Nicole Rioles, MA

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From T1D Exchange, Boston, MA (Dr. Ebekozien, Dr. Odugbesan, and Nicole Rioles); Barbara Davis Center, University of Colorado, Boulder, CO (Dr. Majidi); Cincinnati Children’s Hospital Medical Center, Cincinnati, OH (Dr. Jones); and Nationwide Children’s Hospital, Columbus, OH (Dr. Kamboj)

Health equity has been described as the opportunity for all persons to obtain their highest level of health possible.¹ Unfortunately, even with advances in technology and care practices, disparities persist in health care outcomes. Disparities in prevalence, prognosis, and outcomes still exist in diabetes management.² Non-Hispanic Black and/or Hispanic populations are more likely to have worse glycemic control,^3,4 to encounter more barriers in access to care,⁵ and to have higher levels of acute complications,⁴ and to use advanced technologies less frequently.⁴ Diabetes is one of the preexisting conditions that increase morbidity and mortality in COVID-19.^6,7 Unfortunately, adverse outcomes from COVID-19 also disproportionately impact a specific vulnerable population.^8,9 The urgent transition to managing diabetes remotely during the COVID-19 pandemic may exacerbate long-term inequities because some vulnerable patients might not have access to technology devices necessary for effective remote management.

Here, we describe how quality improvement (QI) tools and principles can be adapted into a framework for advancing health equity. Specifically, we describe a 10-step framework that may be applied in diabetes care management to achieve improvement, using a hypothetical example of increasing use of continuous glucose monitors (CGMs) among patients with type 1 diabetes mellitus.¹⁰ This framework was developed to address the literature gap on practical ways health care providers can address inequities using QI principles, and was implemented by 1 of the authors at a local public health department.¹¹ The framework’s iterative and comprehensive design makes it ideal for addressing inequities in chronic diseases like diabetes, which have multiple root causes with no easy solutions. The improvement program pilot received a national model practice award.^11,12

10-Step Framework

Step 1: Review program/project baseline data for existing disparities. Diabetes programs and routine QI processes encourage existing data review to determine how effective the current system is working and if the existing process has a predictable pattern.^13,14 Our equity-revised framework proposes a more in-depth review to stratify baseline data based on factors that might contribute to inequities, including race, age, income levels, ethnicity, language, sexual orientation, insurance type, and zip code. This process will identify patients not served or unfairly impacted due to socioeconomic factors. For example, using the hypothetical example of improving CGM use, a team completes a preliminary data review and determines that baseline CGM use is 30% in the clinic population. However, in a review to assess for disparities, they also identify that patients on public insurance have a significantly lower CGM uptake of only 15%.

Step 2: Build an equitable project team, including patients with lived experiences. Routine projects typically have clinicians, administrative staff, and analytic staff as members of their team. In a post-COVID-19 world, every team needs to learn directly from people impacted and share decision-making power. The traditional approach to receiving feedback has generally been to collect responses using surveys or focus groups. We propose that individuals/families who are disproportionately impacted be included as active members on QI teams. For example, in the hypothetical example of the CGM project, team members would include patients with type 1 diabetes who are on public insurance and their families.

Step 3: Develop equity-focused goals. The traditional program involves the development of aims that are SMART (specific, measurable, achievable, realistic, time-bound).¹⁵ The proposed framework encourages the inclusion of equity-revised goals (SMARTer) using insights from Steps 1 and 2. For example, your typical smart goal might be to increase the percentage of patients using CGM by 20% in 6 months, while a SMARTer goal would be to increase the proportion of patients using CGM by 20% and reduce the disparities among public and private insurance patients by 30% in 6 months.

Step 4: Identify inequitable processes/pathways. Traditional QI programs might use a process map or flow diagram to depict the current state of a process visually.¹⁶ For example, in Figure 1, the process map diagram depicts some differences in the process for patients with public insurance as opposed to those with private insurance. The framework also advocates for using visual tools like process maps to depict how there might be inequitable pathways in a system. Visually identifying inequitable processes/pathways can help a team see barriers, address challenges, and pilot innovative solutions.

Step 5: Identify how socioeconomic factors are contributing to the current outcome. A good understanding of factors that contribute to the problem is an essential part of finding fundamental solutions. The fishbone diagram¹⁶ is a visualization tool used to identify contributing factors. When investigating contributing factors, it is commonplace to identify factors that fit into 1 of 5 categories: people, process, place, product, and policies (5 Ps). An equity-focused process will include equity as a new major factor category, and the socioeconomic impacts that contribute to inequities will be brainstormed and visually represented. For example, in the hypothetical CGM improvement example, an equity contributing factor is extensive CGM application paperwork for patients on public insurance as compared to those on private insurance. Figure 2 shows equity integrated into a fishbone diagram.

Step 6: Brainstorm possible improvements. Potential improvement ideas for the hypothetical CGM example might include redesigning the existing workflow, piloting CGM educational classes, and using a CGM barrier assessment tool to identify and address barriers to adoption.

Step 7: Use the decision matrix with equity as a criterion to prioritize improvement ideas. Decision matrix¹⁵ is a great tool that is frequently used to help teams prioritize potential ideas. Project team members must decide what criteria are important in prioritizing ideas to implement. Common criteria include implementation cost, time, and resources, but in addition to the common criteria, the team can specify ”impact on equity” as one of their criteria, alongside other standard criteria like impact.

Step 8: Test one small change at a time. This step is consistent with other traditional improvement models using the Plan, Do, Study, Act (PDSA) model for improvement.¹⁷ During this phase, the team should make predictions on the expected impact of the intervention on outcomes. For example, in the hypothetical example, the team predicts that testing and expanding CGM classes will reduce disparities among public versus private health insurance users by 5% and increase overall CGM uptake by 10%.

Step 9: Measure and compare results with predictions to identify inequitable practices or consequences. After each test of change, the team should review the results, including implementation cost considerations, and compare them to the predictions in the earlier step. The team should also document the potential reasons why their predictions were correct or inaccurate, and whether there were any unforeseen outcomes from the intervention.

Step 10: Celebrate small wins and repeat the process. Making fundamental and equitable changes takes time. This framework aimed at undoing inequities, particularly those inequities that have been amplified by the COVID-19 pandemic, is iterative and ongoing.^18,19 Not every test of change will impact the outcome or reduce inequity, but over time, each change will impact the next, generating sustainable effects.

Conclusion

There are ongoing studies examining the adverse outcomes and potential health inequities for patients with diabetes impacted by COVID-19.²⁰ Health care providers need to plan for post-COVID-19 care, keeping in mind that the pandemic might worsen already existing health disparities in diabetes management.^3,4,21 This work will involve an intentional approach to address structural and systemic racism.²² Therefore, the work of building health equity solutions must be rooted in racial justice, economic equity, and equitable access to health care and education.

Initiatives like this are currently being funded through foundation grants as well as state and federal research or program grants. Regional and national payors, including the Centers for Medicare & Medicaid Services, are currently piloting long-term sustainable funding models through programs like accountable care organizations and the Accountable Health Communities Model.²³

Health systems can successfully address health equity and racial justice, using a framework as described above, to identify determinants of health, develop policies to expand access to care for the most vulnerable patients, distribute decision-making power, and train staff by naming structural racism as a driver of health inequities.

Acknowledgment: The authors acknowledge the contributions of patients, families, diabetes care teams, and collaborators within the T1D Exchange Quality Improvement Collaborative, who continually seek to improve care and outcomes for people living with diabetes.

Corresponding author: Osagie Ebekozien, MD, 11 Avenue De La Fayette, Boston, MA 02115; [email protected].

Financial disclosures: None.

Funding: T1D Exchange QI Collaborative is funded by The Leona M. and Harry B. Helmsley Charitable Trust. No specific funding was received for this manuscript or the development of this framework.

Keywords: type 1 diabetes; quality improvement; QI framework; racial justice; health disparities.

From T1D Exchange, Boston, MA (Dr. Ebekozien, Dr. Odugbesan, and Nicole Rioles); Barbara Davis Center, University of Colorado, Boulder, CO (Dr. Majidi); Cincinnati Children’s Hospital Medical Center, Cincinnati, OH (Dr. Jones); and Nationwide Children’s Hospital, Columbus, OH (Dr. Kamboj)

Health equity has been described as the opportunity for all persons to obtain their highest level of health possible.¹ Unfortunately, even with advances in technology and care practices, disparities persist in health care outcomes. Disparities in prevalence, prognosis, and outcomes still exist in diabetes management.² Non-Hispanic Black and/or Hispanic populations are more likely to have worse glycemic control,^3,4 to encounter more barriers in access to care,⁵ and to have higher levels of acute complications,⁴ and to use advanced technologies less frequently.⁴ Diabetes is one of the preexisting conditions that increase morbidity and mortality in COVID-19.^6,7 Unfortunately, adverse outcomes from COVID-19 also disproportionately impact a specific vulnerable population.^8,9 The urgent transition to managing diabetes remotely during the COVID-19 pandemic may exacerbate long-term inequities because some vulnerable patients might not have access to technology devices necessary for effective remote management.

Here, we describe how quality improvement (QI) tools and principles can be adapted into a framework for advancing health equity. Specifically, we describe a 10-step framework that may be applied in diabetes care management to achieve improvement, using a hypothetical example of increasing use of continuous glucose monitors (CGMs) among patients with type 1 diabetes mellitus.¹⁰ This framework was developed to address the literature gap on practical ways health care providers can address inequities using QI principles, and was implemented by 1 of the authors at a local public health department.¹¹ The framework’s iterative and comprehensive design makes it ideal for addressing inequities in chronic diseases like diabetes, which have multiple root causes with no easy solutions. The improvement program pilot received a national model practice award.^11,12

10-Step Framework

Step 1: Review program/project baseline data for existing disparities. Diabetes programs and routine QI processes encourage existing data review to determine how effective the current system is working and if the existing process has a predictable pattern.^13,14 Our equity-revised framework proposes a more in-depth review to stratify baseline data based on factors that might contribute to inequities, including race, age, income levels, ethnicity, language, sexual orientation, insurance type, and zip code. This process will identify patients not served or unfairly impacted due to socioeconomic factors. For example, using the hypothetical example of improving CGM use, a team completes a preliminary data review and determines that baseline CGM use is 30% in the clinic population. However, in a review to assess for disparities, they also identify that patients on public insurance have a significantly lower CGM uptake of only 15%.

Step 2: Build an equitable project team, including patients with lived experiences. Routine projects typically have clinicians, administrative staff, and analytic staff as members of their team. In a post-COVID-19 world, every team needs to learn directly from people impacted and share decision-making power. The traditional approach to receiving feedback has generally been to collect responses using surveys or focus groups. We propose that individuals/families who are disproportionately impacted be included as active members on QI teams. For example, in the hypothetical example of the CGM project, team members would include patients with type 1 diabetes who are on public insurance and their families.

Step 3: Develop equity-focused goals. The traditional program involves the development of aims that are SMART (specific, measurable, achievable, realistic, time-bound).¹⁵ The proposed framework encourages the inclusion of equity-revised goals (SMARTer) using insights from Steps 1 and 2. For example, your typical smart goal might be to increase the percentage of patients using CGM by 20% in 6 months, while a SMARTer goal would be to increase the proportion of patients using CGM by 20% and reduce the disparities among public and private insurance patients by 30% in 6 months.

Step 4: Identify inequitable processes/pathways. Traditional QI programs might use a process map or flow diagram to depict the current state of a process visually.¹⁶ For example, in Figure 1, the process map diagram depicts some differences in the process for patients with public insurance as opposed to those with private insurance. The framework also advocates for using visual tools like process maps to depict how there might be inequitable pathways in a system. Visually identifying inequitable processes/pathways can help a team see barriers, address challenges, and pilot innovative solutions.

Step 5: Identify how socioeconomic factors are contributing to the current outcome. A good understanding of factors that contribute to the problem is an essential part of finding fundamental solutions. The fishbone diagram¹⁶ is a visualization tool used to identify contributing factors. When investigating contributing factors, it is commonplace to identify factors that fit into 1 of 5 categories: people, process, place, product, and policies (5 Ps). An equity-focused process will include equity as a new major factor category, and the socioeconomic impacts that contribute to inequities will be brainstormed and visually represented. For example, in the hypothetical CGM improvement example, an equity contributing factor is extensive CGM application paperwork for patients on public insurance as compared to those on private insurance. Figure 2 shows equity integrated into a fishbone diagram.

Step 6: Brainstorm possible improvements. Potential improvement ideas for the hypothetical CGM example might include redesigning the existing workflow, piloting CGM educational classes, and using a CGM barrier assessment tool to identify and address barriers to adoption.

Step 7: Use the decision matrix with equity as a criterion to prioritize improvement ideas. Decision matrix¹⁵ is a great tool that is frequently used to help teams prioritize potential ideas. Project team members must decide what criteria are important in prioritizing ideas to implement. Common criteria include implementation cost, time, and resources, but in addition to the common criteria, the team can specify ”impact on equity” as one of their criteria, alongside other standard criteria like impact.

Step 8: Test one small change at a time. This step is consistent with other traditional improvement models using the Plan, Do, Study, Act (PDSA) model for improvement.¹⁷ During this phase, the team should make predictions on the expected impact of the intervention on outcomes. For example, in the hypothetical example, the team predicts that testing and expanding CGM classes will reduce disparities among public versus private health insurance users by 5% and increase overall CGM uptake by 10%.

Step 9: Measure and compare results with predictions to identify inequitable practices or consequences. After each test of change, the team should review the results, including implementation cost considerations, and compare them to the predictions in the earlier step. The team should also document the potential reasons why their predictions were correct or inaccurate, and whether there were any unforeseen outcomes from the intervention.

Step 10: Celebrate small wins and repeat the process. Making fundamental and equitable changes takes time. This framework aimed at undoing inequities, particularly those inequities that have been amplified by the COVID-19 pandemic, is iterative and ongoing.^18,19 Not every test of change will impact the outcome or reduce inequity, but over time, each change will impact the next, generating sustainable effects.

Conclusion

There are ongoing studies examining the adverse outcomes and potential health inequities for patients with diabetes impacted by COVID-19.²⁰ Health care providers need to plan for post-COVID-19 care, keeping in mind that the pandemic might worsen already existing health disparities in diabetes management.^3,4,21 This work will involve an intentional approach to address structural and systemic racism.²² Therefore, the work of building health equity solutions must be rooted in racial justice, economic equity, and equitable access to health care and education.

Initiatives like this are currently being funded through foundation grants as well as state and federal research or program grants. Regional and national payors, including the Centers for Medicare & Medicaid Services, are currently piloting long-term sustainable funding models through programs like accountable care organizations and the Accountable Health Communities Model.²³

Health systems can successfully address health equity and racial justice, using a framework as described above, to identify determinants of health, develop policies to expand access to care for the most vulnerable patients, distribute decision-making power, and train staff by naming structural racism as a driver of health inequities.

Acknowledgment: The authors acknowledge the contributions of patients, families, diabetes care teams, and collaborators within the T1D Exchange Quality Improvement Collaborative, who continually seek to improve care and outcomes for people living with diabetes.

Corresponding author: Osagie Ebekozien, MD, 11 Avenue De La Fayette, Boston, MA 02115; [email protected].

Financial disclosures: None.

Funding: T1D Exchange QI Collaborative is funded by The Leona M. and Harry B. Helmsley Charitable Trust. No specific funding was received for this manuscript or the development of this framework.

Keywords: type 1 diabetes; quality improvement; QI framework; racial justice; health disparities.

From T1D Exchange, Boston, MA (Dr. Ebekozien, Dr. Odugbesan, and Nicole Rioles); Barbara Davis Center, University of Colorado, Boulder, CO (Dr. Majidi); Cincinnati Children’s Hospital Medical Center, Cincinnati, OH (Dr. Jones); and Nationwide Children’s Hospital, Columbus, OH (Dr. Kamboj)

Health equity has been described as the opportunity for all persons to obtain their highest level of health possible.¹ Unfortunately, even with advances in technology and care practices, disparities persist in health care outcomes. Disparities in prevalence, prognosis, and outcomes still exist in diabetes management.² Non-Hispanic Black and/or Hispanic populations are more likely to have worse glycemic control,^3,4 to encounter more barriers in access to care,⁵ and to have higher levels of acute complications,⁴ and to use advanced technologies less frequently.⁴ Diabetes is one of the preexisting conditions that increase morbidity and mortality in COVID-19.^6,7 Unfortunately, adverse outcomes from COVID-19 also disproportionately impact a specific vulnerable population.^8,9 The urgent transition to managing diabetes remotely during the COVID-19 pandemic may exacerbate long-term inequities because some vulnerable patients might not have access to technology devices necessary for effective remote management.

Here, we describe how quality improvement (QI) tools and principles can be adapted into a framework for advancing health equity. Specifically, we describe a 10-step framework that may be applied in diabetes care management to achieve improvement, using a hypothetical example of increasing use of continuous glucose monitors (CGMs) among patients with type 1 diabetes mellitus.¹⁰ This framework was developed to address the literature gap on practical ways health care providers can address inequities using QI principles, and was implemented by 1 of the authors at a local public health department.¹¹ The framework’s iterative and comprehensive design makes it ideal for addressing inequities in chronic diseases like diabetes, which have multiple root causes with no easy solutions. The improvement program pilot received a national model practice award.^11,12

10-Step Framework

Step 1: Review program/project baseline data for existing disparities. Diabetes programs and routine QI processes encourage existing data review to determine how effective the current system is working and if the existing process has a predictable pattern.^13,14 Our equity-revised framework proposes a more in-depth review to stratify baseline data based on factors that might contribute to inequities, including race, age, income levels, ethnicity, language, sexual orientation, insurance type, and zip code. This process will identify patients not served or unfairly impacted due to socioeconomic factors. For example, using the hypothetical example of improving CGM use, a team completes a preliminary data review and determines that baseline CGM use is 30% in the clinic population. However, in a review to assess for disparities, they also identify that patients on public insurance have a significantly lower CGM uptake of only 15%.

Step 2: Build an equitable project team, including patients with lived experiences. Routine projects typically have clinicians, administrative staff, and analytic staff as members of their team. In a post-COVID-19 world, every team needs to learn directly from people impacted and share decision-making power. The traditional approach to receiving feedback has generally been to collect responses using surveys or focus groups. We propose that individuals/families who are disproportionately impacted be included as active members on QI teams. For example, in the hypothetical example of the CGM project, team members would include patients with type 1 diabetes who are on public insurance and their families.

Step 3: Develop equity-focused goals. The traditional program involves the development of aims that are SMART (specific, measurable, achievable, realistic, time-bound).¹⁵ The proposed framework encourages the inclusion of equity-revised goals (SMARTer) using insights from Steps 1 and 2. For example, your typical smart goal might be to increase the percentage of patients using CGM by 20% in 6 months, while a SMARTer goal would be to increase the proportion of patients using CGM by 20% and reduce the disparities among public and private insurance patients by 30% in 6 months.

Step 4: Identify inequitable processes/pathways. Traditional QI programs might use a process map or flow diagram to depict the current state of a process visually.¹⁶ For example, in Figure 1, the process map diagram depicts some differences in the process for patients with public insurance as opposed to those with private insurance. The framework also advocates for using visual tools like process maps to depict how there might be inequitable pathways in a system. Visually identifying inequitable processes/pathways can help a team see barriers, address challenges, and pilot innovative solutions.

Step 5: Identify how socioeconomic factors are contributing to the current outcome. A good understanding of factors that contribute to the problem is an essential part of finding fundamental solutions. The fishbone diagram¹⁶ is a visualization tool used to identify contributing factors. When investigating contributing factors, it is commonplace to identify factors that fit into 1 of 5 categories: people, process, place, product, and policies (5 Ps). An equity-focused process will include equity as a new major factor category, and the socioeconomic impacts that contribute to inequities will be brainstormed and visually represented. For example, in the hypothetical CGM improvement example, an equity contributing factor is extensive CGM application paperwork for patients on public insurance as compared to those on private insurance. Figure 2 shows equity integrated into a fishbone diagram.

Step 6: Brainstorm possible improvements. Potential improvement ideas for the hypothetical CGM example might include redesigning the existing workflow, piloting CGM educational classes, and using a CGM barrier assessment tool to identify and address barriers to adoption.

Step 7: Use the decision matrix with equity as a criterion to prioritize improvement ideas. Decision matrix¹⁵ is a great tool that is frequently used to help teams prioritize potential ideas. Project team members must decide what criteria are important in prioritizing ideas to implement. Common criteria include implementation cost, time, and resources, but in addition to the common criteria, the team can specify ”impact on equity” as one of their criteria, alongside other standard criteria like impact.

Step 8: Test one small change at a time. This step is consistent with other traditional improvement models using the Plan, Do, Study, Act (PDSA) model for improvement.¹⁷ During this phase, the team should make predictions on the expected impact of the intervention on outcomes. For example, in the hypothetical example, the team predicts that testing and expanding CGM classes will reduce disparities among public versus private health insurance users by 5% and increase overall CGM uptake by 10%.

Step 9: Measure and compare results with predictions to identify inequitable practices or consequences. After each test of change, the team should review the results, including implementation cost considerations, and compare them to the predictions in the earlier step. The team should also document the potential reasons why their predictions were correct or inaccurate, and whether there were any unforeseen outcomes from the intervention.

Step 10: Celebrate small wins and repeat the process. Making fundamental and equitable changes takes time. This framework aimed at undoing inequities, particularly those inequities that have been amplified by the COVID-19 pandemic, is iterative and ongoing.^18,19 Not every test of change will impact the outcome or reduce inequity, but over time, each change will impact the next, generating sustainable effects.

Conclusion

There are ongoing studies examining the adverse outcomes and potential health inequities for patients with diabetes impacted by COVID-19.²⁰ Health care providers need to plan for post-COVID-19 care, keeping in mind that the pandemic might worsen already existing health disparities in diabetes management.^3,4,21 This work will involve an intentional approach to address structural and systemic racism.²² Therefore, the work of building health equity solutions must be rooted in racial justice, economic equity, and equitable access to health care and education.

Initiatives like this are currently being funded through foundation grants as well as state and federal research or program grants. Regional and national payors, including the Centers for Medicare & Medicaid Services, are currently piloting long-term sustainable funding models through programs like accountable care organizations and the Accountable Health Communities Model.²³

Health systems can successfully address health equity and racial justice, using a framework as described above, to identify determinants of health, develop policies to expand access to care for the most vulnerable patients, distribute decision-making power, and train staff by naming structural racism as a driver of health inequities.

Acknowledgment: The authors acknowledge the contributions of patients, families, diabetes care teams, and collaborators within the T1D Exchange Quality Improvement Collaborative, who continually seek to improve care and outcomes for people living with diabetes.

Corresponding author: Osagie Ebekozien, MD, 11 Avenue De La Fayette, Boston, MA 02115; [email protected].

Financial disclosures: None.

Funding: T1D Exchange QI Collaborative is funded by The Leona M. and Harry B. Helmsley Charitable Trust. No specific funding was received for this manuscript or the development of this framework.

Keywords: type 1 diabetes; quality improvement; QI framework; racial justice; health disparities.