many hospitalists know, a frequently deployed approach to quality improvement (QI) is the Plan-Do-Study-Act (PDSA) cycle method. But it comes with challenges, according to a recent paper in BMJ Quality & Safety.

“There is little evidence on the fidelity of PDSA cycles used by frontline teams, nor how to support and improve the method’s use,” according to the authors. They used document analysis and interviews to review 421 PDSA cycles, tracking fidelity over three annual rounds of projects.

The researchers found that modest, statistically significant improvements in PDSA fidelity occurred, but overall fidelity was low.

“Challenges to achieving greater fidelity reflected problems with understanding the PDSA methodology, intention to use and application in practice,” the authors reported. “These problems were exacerbated by assumptions made in the original QI training and support strategies: that PDSA was easy to understand, that teams would be motivated and willing to use PDSA, and that PDSA is easy to apply.”

The study describes several strategies to help improve PDSA cycle fidelity: different project selection process, redesign of training, increased hands-on support, and investment in training quality improvement support staff. “The findings suggest achieving high PDSA fidelity requires a gradual and negotiated process to explore different perspectives and encourage new ways of working,” the authors concluded.

Reference

1. McNicholas C et al. Evolving quality improvement support strategies to improve Plan-Do-Study–Act cycle fidelity: A retrospective mixed-methods study. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2017-007605.

As many hospitalists know, a frequently deployed approach to quality improvement (QI) is the Plan-Do-Study-Act (PDSA) cycle method. But it comes with challenges, according to a recent paper in BMJ Quality & Safety.

“There is little evidence on the fidelity of PDSA cycles used by frontline teams, nor how to support and improve the method’s use,” according to the authors. They used document analysis and interviews to review 421 PDSA cycles, tracking fidelity over three annual rounds of projects.

The researchers found that modest, statistically significant improvements in PDSA fidelity occurred, but overall fidelity was low.

“Challenges to achieving greater fidelity reflected problems with understanding the PDSA methodology, intention to use and application in practice,” the authors reported. “These problems were exacerbated by assumptions made in the original QI training and support strategies: that PDSA was easy to understand, that teams would be motivated and willing to use PDSA, and that PDSA is easy to apply.”

The study describes several strategies to help improve PDSA cycle fidelity: different project selection process, redesign of training, increased hands-on support, and investment in training quality improvement support staff. “The findings suggest achieving high PDSA fidelity requires a gradual and negotiated process to explore different perspectives and encourage new ways of working,” the authors concluded.

Reference

1. McNicholas C et al. Evolving quality improvement support strategies to improve Plan-Do-Study–Act cycle fidelity: A retrospective mixed-methods study. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2017-007605.

As many hospitalists know, a frequently deployed approach to quality improvement (QI) is the Plan-Do-Study-Act (PDSA) cycle method. But it comes with challenges, according to a recent paper in BMJ Quality & Safety.

“There is little evidence on the fidelity of PDSA cycles used by frontline teams, nor how to support and improve the method’s use,” according to the authors. They used document analysis and interviews to review 421 PDSA cycles, tracking fidelity over three annual rounds of projects.

The researchers found that modest, statistically significant improvements in PDSA fidelity occurred, but overall fidelity was low.

“Challenges to achieving greater fidelity reflected problems with understanding the PDSA methodology, intention to use and application in practice,” the authors reported. “These problems were exacerbated by assumptions made in the original QI training and support strategies: that PDSA was easy to understand, that teams would be motivated and willing to use PDSA, and that PDSA is easy to apply.”

The study describes several strategies to help improve PDSA cycle fidelity: different project selection process, redesign of training, increased hands-on support, and investment in training quality improvement support staff. “The findings suggest achieving high PDSA fidelity requires a gradual and negotiated process to explore different perspectives and encourage new ways of working,” the authors concluded.

Reference

1. McNicholas C et al. Evolving quality improvement support strategies to improve Plan-Do-Study–Act cycle fidelity: A retrospective mixed-methods study. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2017-007605.

Emergency department overcrowding is common, and it can result in both increased costs and poor clinical outcomes.

EyeMark/thinkstockphotos.com

“We sought to evaluate the impact and safety of hospitalist-directed transfers on patients boarding in the ER as a means to alleviate overcrowding,” said Yihan Chen, MD, MPH, of the University of California, Los Angeles. “High inpatient census has been shown to impair ER throughput by increasing the number of ER ‘boarders,’ which creates a suboptimal care environment for practicing hospitalists. For example, some studies have shown associations with delays in medical decision making when admitted patients remain and receive care in the emergency department.”

Dr. Chen was the lead author of an abstract describing a chart review on 1,016 admissions to the hospitalist service. About half remained at the reference hospital and half were transferred to a nearby affiliate hospital.

In analyzing the data, the researchers’ top takeaway was the many benefits for the transferred patients. “Hospitalist-directed transfer and direct admission of stable ER patients to an affiliate facility with greater bed availability is associated with shorter ER lengths of stay, fewer adverse events, and lower rates of readmission within 30 days of hospitalization,” Dr. Chen said. “Having a system in place to transfer patients to an affiliate hospital with lower census is a way to improve flow.”

Hospitalists have a unique opportunity to take on a triage role in the ED to safely and effectively decrease ED overcrowding and throughput, improve resource utilization at the hospital level, and allow for other hospitalists at their institution to optimize patient care on the inpatient ward rather than in the ED, Dr. Chen said.

“Health systems privileged to have more than one facility should consider an intra–health system transfer process lead by triage hospitalists to identify stable patients who can be directly admitted to the off-site, affiliate hospital,” she said. “By improving patient throughput, hospitalists would play a critical role in relieving institutional stressors, impacting cost and quality of care, and enhancing clinical outcomes.”

Reference

1. Chen Y et al. Hospitalist-Directed Transfers Improve Emergency Room Length of Stay. Hospital Medicine 2018, Abstract 12. Accessed April 3, 2019.

Emergency department overcrowding is common, and it can result in both increased costs and poor clinical outcomes.

EyeMark/thinkstockphotos.com

“We sought to evaluate the impact and safety of hospitalist-directed transfers on patients boarding in the ER as a means to alleviate overcrowding,” said Yihan Chen, MD, MPH, of the University of California, Los Angeles. “High inpatient census has been shown to impair ER throughput by increasing the number of ER ‘boarders,’ which creates a suboptimal care environment for practicing hospitalists. For example, some studies have shown associations with delays in medical decision making when admitted patients remain and receive care in the emergency department.”

Dr. Chen was the lead author of an abstract describing a chart review on 1,016 admissions to the hospitalist service. About half remained at the reference hospital and half were transferred to a nearby affiliate hospital.

In analyzing the data, the researchers’ top takeaway was the many benefits for the transferred patients. “Hospitalist-directed transfer and direct admission of stable ER patients to an affiliate facility with greater bed availability is associated with shorter ER lengths of stay, fewer adverse events, and lower rates of readmission within 30 days of hospitalization,” Dr. Chen said. “Having a system in place to transfer patients to an affiliate hospital with lower census is a way to improve flow.”

Hospitalists have a unique opportunity to take on a triage role in the ED to safely and effectively decrease ED overcrowding and throughput, improve resource utilization at the hospital level, and allow for other hospitalists at their institution to optimize patient care on the inpatient ward rather than in the ED, Dr. Chen said.

“Health systems privileged to have more than one facility should consider an intra–health system transfer process lead by triage hospitalists to identify stable patients who can be directly admitted to the off-site, affiliate hospital,” she said. “By improving patient throughput, hospitalists would play a critical role in relieving institutional stressors, impacting cost and quality of care, and enhancing clinical outcomes.”

Reference

1. Chen Y et al. Hospitalist-Directed Transfers Improve Emergency Room Length of Stay. Hospital Medicine 2018, Abstract 12. Accessed April 3, 2019.

Emergency department overcrowding is common, and it can result in both increased costs and poor clinical outcomes.

EyeMark/thinkstockphotos.com

“We sought to evaluate the impact and safety of hospitalist-directed transfers on patients boarding in the ER as a means to alleviate overcrowding,” said Yihan Chen, MD, MPH, of the University of California, Los Angeles. “High inpatient census has been shown to impair ER throughput by increasing the number of ER ‘boarders,’ which creates a suboptimal care environment for practicing hospitalists. For example, some studies have shown associations with delays in medical decision making when admitted patients remain and receive care in the emergency department.”

Dr. Chen was the lead author of an abstract describing a chart review on 1,016 admissions to the hospitalist service. About half remained at the reference hospital and half were transferred to a nearby affiliate hospital.

In analyzing the data, the researchers’ top takeaway was the many benefits for the transferred patients. “Hospitalist-directed transfer and direct admission of stable ER patients to an affiliate facility with greater bed availability is associated with shorter ER lengths of stay, fewer adverse events, and lower rates of readmission within 30 days of hospitalization,” Dr. Chen said. “Having a system in place to transfer patients to an affiliate hospital with lower census is a way to improve flow.”

Hospitalists have a unique opportunity to take on a triage role in the ED to safely and effectively decrease ED overcrowding and throughput, improve resource utilization at the hospital level, and allow for other hospitalists at their institution to optimize patient care on the inpatient ward rather than in the ED, Dr. Chen said.

“Health systems privileged to have more than one facility should consider an intra–health system transfer process lead by triage hospitalists to identify stable patients who can be directly admitted to the off-site, affiliate hospital,” she said. “By improving patient throughput, hospitalists would play a critical role in relieving institutional stressors, impacting cost and quality of care, and enhancing clinical outcomes.”

Reference

1. Chen Y et al. Hospitalist-Directed Transfers Improve Emergency Room Length of Stay. Hospital Medicine 2018, Abstract 12. Accessed April 3, 2019.

Artificial intelligence (A.I.) is likely to change almost everything in medical practice, according to a new book called “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again,” by Eric Topol, MD.

archy13/Getty Images

Dr. Topol told The Hospitalist that his book’s subtitle “is the paradox: the unexpected, far-reaching goal of A.I. that can, if used properly, restore the most important part of medicine – a deep patient-doctor relationship.”

That’s because A.I. can do more than enhance diagnoses; it can also help with tasks such as note-taking and reading scans, making it possible for hospitalists to spend more time connecting with their patients. “Hospitalists could have a much better handle on a patient’s dataset via algorithmic processing, providing alerts and augmented performance of hospitalists (when validated),” Dr. Topol said. “They can also expect far less keyboard use with the help of speech recognition, natural language processing, and deep learning.”In an interview with the New York Times, Dr. Topol said that by augmenting human performance, A.I. has the potential to markedly improve productivity, efficiency, work flow, accuracy and speed, both for doctors and for patients, giving more charge and control to consumers through algorithmic support of their data.

“We can’t, and will never, rely on only algorithms for interpretation of life and death matters,” he said. “That requires human expert contextualization, something machines can’t do.”Of course, there could be pitfalls. “The liabilities include breaches of privacy and security, hacking, the lack of explainability of most A.I. algorithms, the potential to worsen inequities, the embedded bias, and ethical quandaries,” he said.
 

Reference

1. O’Connor A. How Artificial Intelligence Could Transform Medicine. New York Times. March 11, 2019. https://www.nytimes.com/2019/03/11/well/live/how-artificial-intelligence-could-transform-medicine.html.

Artificial intelligence (A.I.) is likely to change almost everything in medical practice, according to a new book called “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again,” by Eric Topol, MD.

archy13/Getty Images

Dr. Topol told The Hospitalist that his book’s subtitle “is the paradox: the unexpected, far-reaching goal of A.I. that can, if used properly, restore the most important part of medicine – a deep patient-doctor relationship.”

That’s because A.I. can do more than enhance diagnoses; it can also help with tasks such as note-taking and reading scans, making it possible for hospitalists to spend more time connecting with their patients. “Hospitalists could have a much better handle on a patient’s dataset via algorithmic processing, providing alerts and augmented performance of hospitalists (when validated),” Dr. Topol said. “They can also expect far less keyboard use with the help of speech recognition, natural language processing, and deep learning.”In an interview with the New York Times, Dr. Topol said that by augmenting human performance, A.I. has the potential to markedly improve productivity, efficiency, work flow, accuracy and speed, both for doctors and for patients, giving more charge and control to consumers through algorithmic support of their data.

“We can’t, and will never, rely on only algorithms for interpretation of life and death matters,” he said. “That requires human expert contextualization, something machines can’t do.”Of course, there could be pitfalls. “The liabilities include breaches of privacy and security, hacking, the lack of explainability of most A.I. algorithms, the potential to worsen inequities, the embedded bias, and ethical quandaries,” he said.
 

Reference

1. O’Connor A. How Artificial Intelligence Could Transform Medicine. New York Times. March 11, 2019. https://www.nytimes.com/2019/03/11/well/live/how-artificial-intelligence-could-transform-medicine.html.

Artificial intelligence (A.I.) is likely to change almost everything in medical practice, according to a new book called “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again,” by Eric Topol, MD.

archy13/Getty Images

Dr. Topol told The Hospitalist that his book’s subtitle “is the paradox: the unexpected, far-reaching goal of A.I. that can, if used properly, restore the most important part of medicine – a deep patient-doctor relationship.”

That’s because A.I. can do more than enhance diagnoses; it can also help with tasks such as note-taking and reading scans, making it possible for hospitalists to spend more time connecting with their patients. “Hospitalists could have a much better handle on a patient’s dataset via algorithmic processing, providing alerts and augmented performance of hospitalists (when validated),” Dr. Topol said. “They can also expect far less keyboard use with the help of speech recognition, natural language processing, and deep learning.”In an interview with the New York Times, Dr. Topol said that by augmenting human performance, A.I. has the potential to markedly improve productivity, efficiency, work flow, accuracy and speed, both for doctors and for patients, giving more charge and control to consumers through algorithmic support of their data.

“We can’t, and will never, rely on only algorithms for interpretation of life and death matters,” he said. “That requires human expert contextualization, something machines can’t do.”Of course, there could be pitfalls. “The liabilities include breaches of privacy and security, hacking, the lack of explainability of most A.I. algorithms, the potential to worsen inequities, the embedded bias, and ethical quandaries,” he said.
 

Reference

1. O’Connor A. How Artificial Intelligence Could Transform Medicine. New York Times. March 11, 2019. https://www.nytimes.com/2019/03/11/well/live/how-artificial-intelligence-could-transform-medicine.html.

Quality improvement (QI) education has become increasingly seen as core content in graduate medical education, said Brian Wong, MD, FRCPC, of the University of Toronto. One of the most common strategies for teaching QI is to have residents participate in a QI project, in which hospitalists often take a leading role.

“Given the investment made and time spent carrying out these projects, it is important to know whether or not the training has led to the desired outcome from both a learning and a project standpoint,” Dr. Wong said, which is why he coauthored a recent editorial on the subject in BMJ Quality and Safety. QI educators have long recognized that it’s difficult to know whether the education was successful.

“For example, if the project was not successful, does it matter if the residents learned key QI principles that they were able to apply to their project work?” Dr. Wong noted. “Our perspective extends this discussion by asking, ‘What does success look like in QI education?’ We argue that rather than focusing on whether the project was successful or not, our real goal should be to create QI educational experiences that will ensure that residents change their behaviors in future practice to embrace QI as an activity that is core to their everyday work.”

Hospitalists have an important role in that. “They can set the stage for learners to recognize just how important it is to incorporate QI into daily work. Through this role modeling, residents who carry out QI projects can see that the lessons learned contribute to lifelong engagement in QI.”

Dr. Wong’s hope is to focus on the type of QI experience that fosters long-term behavior changes.

“We want residents, when they graduate, to embrace QI, to volunteer to participate in organizational initiatives, to welcome practice data and reflect on it for the purposes of continuous improvement, to collaborate interprofessionally to make small iterative changes to the care delivery system to ensure that patients receive the highest quality of care possible,” he said. “My hope is that we can start to think differently about how we measure success in QI education.”

Reference

1. Myers JS, Wong BM. Measuring outcomes in quality improvement education: Success is in the eye of the beholder. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2018-008305.

Quality improvement (QI) education has become increasingly seen as core content in graduate medical education, said Brian Wong, MD, FRCPC, of the University of Toronto. One of the most common strategies for teaching QI is to have residents participate in a QI project, in which hospitalists often take a leading role.

“Given the investment made and time spent carrying out these projects, it is important to know whether or not the training has led to the desired outcome from both a learning and a project standpoint,” Dr. Wong said, which is why he coauthored a recent editorial on the subject in BMJ Quality and Safety. QI educators have long recognized that it’s difficult to know whether the education was successful.

“For example, if the project was not successful, does it matter if the residents learned key QI principles that they were able to apply to their project work?” Dr. Wong noted. “Our perspective extends this discussion by asking, ‘What does success look like in QI education?’ We argue that rather than focusing on whether the project was successful or not, our real goal should be to create QI educational experiences that will ensure that residents change their behaviors in future practice to embrace QI as an activity that is core to their everyday work.”

Hospitalists have an important role in that. “They can set the stage for learners to recognize just how important it is to incorporate QI into daily work. Through this role modeling, residents who carry out QI projects can see that the lessons learned contribute to lifelong engagement in QI.”

Dr. Wong’s hope is to focus on the type of QI experience that fosters long-term behavior changes.

“We want residents, when they graduate, to embrace QI, to volunteer to participate in organizational initiatives, to welcome practice data and reflect on it for the purposes of continuous improvement, to collaborate interprofessionally to make small iterative changes to the care delivery system to ensure that patients receive the highest quality of care possible,” he said. “My hope is that we can start to think differently about how we measure success in QI education.”

Reference

1. Myers JS, Wong BM. Measuring outcomes in quality improvement education: Success is in the eye of the beholder. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2018-008305.

Quality improvement (QI) education has become increasingly seen as core content in graduate medical education, said Brian Wong, MD, FRCPC, of the University of Toronto. One of the most common strategies for teaching QI is to have residents participate in a QI project, in which hospitalists often take a leading role.

“Given the investment made and time spent carrying out these projects, it is important to know whether or not the training has led to the desired outcome from both a learning and a project standpoint,” Dr. Wong said, which is why he coauthored a recent editorial on the subject in BMJ Quality and Safety. QI educators have long recognized that it’s difficult to know whether the education was successful.

“For example, if the project was not successful, does it matter if the residents learned key QI principles that they were able to apply to their project work?” Dr. Wong noted. “Our perspective extends this discussion by asking, ‘What does success look like in QI education?’ We argue that rather than focusing on whether the project was successful or not, our real goal should be to create QI educational experiences that will ensure that residents change their behaviors in future practice to embrace QI as an activity that is core to their everyday work.”

Hospitalists have an important role in that. “They can set the stage for learners to recognize just how important it is to incorporate QI into daily work. Through this role modeling, residents who carry out QI projects can see that the lessons learned contribute to lifelong engagement in QI.”

Dr. Wong’s hope is to focus on the type of QI experience that fosters long-term behavior changes.

“We want residents, when they graduate, to embrace QI, to volunteer to participate in organizational initiatives, to welcome practice data and reflect on it for the purposes of continuous improvement, to collaborate interprofessionally to make small iterative changes to the care delivery system to ensure that patients receive the highest quality of care possible,” he said. “My hope is that we can start to think differently about how we measure success in QI education.”

Reference

1. Myers JS, Wong BM. Measuring outcomes in quality improvement education: Success is in the eye of the beholder. BMJ Qual Saf. 2019 Mar 18. doi: 10.1136/bmjqs-2018-008305.

Hundreds of scientists around the world enter a competition every 2 years called the Critical Assessment of Structure Prediction.

“Tackling a biological puzzle they call ‘the protein folding problem,’ they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease,” according to a report in the New York Times.

In 2019, those scientists did not win the contest. “It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company. DeepMind specializes in ‘deep learning,’ a type of artificial intelligence that is rapidly changing drug discovery science.”

Reference

1. Metz C. “Making New Drugs With a Dose of Artificial Intelligence,” New York Times. Feb. 5, 2019. https://www.nytimes.com/2019/02/05/technology/artificial-intelligence-drug-research-deepmind.html. Accessed Feb 7, 2019.

Hundreds of scientists around the world enter a competition every 2 years called the Critical Assessment of Structure Prediction.

“Tackling a biological puzzle they call ‘the protein folding problem,’ they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease,” according to a report in the New York Times.

In 2019, those scientists did not win the contest. “It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company. DeepMind specializes in ‘deep learning,’ a type of artificial intelligence that is rapidly changing drug discovery science.”

Reference

1. Metz C. “Making New Drugs With a Dose of Artificial Intelligence,” New York Times. Feb. 5, 2019. https://www.nytimes.com/2019/02/05/technology/artificial-intelligence-drug-research-deepmind.html. Accessed Feb 7, 2019.

Hundreds of scientists around the world enter a competition every 2 years called the Critical Assessment of Structure Prediction.

“Tackling a biological puzzle they call ‘the protein folding problem,’ they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease,” according to a report in the New York Times.

In 2019, those scientists did not win the contest. “It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company. DeepMind specializes in ‘deep learning,’ a type of artificial intelligence that is rapidly changing drug discovery science.”

Reference

1. Metz C. “Making New Drugs With a Dose of Artificial Intelligence,” New York Times. Feb. 5, 2019. https://www.nytimes.com/2019/02/05/technology/artificial-intelligence-drug-research-deepmind.html. Accessed Feb 7, 2019.

In 2017, the United States spent $3.5 trillion on health care, and that number is projected to be close 20% of our GDP over the next 10 years. For consumers, prescription drugs feel like the biggest contributor.

“Although pharmaceutical spending accounts for less than 10% of health care spending, consumers bear much more of the out-of-pocket cost of the prescription drugs through copays or coinsurance at the pharmacy counter than they pay for hospital or physician costs,” said Tanisha Carino, PhD, author of a Health Affairs blog post about directions for innovation in health care. “This experience has led to rising concerns among Americans about the cost of prescription drugs.”

In fact, a December 2018 Politico-Harvard poll showed Americans from both political parties overwhelmingly agreed that taking action to lower drug prices should have been the top priority of the new Congress that took office in January of this year.

“Addressing the affordability of prescription drugs will require investing in medical research and policies that speed new products to the market that will promote competition and, hopefully, will hold down prices and offer greater choice to patients,” said Dr. Carino, who is executive director of FasterCures, a center of the Milken Institute devoted to improving the biomedical innovation ecosystem. “Policymakers have an opportunity to address the immediate concerns patients have in affording their medication.”

According to Dr. Carino, policymakers can also continue to encourage health-improving medical innovation through the following:

• Boosting investment in research and development.
• Increasing safety and coordination of health data for biomedical research.
• Incentivizing innovation in underinvested areas.
• Building the capacity of patient organizations.

Hospitalists, she added, will play a critical role in participating in the clinical research that will lead to the next generation of treatments.
 

Reference

1. Carino T. “To get more bang for your health-care buck, invest in innovation.” Health Affairs Blog. 2019 Jan 24. doi: 10.1377/hblog20190123.483080. Accessed Feb. 6, 2019.

In 2017, the United States spent $3.5 trillion on health care, and that number is projected to be close 20% of our GDP over the next 10 years. For consumers, prescription drugs feel like the biggest contributor.

“Although pharmaceutical spending accounts for less than 10% of health care spending, consumers bear much more of the out-of-pocket cost of the prescription drugs through copays or coinsurance at the pharmacy counter than they pay for hospital or physician costs,” said Tanisha Carino, PhD, author of a Health Affairs blog post about directions for innovation in health care. “This experience has led to rising concerns among Americans about the cost of prescription drugs.”

In fact, a December 2018 Politico-Harvard poll showed Americans from both political parties overwhelmingly agreed that taking action to lower drug prices should have been the top priority of the new Congress that took office in January of this year.

“Addressing the affordability of prescription drugs will require investing in medical research and policies that speed new products to the market that will promote competition and, hopefully, will hold down prices and offer greater choice to patients,” said Dr. Carino, who is executive director of FasterCures, a center of the Milken Institute devoted to improving the biomedical innovation ecosystem. “Policymakers have an opportunity to address the immediate concerns patients have in affording their medication.”

According to Dr. Carino, policymakers can also continue to encourage health-improving medical innovation through the following:

• Boosting investment in research and development.
• Increasing safety and coordination of health data for biomedical research.
• Incentivizing innovation in underinvested areas.
• Building the capacity of patient organizations.

Hospitalists, she added, will play a critical role in participating in the clinical research that will lead to the next generation of treatments.
 

Reference

1. Carino T. “To get more bang for your health-care buck, invest in innovation.” Health Affairs Blog. 2019 Jan 24. doi: 10.1377/hblog20190123.483080. Accessed Feb. 6, 2019.

In 2017, the United States spent $3.5 trillion on health care, and that number is projected to be close 20% of our GDP over the next 10 years. For consumers, prescription drugs feel like the biggest contributor.

“Although pharmaceutical spending accounts for less than 10% of health care spending, consumers bear much more of the out-of-pocket cost of the prescription drugs through copays or coinsurance at the pharmacy counter than they pay for hospital or physician costs,” said Tanisha Carino, PhD, author of a Health Affairs blog post about directions for innovation in health care. “This experience has led to rising concerns among Americans about the cost of prescription drugs.”

In fact, a December 2018 Politico-Harvard poll showed Americans from both political parties overwhelmingly agreed that taking action to lower drug prices should have been the top priority of the new Congress that took office in January of this year.

“Addressing the affordability of prescription drugs will require investing in medical research and policies that speed new products to the market that will promote competition and, hopefully, will hold down prices and offer greater choice to patients,” said Dr. Carino, who is executive director of FasterCures, a center of the Milken Institute devoted to improving the biomedical innovation ecosystem. “Policymakers have an opportunity to address the immediate concerns patients have in affording their medication.”

According to Dr. Carino, policymakers can also continue to encourage health-improving medical innovation through the following:

• Boosting investment in research and development.
• Increasing safety and coordination of health data for biomedical research.
• Incentivizing innovation in underinvested areas.
• Building the capacity of patient organizations.

Hospitalists, she added, will play a critical role in participating in the clinical research that will lead to the next generation of treatments.
 

Reference

1. Carino T. “To get more bang for your health-care buck, invest in innovation.” Health Affairs Blog. 2019 Jan 24. doi: 10.1377/hblog20190123.483080. Accessed Feb. 6, 2019.

Physician payment models that include productivity bonuses are widespread, says Reshma Gupta, MD, MSHPM.

“These payment models are thought to affect clinician behavior, with productivity bonuses incentivizing clinicians to do more. While new policies aim to reduce total costs of care, little is known about the association between physician payment models and the culture of delivering high-value care,” said Dr. Gupta, the medical director for quality improvement at UCLA Health in Los Angeles.

To find out if hospitalist reimbursement models are associated with high-value culture in university, community, and safety-net hospitals, internal medicine hospitalists from 12 hospitals across California completed a cross-sectional survey assessing their perceptions of high-value care culture within their institutions. Dr. Gupta and colleagues summarized the results.

The study found that nearly 30% of hospitalists who were sampled reported payment with productivity bonuses, while only 5% of hospitalists sampled reported quality or value-based bonuses, Dr. Gupta said. “Hospitalists who reported payment with productivity bonuses were more likely to report lower high-value care culture within their programs.”

Hospitalist leaders interested in improving high-value care culture can use the High Value Care Culture Survey (http://www.highvaluecareculturesurvey.com) to quickly assess the culture within their programs, diagnose areas of opportunity and target improvement efforts.

“They can test new physician payment models within their programs and evaluate their high-value care culture to identify areas of opportunity for improvement,” Dr. Gupta said.

Reference

1. Gupta R et al. Association between hospitalist productivity payments and high-value care culture. J Hosp Med. 2019;1;16-21.

Physician payment models that include productivity bonuses are widespread, says Reshma Gupta, MD, MSHPM.

“These payment models are thought to affect clinician behavior, with productivity bonuses incentivizing clinicians to do more. While new policies aim to reduce total costs of care, little is known about the association between physician payment models and the culture of delivering high-value care,” said Dr. Gupta, the medical director for quality improvement at UCLA Health in Los Angeles.

To find out if hospitalist reimbursement models are associated with high-value culture in university, community, and safety-net hospitals, internal medicine hospitalists from 12 hospitals across California completed a cross-sectional survey assessing their perceptions of high-value care culture within their institutions. Dr. Gupta and colleagues summarized the results.

The study found that nearly 30% of hospitalists who were sampled reported payment with productivity bonuses, while only 5% of hospitalists sampled reported quality or value-based bonuses, Dr. Gupta said. “Hospitalists who reported payment with productivity bonuses were more likely to report lower high-value care culture within their programs.”

Hospitalist leaders interested in improving high-value care culture can use the High Value Care Culture Survey (http://www.highvaluecareculturesurvey.com) to quickly assess the culture within their programs, diagnose areas of opportunity and target improvement efforts.

“They can test new physician payment models within their programs and evaluate their high-value care culture to identify areas of opportunity for improvement,” Dr. Gupta said.

Reference

1. Gupta R et al. Association between hospitalist productivity payments and high-value care culture. J Hosp Med. 2019;1;16-21.

Physician payment models that include productivity bonuses are widespread, says Reshma Gupta, MD, MSHPM.

“These payment models are thought to affect clinician behavior, with productivity bonuses incentivizing clinicians to do more. While new policies aim to reduce total costs of care, little is known about the association between physician payment models and the culture of delivering high-value care,” said Dr. Gupta, the medical director for quality improvement at UCLA Health in Los Angeles.

To find out if hospitalist reimbursement models are associated with high-value culture in university, community, and safety-net hospitals, internal medicine hospitalists from 12 hospitals across California completed a cross-sectional survey assessing their perceptions of high-value care culture within their institutions. Dr. Gupta and colleagues summarized the results.

The study found that nearly 30% of hospitalists who were sampled reported payment with productivity bonuses, while only 5% of hospitalists sampled reported quality or value-based bonuses, Dr. Gupta said. “Hospitalists who reported payment with productivity bonuses were more likely to report lower high-value care culture within their programs.”

Hospitalist leaders interested in improving high-value care culture can use the High Value Care Culture Survey (http://www.highvaluecareculturesurvey.com) to quickly assess the culture within their programs, diagnose areas of opportunity and target improvement efforts.

“They can test new physician payment models within their programs and evaluate their high-value care culture to identify areas of opportunity for improvement,” Dr. Gupta said.

Reference

1. Gupta R et al. Association between hospitalist productivity payments and high-value care culture. J Hosp Med. 2019;1;16-21.

Artificial intelligence (AI) and machine learning (ML) are promoted as the solution to many health care problems, but the area risks becoming technology led – with only secondary consideration to the safe clinical application of the technology, says Robert Challen, PhD.

Dr. Challen, of the University of Exeter (England), is the lead author of a recent paper that examines the short-, medium-, and long-term issues with medical applications of AI. “In the short term, AI systems will effectively function like laboratory screening tests, identifying patients who are at higher risk than others of disease, or who could benefit more from a particular treatment,” Dr. Challen said. “We usually accept that laboratory tests are useful to help make a diagnosis; however, clinicians are aware that they might not always be accurate and interpret their output in the clinical context. AI systems are no different in that they will be a useful tool so long as they are designed with safety in mind and used with a pragmatic attitude to their interpretation.”

The paper also suggests a set of short-and medium-term clinical safety issues that need addressing when bringing these systems from laboratory to bedside.

In the longer term, as more continuously learning and autonomous systems are developed, the safety risks will need to be continuously reevaluated, he added. “Any new technology comes with limitations and understanding those limitations is key to safe use of that technology. In the same way a new screening test has limitations on its sensitivity and specificity that define how it can be used, AL and ML systems have limitations on accuracy and which patients they can be used on,” Dr. Challen said. If hospitalists understand these limitations, they can participate better in their development.

Dr. Challen recommends that hospitalists help the development of AI tools by participating in studies that assess AI applications in the clinical environment. “Try to make sure that where AI research is taking place, there is strong clinical involvement.”

Reference

1. Challen R et al. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019 Jan 12. doi: 10.1136/bmjqs-2018-008370.

Artificial intelligence (AI) and machine learning (ML) are promoted as the solution to many health care problems, but the area risks becoming technology led – with only secondary consideration to the safe clinical application of the technology, says Robert Challen, PhD.

Dr. Challen, of the University of Exeter (England), is the lead author of a recent paper that examines the short-, medium-, and long-term issues with medical applications of AI. “In the short term, AI systems will effectively function like laboratory screening tests, identifying patients who are at higher risk than others of disease, or who could benefit more from a particular treatment,” Dr. Challen said. “We usually accept that laboratory tests are useful to help make a diagnosis; however, clinicians are aware that they might not always be accurate and interpret their output in the clinical context. AI systems are no different in that they will be a useful tool so long as they are designed with safety in mind and used with a pragmatic attitude to their interpretation.”

The paper also suggests a set of short-and medium-term clinical safety issues that need addressing when bringing these systems from laboratory to bedside.

In the longer term, as more continuously learning and autonomous systems are developed, the safety risks will need to be continuously reevaluated, he added. “Any new technology comes with limitations and understanding those limitations is key to safe use of that technology. In the same way a new screening test has limitations on its sensitivity and specificity that define how it can be used, AL and ML systems have limitations on accuracy and which patients they can be used on,” Dr. Challen said. If hospitalists understand these limitations, they can participate better in their development.

Dr. Challen recommends that hospitalists help the development of AI tools by participating in studies that assess AI applications in the clinical environment. “Try to make sure that where AI research is taking place, there is strong clinical involvement.”

Reference

1. Challen R et al. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019 Jan 12. doi: 10.1136/bmjqs-2018-008370.

Artificial intelligence (AI) and machine learning (ML) are promoted as the solution to many health care problems, but the area risks becoming technology led – with only secondary consideration to the safe clinical application of the technology, says Robert Challen, PhD.

Dr. Challen, of the University of Exeter (England), is the lead author of a recent paper that examines the short-, medium-, and long-term issues with medical applications of AI. “In the short term, AI systems will effectively function like laboratory screening tests, identifying patients who are at higher risk than others of disease, or who could benefit more from a particular treatment,” Dr. Challen said. “We usually accept that laboratory tests are useful to help make a diagnosis; however, clinicians are aware that they might not always be accurate and interpret their output in the clinical context. AI systems are no different in that they will be a useful tool so long as they are designed with safety in mind and used with a pragmatic attitude to their interpretation.”

The paper also suggests a set of short-and medium-term clinical safety issues that need addressing when bringing these systems from laboratory to bedside.

In the longer term, as more continuously learning and autonomous systems are developed, the safety risks will need to be continuously reevaluated, he added. “Any new technology comes with limitations and understanding those limitations is key to safe use of that technology. In the same way a new screening test has limitations on its sensitivity and specificity that define how it can be used, AL and ML systems have limitations on accuracy and which patients they can be used on,” Dr. Challen said. If hospitalists understand these limitations, they can participate better in their development.

Dr. Challen recommends that hospitalists help the development of AI tools by participating in studies that assess AI applications in the clinical environment. “Try to make sure that where AI research is taking place, there is strong clinical involvement.”

Reference

1. Challen R et al. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019 Jan 12. doi: 10.1136/bmjqs-2018-008370.

Seventy percent of Americans would like to have conversations about the costs of care with their health care providers, but only 28% do so, according to polling conducted for the Robert Wood Johnson Foundation (RWJF) by Avalere Health.

utah778/Thinkstock

With those polling results in hand, 2 years ago RWJF and Avalere Health launched the Cost Conversation projects to help. Practice briefs for these kinds of conversations are now available on America’s Essential Hospitals’ website (https://essentialhospitals.org/cost-care/practice-briefs/).
 

Reference

1. Ganos E, Steinberg K, Seidman J, Masi D, Gomez-Rexode A, Fraser A. Talking About Costs: Innovation In Clinician-Patient Conversations. Health Affairs. Published online Nov. 27, 2018. doi: 10.1377/hblog20181126.366161. Accessed Dec. 11, 2018.

Seventy percent of Americans would like to have conversations about the costs of care with their health care providers, but only 28% do so, according to polling conducted for the Robert Wood Johnson Foundation (RWJF) by Avalere Health.

utah778/Thinkstock

With those polling results in hand, 2 years ago RWJF and Avalere Health launched the Cost Conversation projects to help. Practice briefs for these kinds of conversations are now available on America’s Essential Hospitals’ website (https://essentialhospitals.org/cost-care/practice-briefs/).
 

Reference

1. Ganos E, Steinberg K, Seidman J, Masi D, Gomez-Rexode A, Fraser A. Talking About Costs: Innovation In Clinician-Patient Conversations. Health Affairs. Published online Nov. 27, 2018. doi: 10.1377/hblog20181126.366161. Accessed Dec. 11, 2018.

Seventy percent of Americans would like to have conversations about the costs of care with their health care providers, but only 28% do so, according to polling conducted for the Robert Wood Johnson Foundation (RWJF) by Avalere Health.

utah778/Thinkstock

With those polling results in hand, 2 years ago RWJF and Avalere Health launched the Cost Conversation projects to help. Practice briefs for these kinds of conversations are now available on America’s Essential Hospitals’ website (https://essentialhospitals.org/cost-care/practice-briefs/).
 

Reference

1. Ganos E, Steinberg K, Seidman J, Masi D, Gomez-Rexode A, Fraser A. Talking About Costs: Innovation In Clinician-Patient Conversations. Health Affairs. Published online Nov. 27, 2018. doi: 10.1377/hblog20181126.366161. Accessed Dec. 11, 2018.

Many performance improvement programs try to create a higher value health system by incentivizing physicians and health systems to behave in particular ways. These have often been pay-for-performance programs that offer bonuses or impose penalties depending on how providers perform on various metrics.

“In theory, this makes sense,” said Dhruv Khullar, MD, MPP, lead author of a JAMA article about the future of incentives, and assistant professor at Weill Cornell Medicine in New York. “But in practice, these programs have not been successful in consistently improving quality, and sometimes they have been counterproductive. In our article, we argued that focusing too narrowly on financial rewards is not the right strategy to improve health system performance – and is sometimes at odds with the physician professionalism and what really motivates most clinicians.”

Pay-for-performance programs suffer from several fundamental flaws: they focus too narrowly on financial incentives and use centralized accountability instead of local culture, for example, Dr. Khullar said.

“A better future state would involve capitalizing on physician professionalism through nonfinancial rewards, resources for quality improvement, team-based assessments, and emphasizing continuous learning and organizational culture,” he noted. Performance programs would take a more global view of clinical care by emphasizing culture, teams, trust, and learning. Such a system would allow hospitalists and other physicians to worry less about meeting specific metrics and focus more on providing high-quality care to their patients.

“I would hope physicians, payers, and administrators would reconsider some previously held beliefs about quality improvement, especially the idea that better quality requires giving people bonus payments or imposing financial penalties,” Dr. Khullar said. “We believe the next wave of performance improvement programs should entertain other paths to better quality, which are more in line with human motivation and physician professionalism.”
 

Reference

1. Khullar D, Wolfson D, Casalino LP. Professionalism, Performance, and the Future of Physician Incentives. JAMA. 2018 Nov 26 (Epub ahead of print). doi: 10.1001/jama.2018.17719. Accessed Dec. 11, 2018.

Many performance improvement programs try to create a higher value health system by incentivizing physicians and health systems to behave in particular ways. These have often been pay-for-performance programs that offer bonuses or impose penalties depending on how providers perform on various metrics.

“In theory, this makes sense,” said Dhruv Khullar, MD, MPP, lead author of a JAMA article about the future of incentives, and assistant professor at Weill Cornell Medicine in New York. “But in practice, these programs have not been successful in consistently improving quality, and sometimes they have been counterproductive. In our article, we argued that focusing too narrowly on financial rewards is not the right strategy to improve health system performance – and is sometimes at odds with the physician professionalism and what really motivates most clinicians.”

Pay-for-performance programs suffer from several fundamental flaws: they focus too narrowly on financial incentives and use centralized accountability instead of local culture, for example, Dr. Khullar said.

“A better future state would involve capitalizing on physician professionalism through nonfinancial rewards, resources for quality improvement, team-based assessments, and emphasizing continuous learning and organizational culture,” he noted. Performance programs would take a more global view of clinical care by emphasizing culture, teams, trust, and learning. Such a system would allow hospitalists and other physicians to worry less about meeting specific metrics and focus more on providing high-quality care to their patients.

“I would hope physicians, payers, and administrators would reconsider some previously held beliefs about quality improvement, especially the idea that better quality requires giving people bonus payments or imposing financial penalties,” Dr. Khullar said. “We believe the next wave of performance improvement programs should entertain other paths to better quality, which are more in line with human motivation and physician professionalism.”
 

Reference

1. Khullar D, Wolfson D, Casalino LP. Professionalism, Performance, and the Future of Physician Incentives. JAMA. 2018 Nov 26 (Epub ahead of print). doi: 10.1001/jama.2018.17719. Accessed Dec. 11, 2018.

Many performance improvement programs try to create a higher value health system by incentivizing physicians and health systems to behave in particular ways. These have often been pay-for-performance programs that offer bonuses or impose penalties depending on how providers perform on various metrics.

“In theory, this makes sense,” said Dhruv Khullar, MD, MPP, lead author of a JAMA article about the future of incentives, and assistant professor at Weill Cornell Medicine in New York. “But in practice, these programs have not been successful in consistently improving quality, and sometimes they have been counterproductive. In our article, we argued that focusing too narrowly on financial rewards is not the right strategy to improve health system performance – and is sometimes at odds with the physician professionalism and what really motivates most clinicians.”

Pay-for-performance programs suffer from several fundamental flaws: they focus too narrowly on financial incentives and use centralized accountability instead of local culture, for example, Dr. Khullar said.

“A better future state would involve capitalizing on physician professionalism through nonfinancial rewards, resources for quality improvement, team-based assessments, and emphasizing continuous learning and organizational culture,” he noted. Performance programs would take a more global view of clinical care by emphasizing culture, teams, trust, and learning. Such a system would allow hospitalists and other physicians to worry less about meeting specific metrics and focus more on providing high-quality care to their patients.

“I would hope physicians, payers, and administrators would reconsider some previously held beliefs about quality improvement, especially the idea that better quality requires giving people bonus payments or imposing financial penalties,” Dr. Khullar said. “We believe the next wave of performance improvement programs should entertain other paths to better quality, which are more in line with human motivation and physician professionalism.”
 

Reference

1. Khullar D, Wolfson D, Casalino LP. Professionalism, Performance, and the Future of Physician Incentives. JAMA. 2018 Nov 26 (Epub ahead of print). doi: 10.1001/jama.2018.17719. Accessed Dec. 11, 2018.