The T1D Exchange Quality Improvement Collaborative (T1DX-QI) is a framework used to systematically improve systems and processes. It was established in 2016 and has continued to evolve throughout the past 6 years (Figure 1). In health care settings, the T1DX-QI allows health care professionals, patients with type 1 diabetes, and families to be part of a process that evaluates and improves many areas of health care for everyone who touches the health care system (1). Learning health systems (LHSs) are systems in which “science, informatics, incentives, and culture are aligned for continuous improvement and innovation” (2). T1DX-QI is an LHS that is real-world data–enabled and now includes more than 45 pediatric and adult sites (Figure 2 and Supplementary Table S1). The T1DX-QI special article collection in this issue of Clinical Diabetes highlights T1DX-QI’s continued expansion and contributions to diabetes care.
In the first article of the series, Marks et al. (3) note that few LHSs are focused on quality improvement (QI). As T1DX-QI expands as an LHS, it is important to support a successful environment for development, growth, and collaboration for new sites that join the collaborative. Toward that end, T1DX-QI developed a self-assessment questionnaire (3) to evaluate each site’s report of current QI capabilities at the time of joining T1DX-QI. This questionnaire is specifically focused on the central pillars of successful QI efforts. These data have provided insights into the diversity of QI cultures among sites, highlighting areas of success and areas of continued focus as T1DX-QI continues to expand (3).
One feature identified as essential in an LHS is data standardization across all sites, to ensure uniform data collection. Mungmode et al. (4) explore the design and implementation of a data platform, the QI Portal, that ensures data standardization across all T1DX-QI sites. The QI Portal addresses many LHS challenges previously described in the literature (2). Having standardized data across all sites allows for comparative data collection and analysis across a large population and evaluation of outcomes that can lead to real-world, real-time changes.
The remaining three articles in this collection highlight examples of how a QI LHS can draw on population data to gain a greater understanding of real-world clinical practices and barriers to care.
Smart insulin pen (SIP) technology provides an option for individuals with diabetes who are not interested in or want to take a break from using an insulin pump but who still want similar benefits, including dose calculation assistance, dose tracking, and dose reminders (5). However, challenges exist in use of SIPs. Ospelt et al. (6) looked into the benefits and facilitators of SIP use across T1DX-QI sites. SIPs are a viable alternative option to insulin pump technology that can provide patients with benefits in dosing and monitoring and provide health care professionals with improved data reports to aid dose adjustments compared with the use of traditional insulin pens. However, this study shows that barriers in access and training must be overcome to expand the use of SIPs in clinical practice.
Individuals with type 1 diabetes may struggle with diabetes management at various times throughout their lives because of physiologic and psychosocial changes (7). Identifying factors associated with optimal diabetes management can provide opportunities for health care teams to implement real-time changes for improvement. Demeterco-Berggren et al. (8) found that youth with optimal glycemic management were more likely to be non-Hispanic White, have private insurance, and screen negative for depression or anxiety. Akturk et al. (9) found that adults with optimal management were more likely to be older and non-Hispanic White, have private health insurance, and screen negative for depression. Both studies highlight that greater efforts and interventions are needed to reduce disparities. These studies also highlight the importance of screening for and treating mental health issues. Increased efforts to train, hire, and integrate mental health specialists in clinics treating patients with diabetes are vital to improve diabetes management (7).
Demeterco-Berggren et al. (8) and Akturk et al. (9) also found that technology use was associated with improved glycemic management. These studies demonstrate that disparities in technology exacerbate the health disparities seen in diabetes care, including in glycemic management and adverse outcomes. T1DX-QI is currently undertaking initiatives to improve health inequities through several routes, including increasing proper data identification for mapping, measuring implicit bias in institutions, and engaging community leaders and clinics to address health inequities (10,11).
The articles in this T1DX-QI collection highlight the importance of having a QI LHS that can lead to improvement within institutions. The ability to collect and analyze data in a standard, uniform method allows for population studies to evaluate real-world outcomes, with the ultimate purpose of implementing real-time changes in clinical practices to improve patient care.
T1DX-QI has continued to grow yearly since its inception; there are now more than 45 pediatric and adult sites in the collaborative, with an anticipated goal to expand to more than 60 sites during the next 3 years. The integration of additional sites that can bring unique and varying perspectives in diabetes care is needed to further expand the T1DX-QI to serve a growing population of people with diabetes.
The authors thank the Leona M. and Harry B. Helmsley Charitable Trust for funding the T1DX-QI. The authors also acknowledge the contributions of patients, families, diabetes care teams, and collaborators within T1DX-QI, who continually seek to improve care and outcomes for people with diabetes.
Duality of Interest
S.A. is a health care disparities advisor for Medtronic and Beta Bionics. O.E. is a member of the Medtronic Diabetes Health Equity Advisory Board, for which his organization, the T1D Exchange, is compensated; he is also a principal investigator on investigator-initiated research projects funded by Abbott, Dexcom, Eli Lilly, Medtronic Diabetes, and Vertex. No other potential conflicts of interest relevant to this article were reported.
All of the authors wrote and reviewed/edited the manuscript. N.R. and O.E. are the guarantors of this work and take responsibility for the integrity of this commentary.
This article contains supplementary material online at https://doi.org/10.2337/figshare.21096403.
This article is part of a special article collection available at https://diabetesjournals.org/collection/1507/Leveraging-Real-World-Data-for-Quality-Improvement.