Diabetes Quality Improvement Research Articles

Equity-Focused Evaluation of a Medicaid-Funded Statewide Diabetes Quality Improvement Project Collaborative.

To evaluate the Ohio Diabetes Quality Improvement Project (QIP) equity aim to reduce the percentage of Non-Hispanic Black (NHB) and Hispanic patients with A1C >9% by ≥20% over 2 years. The Ohio Department of Medicaid, Ohio Colleges of Medicine Government Resource Center, Ohio Medicaid managed care plans, and seven medical schools in Ohio formed the Diabetes QIP collaborative using the collective impact model to improve diabetes outcomes and equity in 20 practices across 11 health systems. The quality improvement (QI) strategies included data audit and feedback, peer-to-peer learning, QI coaching/practice facilitation, and subject matter expert consultation through coaching calls, monthly webinars, and annual virtual learning sessions. Electronic health record data were collected for preintervention (2019-2020) and intervention (2020-2022) periods. Assessments of improvements in A1C were based on prevalence of A1C >9% from preintervention, year 1, and year 2 with stratification by race and ethnicity. The Diabetes QIP included 7,689 (54% female) sociodemographically diverse patients, self-identifying as non-Hispanic White (NHW) (42%), NHB (43%), Hispanic (8%), non-Hispanic Asian (4%), or other (3%). In year 2 compared with baseline, there were decreases in the proportion of patients with A1C >9% among NHW, NHB, and Hispanic patients (NHW from 19% to 12% [37% reduction], NHB 23% to 18% [22% reduction], and Hispanic 29% to 23% [20% reduction]). The Ohio Diabetes QIP, focused on multisector collaborative approaches, reduced the percentage of patients with A1C >9% by ≥20% among NHW, NHB, and Hispanic populations. Given the persistence of disparities, further equity-focused refinements are warranted to address disparities in diabetes control.

A Medicaid-Funded Statewide Diabetes Quality Improvement Collaborative: Ohio 2020‒2022.

We used a collective impact model to form a statewide diabetes quality improvement collaborative to improve diabetes outcomes and advance diabetes health equity. Between 2020 and 2022, in collaboration with the Ohio Department of Medicaid, Medicaid Managed Care Plans, and Ohio's seven medical schools, we recruited 20 primary care practices across the state. The percentage of patients with hemoglobin A1c greater than 9% improved from 25% to 20% over two years. Applying our model more broadly could accelerate improvement in diabetes outcomes. (Am J Public Health. 2023;113(12):1254-1257. https://doi.org/10.2105/AJPH.2023.307410).

Evidence Synthesis for Complex Interventions Using Meta-Regression Models.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.

Baseline Quality Improvement Capacity of 33 Endocrinology Centers Participating in the T1D Exchange Quality Improvement Collaborative.

This article describes the evolution of the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI) and provides insight into the development and growth of a successful type 1 diabetes quality improvement (QI) program. Since its inception 8 years ago, the collaborative has expanded to include centers across the United States with varying levels of QI experience, while simultaneously achieving many tangible improvements in type 1 diabetes care. These successes underscore the importance of learning health systems, data-sharing, benchmarking, and peer collaboration as drivers for continuous QI. Future efforts will include recruiting additional small- to medium-sized centers focused on adult care and underserved communities to further the goal of improving care and outcomes for all people living with type 1 diabetes.

Stakeholder Perspectives regarding the Acceptability and Sustainability of a Multi-component Diabetes Care Strategy in South Asia: a longitudinal qualitative analysis.

South Asia has the highest worldwide burden of diabetes, yet little is known about how to promote the sustainability of diabetes quality improvement care models in South Asia and how these approaches are viewed by health service users. The present study evaluates how organizational features influence sustainability of the CARRS Trial intervention and assesses patient’s acceptability of the care model. In-depth interviews with clinical stakeholders were conducted pre-implementation (physicians, n = 19), during late-implementation, after 2.5 years of supported intervention (physicians, n = 9), and two years post-implementation (care coordinators, n = 3; patients, n = 34). The consolidated framework for implementation research (CFIR) was used to code transcriptions and conduct a thematic analysis. Prior to implementation, physicians perceived the care model as an opportunity to enhance the quality of diabetes care. By late-implementation, physicians reported alignment between intervention care components and clinics’ needs and work cultures. However, physicians felt that additional cost- and clinical effectiveness data would be needed to assess intervention sustainability. Patients perceived the intervention as acceptable because care coordination facilitated more responsive and efficient clinic services. Efforts to scale-up and scale-out this intervention should focus on ensuring the care components are compatible with clinics’ priority needs and identify how the care coordinator’s role will integrate into existing clinical workflows.

Improving glycaemic management in people with diabetes on maintenance haemodialysis

The Diabetes Care in Haemodialysis (DiH) working group aims to improve the care of people with diabetes mellitus on the haemodialysis unit by supporting the implementation of the 2016 JBDS guidelines. In order to support the implementation of the guideline recommendations the DiH working group have established a set of standards, developed an audit tool to demonstrate adherence to standards, and have developed educational support for haemodialysis staff both online and delivered face-to-face. We publish audit findings from five distinct haemodialysis units, highlighting the impact of the educational programme on the achievement of guideline standards. We also report the impact of the COVID-19 pandemic on diabetes care and quality improvement.

HbA1c, blood pressure, and cholesterol control in adults with diabetes: A report card for Kuwait

ABSTRACTAimTo assess the level of glycemic, blood pressure, and cholesterol control (the ‘ABCs’) nationally amongst adults with diabetes living in Kuwait.Materials and MethodsUsing data from two national cross‐sectional surveys, the levels of risk factor control were assessed in 1,801 adults with diabetes, aged 18–82 years. Glycemic control was defined as HbA1c < 7%, blood pressure control as systolic and diastolic blood pressures of <140/90 mmHg, and non‐HDL cholesterol control as <3.4 mmol/L.ResultsThe percentage of adults with diabetes achieving control was 39.2% (95% CI, 37.0–41.5) for glycemia, 58.4% (95% CI, 56.0–60.7) for blood pressure, and 28.3% (95% CI, 26.3–30.4) for non‐HDL cholesterol. The percentage of adults who were non‐smokers was 77.6% (95%, CI 75.6–79.4). The percentage of adults with diabetes achieving control on all three risk factors was 7.4% (95% CI, 6.3–8.8), and only 5.8% (95% CI, 4.8–7.0) achieved ABC control and were nonsmokers. ABC control was 30% higher in women compared with men. Non‐Kuwaitis were almost twice as likely to have uncontrolled ABC factors compared with Kuwaitis.ConclusionsOnly 1 in 13 people with diabetes in Kuwait achieved good control of glycemia, blood pressure, and cholesterol. Only 2 in 5 achieved glycemic control, 6 in 10 blood pressure control, and 2 in 7 cholesterol control. A national diabetes quality improvement program is urgently needed to improve the quality of care and to prevent long‐term complications.

912-P: Integrating CGM Use into Primary Care Practice: A Diabetes Quality Improvement Project

Background: Continuous glucose monitoring (CGM) is a powerful tool to help manage diabetes. Primary care providers (PCPs) have been slow to incorporate CGM into practice, representing a significant gap in healthcare delivery. We implemented a quality improvement intervention at our primary care clinic to increase utilization of CGM. Methods: We conducted a pre-intervention survey with PCPs to assess baseline knowledge and comfort with CGM, followed by a 6 month post-intervention survey. Our interventions included the creation of a CGM toolkit, provider education, and implementation of an office workflow for prescribing CGM and educating patients. Our primary outcome measure was the number of CGM prescriptions written. Secondary outcome measures included change in provider knowledge and comfort with CGM use, and change in hemoglobin A1c after 3-6 months of CGM use. Results: Our survey asked providers to rate their comfort and familiarity with CGM along with questions regarding barriers to CGM use. The baseline survey showed that provider familiarity and comfort explaining CGM to patients was poor, frequently rated as a 1 or 2 (1= not at all familiar, 5= very familiar) . Six months after implementation of our toolkit and education, 70% of providers responded with a 4 or 5. We identified 41 clinic patients who had CGM prescriptions in the six month time period prior to our study initiation. Six months after intervention, a total of 1patients had CGM prescriptions (146% increase) . Of the 60 patients newly prescribed CGM, we obtained retrospective data on patients. Mean hemoglobin A1c of was 9.96% at baseline and improved to 8.29% after 3-6 months of CGM use (change of 1.67%) . Conclusion: Implementation of a toolkit focusing on provider education and an organized workflow for prescribing can increase the use of CGM in primary care practice, as well as positively affect provider knowledge and comfort with CGM use. Our preliminary data indicates that patients may also benefit from improved glycemic control. Disclosure R.Mamtani: None. T.Wolinsky: Other Relationship; Pfizer Inc. B.Simon: None. Funding American Diabetes Association

Imputing intracluster correlation coefficients from a posterior predictive distribution is a feasible method of dealing with unit of analysis errors in a meta-analysis of cluster RCTs

BackgroundIncorporating cluster randomized trials (CRTs) into meta-analyses is challenging because appropriate standard errors of study estimates accounting for clustering are not always reported. Systematic reviews of CRTs often use a single constant external estimate of the intraclass correlation coefficient (ICC) to adjust study estimate standard errors and facilitate meta-analyses; an approach that fails to account for possible variation of ICCs among studies and the imprecision with which they are estimated. Using a large systematic review of the effects of diabetes quality improvement interventions, we investigated whether we could better account for ICC variation and uncertainty in meta-analyzed effect estimates by imputing missing ICCs from a posterior predictive distribution constructed from a database of relevant ICCs. MethodsWe constructed a dataset of ICC estimates from applicable studies. For outcomes with two or more available ICC estimates, we constructed posterior predictive ICC distributions in a Bayesian framework. For a selected continuous outcome, glycosylated hemoglobin (HbA1c), we compared the impact of incorporating a single constant ICC versus imputing ICCs drawn from the posterior predictive distribution when estimating the effect of intervention components on post treatment mean in a case study of diabetes quality improvement trials. ResultsUsing internal and external ICC estimates, we were able to construct a database of 59 ICCs for 12 of the 13 review outcomes (range 1–10 per outcome) and estimate the posterior predictive ICC distribution for 11 review outcomes. Synthesized results were not markedly changed by our approach for HbA1c. ConclusionBuilding posterior predictive distributions to impute missing ICCs is a feasible approach to facilitate principled meta-analyses of cluster randomized trials using prior data. Further work is needed to establish whether the application of these methods leads to improved review inferences for different reviews based on different factors (e.g., proportion of CRTs and CRTs with missing ICCs, different outcomes, variation and precision of ICCs).

Associations between attainment of incentivized primary care indicators and incident lower limb amputation among those with type 2 diabetes: a population-based historical cohort study

IntroductionEngland has invested considerably in diabetes care through such programs as the Quality and Outcomes Framework (QOF) and National Diabetes Audit (NDA). Associations between program indicators and clinical endpoints, such...

2019 Health Care & Education Presidential Address: It's All About Access!

This article is adapted from a speech Ms. Youssef delivered as President, Health Care & Education, of the American Diabetes Association at its 79th Scientific Sessions in San Francisco, CA, on 8 June 2019.Diabetes care management and prevention are national priorities with significant health and economic consequences at the patient, provider, and system levels. Gretchen Youssef, MS, RD, CDCES, program director of the MedStar Health Diabetes Institute, is a registered dietitian and diabetes care and education specialist who serves diabetes clinical, education, research, and quality improvement initiatives across the 10-hospital MedStar Health System in Washington, DC, and Maryland.Ms. Youssef began her research career with the MedStar Health Research Institute and the National Institutes of Health Diabetes Prevention Program. She then joined the MedStar Health Diabetes Institute, where she continues her interests in diabetes prevention, diabetes self-care management, delivery of community-based diabetes self-management education, examination of physical activity and food choices in urban older women, and development and validation of the "KNOW Diabetes" Survival Skills Knowledge test, which can be used to identify self-care knowledge deficits in adults with type 2 diabetes. Over the past decade, she has focused on health services research addressing real-world delivery of evidence-based type 2 diabetes care management and the changing role of the diabetes care and education specialist in diabetes care. Ms. Youssef is committed to improving lives of people with diabetes, especially those who are challenged by low health literacy and socioeconomic and mental health barriers, which often prevent them from accessing and receiving optimal care. Throughout her career she has been an active member of the American Diabetes Association.

26 - Perspectives on Sustaining, Spreading and Scaling Up of Diabetes Quality Improvement Interventions

Quality improvement (QI) programs rarely consider how their intervention can be sustained long term. A survey of authors of randomized trials of diabetes QI interventions included in an ongoing systematic review found that 78% of trials reported improved quality of care, but 40% of these trials were not sustained after study completion. This study further explores if, how and why the effective diabetes interventions were sustained, spread or scaled.

Hypertension and Diabetes Quality Improvement in a Practice Transformation Network.

The Garden Practice Transformation Network in Maryland brought together primary and specialty practices working toward value-based models and efficient care delivery. Practices were provided with coaching and live and multimedia education regarding practice transformation and quality improvement. Coaches supported practices in multipronged approaches to quality improvement. Practice champions and clinical staff were trained on appropriate documentation of blood pressure (BP) and diabetes measures, and new workflows to optimize care delivery. Quality improvement staff were trained in extracting data from electronic health records, providing feedback to practice clinicians, and reporting to Practice Transformation Network staff. Final measurement of BP control was 66%, and final measurement of blood glucose control was 28%. Quality improvement activities in a practice transformation network led to the delivery of high-quality care and quality improvement.

Behavior Change Techniques in Continuing Professional Development.

Continuing professional development (CPD) is a widely used and evolving set of complex interventions that seeks to update and improve the knowledge, skills, and performance of health care professionals to ultimately improve patient care and outcomes. While synthesized evidence shows CPD in general to be effective, effects vary, in part due to variation in CPD interventions and limited understanding of CPD mechanisms of action. We introduce two behavioral science tools-the Behavior Change Technique Taxonomy version 1 and the Theoretical Domains Framework-that can be used to characterize the content of CPD interventions and the determinants of behaviour potentially targeted by the interventions, respectively. We provide a worked example of the use of these tools in coding the educational content of 43 diabetes quality improvement trials containing clinician education as part of their multicomponent intervention. Fourteen (of a possible 93; 15%) behavior change techniques were identified in the clinician education content of the quality improvement trials, suggesting a focus of addressing the behavioral determinants beliefs about consequences, knowledge, skills, and social influences, of diabetes care providers' behavior. We believe that the Behavior Change Technique Taxonomy version 1 and Theoretical Domains Framework offer a novel lens to analyze the CPD content of existing evidence and inform the design and evaluation of future CPD interventions.

Diabetes quality improvement at scale: how Barking and Dagenham, Redbridge, and Havering CCGs tackled the 'Seven Spreadly Sins'.

The potential benefit of any quality improvement scheme can be increased manifold if it is consistently and systematically applied across organisations and systems. However, this is not easily achieved. Across the UK, a third of healthcare improvement projects never spread beyond their particular unit, a further third are embedded within their own unit and spread across an organisation, and only a third are spread across their own and other similar organisations.1,2 The Institute of Healthcare Improvement (IHI) has branded the ‘Seven Spreadly Sins’ as common slip-ups that organisations make that prevent the successful spread of quality.3 In 2016, Barking and Dagenham, Havering, and Redbridge Care Commissioning Groups (BHR CCGs) embarked upon a programme to improve the quality of care provided to diabetes patients. The three CCGs are challenged by escalating diabetes prevalence, poor uptake of the National Institute for Health and Care Excellence’s (NICE) recommended eight care processes,4 and an increasing rate of complications. Encouraged by the rapid improvement of quality in one area,5 we sought to …

Our diabetes quality improvement initiative journey - ensuring an efficient and purposeful clinic experience…

Our diabetes quality improvement initiative journey - ensuring an efficient and purposeful clinic experience…

Contacting authors by telephone increased response proportions compared with emailing: results of a randomized study

ObjectiveThe aim of the study was to compare response proportions and research costs of telephone calling vs. continued emailing nonresponding authors of studies included in a systematic review. Study Design and SettingKey features of included studies were poorly reported in a systematic review of diabetes quality improvement interventions. We developed a survey to request additional information from contact authors. After three email contact attempts, only 76 of 279 authors (27%) had completed the survey. In this study, we randomly assigned nonresponding authors to contact by telephone calling vs. continued emailing to compare the effect of these strategies on response proportions and research costs. ResultsWe randomized 87 authors to telephone and 89 to email contact. Telephone contact increased survey completion (36.7% vs. 20.2%; adjusted risk difference of 15.6% [95% confidence interval: 2.90%, 28.4%]; adjusted odds ratio 2.26 [95% confidence interval: 1.10, 4.76]) but required more time to deliver (20 vs. 10 hours in total; 14 vs. 7 minutes per randomized author; 26 vs. 4 weeks), and cost more (total intervention cost of $504 Canadian dollars vs. $252 for the telephone and email arm, respectively). ConclusionContacting nonresponding authors of included studies by telephone increased response compared with emailing but required more investigator time and had higher cost.

Determining Root Causes and Designing Change Ideas in a Quality Improvement Project.

In this second article of our diabetes quality improvement primer series, readers will become familiar with various diagnostic tools used to understand the root causes of a quality problem. We discuss change concepts, and specific change ideas are developed to match the root causes. We review the application of a plan-do-study-act cycle from the Model for Improvement quality improvement framework to test 1 change idea and measure for the intended improvements.

Implementing Change Ideas, Interpreting Data and Sustaining Change in a Quality Improvement Project.

This article is the third and final installment in our diabetes quality improvement primer series. It summarizes how to interpret real-time data with run charts and highlights 4 key rules that can be applied to understand whether improvement is statistically significant. We also review the importance of outlining a family of measures, including outcome, process and balancing measures. Finally, we discuss strategies for sustaining change.

Beginning a Diabetes Quality Improvement Project.

There is a large evidence-to-clinical practice gap in diabetes care. Application of quality improvement (QI) strategies can be used to improve gaps in care delivery. In this first of 3 articles in the diabetes QI primer series, we introduce the steps required to plan a QI project by using a case example for improving foot screening of people with diabetes. We review how to select an appropriate QI project, conduct a baseline gap analysis to clarify the QI problem and engage stakeholders to ensure successful implementation. The next 2 articles in the series will focus on root-cause analysis, selection of change ideas to improve care gaps, execution of the QI project using rapid-cycle testing and monitoring to sustain improvement over time.