Development and Operationalization of a Data Framework to Assess Quality of Integrated Diabetes Care in the Fragmented Data Landscape of Belgium

Background. To assess the quality of integrated diabetes care, we should be able to follow the patient throughout the care path, monitor his/her care process and link them to his/her health outcomes, while simultaneously link this information to the primary care system and its performance on the structure and organization related quality indicators. However the development process of such a data framework is challenging, even in period of increasing and improving health data storage and management. This study aims to develop an integrated multi-level data framework for quality of diabetes care and to operationalize this framework in the fragmented Belgium health care and data landscape.Methods. Based on document reviews and iterative expert consultations, theoretical approaches and quality indicators were identified and assessed. After mapping and assessing the validity of existing health information systems and available data sources through expert consultations, the theoretical framework was translated in a data framework with measurable quality indicators. The construction of the data base included sampling procedures, data-collection, and several technical and privacy-related aspects of linking and accessing Belgian datasets.Results. To address three dimensions of quality of care, we integrated the chronic care model and cascade of care approach, addressing respectively the structure related quality indicators and the process and outcome related indicators. The corresponding data framework is based on self-collected data at the primary care practice level (using the Assessment of quality of integrated care tool), and linked health insurance data with lab data at the patient level. Conclusion. In this study, we have described the transition of a theoretical quality of care framework to a unique multilevel database, which allows assessing the quality of diabetes care, by considering the complete care continuum (process and outcomes) as well as organizational characteristics of primary care practices.

DPARD: rationale, design and initial results from the Dutch national diabetes registry

Background Treatment of diabetes mellitus has majorly improved over the past century, however, the disease burden is high and its prevalence still expanding. Further insight in the diabetes population is imperative to improve the quality of diabetes care by enhancement of knowledge-based diabetes management strategies. To this end, in 2017 a Dutch nationwide consortium of diabetologists, paediatric endocrinologists, and diabetes patients has founded a national outpatient diabetes care registry named Dutch Pediatric and Adult Registry of Diabetes (DPARD). We aim to describe the implementation of DPARD and to provide an overview of the characteristics of patients included during the first 2 years. Methods For the DPARD cohort with long-term follow-up of observational nature, hospital data are gathered directly from electronic health records and securely transferred and stored. DPARD provides weekly updated clinical information on the diabetes population care on a hospital-level benchmarked against the national average. Results Between November 2017 and January 2020, 20,857 patients were included from 8 (11%) Dutch hospitals with a level of care distribution representative of all diabetic outpatients in the Netherlands. Among patients with known diabetes type, 41% had type 1 diabetes, 51% type 2 diabetes, and 8% had diabetes due to other causes. Characteristics of the total patient population were similar to patients with unknown diabetes classification. HbA1c levels decreased over the years, while BMI levels showed an increase over time. Conclusions The national DPARD registry aims to facilitate investigation of prevalence and long-term outcomes of Dutch outpatients with diabetes mellitus and their treatment, thus allowing for quality improvement of diabetes care as well as allowing for comparison of diabetes care on an international level.

The Endocrine Hospitalist: Enhancing the Quality of Diabetes Care

The endocrine hospitalist and inpatient diabetes management team increases access to endocrinology consultations and improves glycemic control and quality metrics such as length of stay and hospital readmission. Enhanced glycemic care is needed in both academic and community hospital settings. Endocrine fellowship programs should implement endocrine hospitalist rotations with emphasis on training endocrine fellows to deliver fast-paced inpatient endocrine care. Entrepreneurship, innovation, and a “start-up” culture within the field of Endocrinology should be encouraged and supported by healthcare systems.

AMD Annals 2020 - Synopsis on Type 2 Diabetes. Evaluation of AMD quality indicators of type 2 diabetes care in Italy

OBJECTIVE OF THE STUDY The AMD 2020 Annals on Type 2 Diabetes (DM2) set out to show, 2 years after the last evaluation, how the quality of DM2 care has evolved in Italy. DESIGN AND METHODS In order to participate in the initiative, the centers had to be equipped with information systems capable of guaranteeing the standardized extraction of the information necessary for the creation of the AMD Data File. The data analyzed concern socio-de-mographic and clinical characteristics and volume of activity. The selection of indicators is based on Revision 8 of June 2019 (AMD Annals website). RESULTS DM2 patients increased to 473,740 (57.1% M; 42.9% F, 67.4% aged> 65 y). 6% new diagnoses. All monitoring indicators, of favorable and unfavorable outcome, have improved: 52.9% of DM2 have HbA1c levels <= 7.0%(53 mmol/mol), 63.5% have LDL cholesterol values <100 mg / dl, 53,5% have blood pressure levels <140/80 mmHg, 39.9% are obese. The proportion of patients with GFR <60 mL/min*1.73 m2 rose to 29%, and 7.1% had GFR <30 mL / min. Therapy: there is a reduction in the use of sulfonylureas and glinides (19.9%); stable use of insulin; new drugs are increasingly prescribed (DPPIVi:21%; GLP1-RA: 5.9%; SGLT2i: 9.6%). 60.8% are on lipid-lowering treatment, 70% are on antihypertensive therapy, but 48.6% are not on target. Complications: 22% have diabetic retinopathy; 7.5% had myocardial infarction, 2.7 had a stroke, 14.7% had a history of cardiovascular disease. 50.8% of subjects with age>75a have HbA1c levels <= 7.0%(53 mmol/mol), of these 16.3% are treated with drugs that can induce hypoglycemia. Patients with Q Score> 25 are growing (60.3%). CONCLUSIONS The AMD 2020 Annals on DM2 show a marked improvement in all indicators of quality of care, but large areas of undertreatment and other overtreatment remain, and call to action. KEY WORDS AMD Annals; type2 diabetes mellitus; quality of diabetes care in type 2 diabetes patients in Italy; undertreatment; overtreatment.

Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review

Background: Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are “locked” in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. Methods: We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. Results: We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. Conclusion: Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.