Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal

Continuous improvement of quality of care in pediatric diabetes: the ISPED CARD clinical registry

AIM

In Italy, the ISPED CARD initiative was launched to measure and improve quality of care in children and adolescents with type 1 diabetes.

METHODS

Process and outcome indicators and the related information derived from electronic medical records were identified. A network of pediatric diabetes centers was created on a voluntary basis.

RESULTS

Overall, 20 centers provided data on 3284 patients aged <  = 18 years. HbA1c was monitored ≥ 2/year in 81.2% of the cases. BMI was monitored ≥ 1/year in 99.0%, lipid profile in 45.3%, and blood pressure in 91.7%. Pubertal status, albuminuria, eye examination, and screening of celiac disease and thyroiditis were underreported. From 2017 to 2021, average HbA1c levels decreased from 7.8 ± 1.2 to 7.6 ± 1.3%, while patients with LDL cholesterol > 100 mg/dl increased from 18.9 to 36.7%. Prevalence of patients with elevated blood pressure and BMI/SDS values also increased. In 2021, 44.7% of patients were treated with the newest basal insulins, while use of regular human insulin had dropped to 7.7%. Use of insulin pump remained stable (37.9%).

CONCLUSIONS

This report documents the feasibility of the ISPED CARD initiative and shows lights and shadows in the care provided. Improving care, increasing number of centers, and ameliorating data recording represent future challenges.

Demographic, Clinical, Management, and Outcome Characteristics of 8,004 Young Children With Type 1 Diabetes

OBJECTIVE

To compare demographic, clinical, and therapeutic characteristics of children with type 1 diabetes age <6 years across three international registries: Diabetes Prospective Follow-Up Registry (DPV; Europe), T1D Exchange Quality Improvement Network (T1DX-QI; U.S.), and Australasian Diabetes Data Network (ADDN; Australasia).

RESEARCH DESIGN AND METHODS

An analysis was conducted comparing 2019-2021 prospective registry data from 8,004 children.

RESULTS

Mean ± SD ages at diabetes diagnosis were 3.2 ± 1.4 (DPV and ADDN) and 3.7 ± 1.8 years (T1DX-QI). Mean ± SD diabetes durations were 1.4 ± 1.3 (DPV), 1.4 ± 1.6 (T1DX-QI), and 1.5 ± 1.3 years (ADDN). BMI z scores were in the overweight range in 36.2% (DPV), 41.8% (T1DX-QI), and 50.0% (ADDN) of participants. Mean ± SD HbA1c varied among registries: DPV 7.3 ± 0.9% (56 ± 10 mmol/mol), T1DX-QI 8.0 ± 1.4% (64 ± 16 mmol/mol), and ADDN 7.7 ± 1.2% (61 ± 13 mmol/mol). Overall, 37.5% of children achieved the target HbA1c of <7.0% (53 mmol/mol): 43.6% in DPV, 25.5% in T1DX-QI, and 27.5% in ADDN. Use of diabetes technologies such as insulin pump (DPV 86.6%, T1DX 46.6%, and ADDN 39.2%) and continuous glucose monitoring (CGM; DPV 85.1%, T1DX-QI 57.6%, and ADDN 70.5%) varied among registries. Use of hybrid closed-loop (HCL) systems was uncommon (from 0.5% [ADDN] to 6.9% [DPV]).

CONCLUSIONS

Across three major registries, more than half of children age <6 years did not achieve the target HbA1c of <7.0% (53 mmol/mol). CGM was used by most participants, whereas insulin pump use varied across registries, and HCL system use was rare. The differences seen in glycemia and use of diabetes technologies among registries require further investigation to determine potential contributing factors and areas to target to improve the care of this vulnerable group.

Optimizing Glycemic Outcomes for Children with Type 1 Diabetes

Changes in physical growth, neurocognitive development, and pubertal maturation are some of the challenges to achieving blood glucose targets in children with type 1 diabetes mellitus. To optimize glycemic outcomes, a comprehensive approach is crucial to address psychosocial needs, expand the use of diabetes technology, and diminish health inequities.

Improving Outcomes for People with Type 1 Diabetes Through Collaboration: Summary of Type 1 Diabetes Exchange Quality Improvement Collaborative Studies

Type 1 diabetes (T1D) management has evolved over the last decade. Innovations and groundbreaking research have paved the way for improved outcomes for people with T1D. One of the major T1D focused research network that has supported real-world research studies in the United States is the T1D Exchange Quality Improvement Collaborative (T1DX-QI) Network.T1DX-QI is a large multicenter network of 55 T1D clinics that uses quality improvement, health equity framework, and population health principles to improve outcomes for people with T1D. This article summarizes insights from T1DX-QI clinical and population health improvement studies.

Technology and health inequities in diabetes care: How do we widen access to underserved populations and utilize technology to improve outcomes for all?

Digital health technologies are being utilized increasingly in the modern management of diabetes. These include tools such as continuous glucose monitoring systems, connected blood glucose monitoring devices, hybrid closed-loop systems, smart insulin pens, telehealth, and smartphone applications (apps). Although many of these technologies have a solid evidence base, from the perspective of a person living with diabetes, there remain multiple barriers preventing their optimal use, creating a digital divide. In this article, we describe many of the origins of these barriers and offer recommendations on widening access to digital health technologies for underserved populations living with diabetes to improve their health outcomes.

Current Practices in Operationalizing and Addressing Racial Equity in the Provision of Type 1 Diabetes Care: Insights from the Type 1 Diabetes Exchange Quality Improvement Collaborative Health Equity Advancement Lab

OBJECTIVE

Medical racism contributes to adverse health outcomes. Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI) is a large population-based cohort engaged in data sharing and quality improvement to drive system changes in T1D care. The annual T1DX-QI survey included questions to evaluate racial equity in diabetes care and practices to promote equity.

METHODS

The annual T1DX-QI survey was administered to participating clinics in fall 2022 and had a 93% response rate. There were 50 responses (pediatric: 66% and adult: 34%). Questions, in part, evaluated clinical resources and racial equity. Response data were aggregated, summarized, and stratified by pediatric/adult institutions.

RESULTS

Only 21% pediatric and 35% adult institutions felt that all their team members can articulate how medical racism contributes to adverse diabetes outcomes. Pediatric institutions reported more strategies to address medical racism than adult (3.6 vs 3.1). Organizational strategies to decrease racial discrimination included employee trainings, equity offices/committees, patient resources, and hiring practices. Patient resources included interpreter services, transportation, insurance navigation, and housing and food assistance. Hiring practices included changing prior protocols, hiring from the community, and diversifying workforces. Most institutions have offered antiracism training in the last year (pediatric: 85% and adult: 72%) and annually (pediatric: 64% and adult: 56%). Pediatric teams felt that their antiracism training was effective more often (pediatric: 60% and adult: 45%) and more commonly, they were provided resources (pediatric: 67% and adult: 47%) to help address inequities.

CONCLUSION

Despite increased antiracism training, insufficient institutional support and perceived subeffective training still represent obstacles, especially in adult institutions. Sharing effective strategies to address medical racism will help institutions take steps to mitigate inequities.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative

BACKGROUND

Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI).

METHOD

We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements.

RESULTS

All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements.

CONCLUSIONS

The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Roadmap to Achieving Continuous Glucose Monitoring Equity: Insights From the T1D Exchange Quality Improvement Collaborative

This article describes successful interventions from the T1D Exchange Quality Improvement Collaborative (T1DX-QI) to reduce inequities in access to and use of continuous glucose monitoring (CGM). The author proposes a roadmap with recommendations for different stakeholders to achieve CGM equity using insights from the T1DX-QI experience.

An Observational Crossover Study of People Using Real-Time Continuous Glucose Monitors Versus Self-Monitoring of Blood Glucose: Real-World Evidence Using EMR Data From More Than 12,000 People With Type 1 Diabetes

BACKGROUND

We used real-world electronic health record (EHR) data to examine HbA1c levels among children and adults with type 1 diabetes (T1D) who are classified as continuous glucose monitor (CGM) users after T1D diagnosis and switch to self-monitoring of blood glucose (SMBG) during follow-up, versus people who opt for SMBG after T1D diagnosis and switch to CGM during follow-up visits.

METHODS

We conducted an observational, case-crossover study using electronic medical record (EMR) data from the T1D Exchange Quality Improvement Collaborative. The primary outcome in this study was HbA1c. Baseline HbA1c levels were taken at the index date, corresponding to initial device classification, and compared with HbA1c value recorded at the clinic visit following device switch.

RESULTS

Of all patients classified in the SMBG group, 7,706 switched to CGM use within the 5-year study time frame, and 5,123 of all initial CGM users switched to SMBG within the study time frame and were included in this analysis. At baseline, median (interquartile range [IQR]) HbA1c for SMBG use was 8.1 (2.4), whereas postcrossover to CGM use, there was a decline in median (IQR) levels to 7.7 (1.9) (P < .001). For baseline CGM users, median (IQR) HbA1c levels were 7.9 (2.0), and postcrossover to SMBG, median (IQR) HbA1c levels increased to 8.0 (2.9) (P < .001).

CONCLUSION

We found that people who switched to CGM use had significantly improved HbA1c levels compared to those who switched to glucose monitoring with SMBG.