Health Care Interventions to Improve the Quality of Diabetes Care in African Americans: A systematic review and meta-analysis

The purpose of creating the DIABNOR register is to study the quality of diabetes care in Norway. We will study the prevalence of serious complications ("hard endpoints") and what kind of treatment patients receive and how these vary with age, gender, ethnicity, education and place of residence. It is a crosssectional study based on data from all hospital contacts in Norway between 2004 and 2010, linked to the National Prescription Database (NorPD) containing information on all drug treatment patients receive, not only from hospitals but also from general practitioners. Data are also linked to Statistics Norway for collecting information about ethnicity, social status and marital status. Development of quality indicators (QI) for diabetes is also an objective of the project, including indicators to measure compliance with guidelines and achievement of treatment goals. The researchers behind the project believe it is important to measure quality and differences in health care in order to improve the prognosis in this patient group

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P05 Exploring the impact of the quality and outcomes framework on the quality of diabetes care and health care inequalities in England

Journal of Epidemiology & Community Health ◽

10.1136/jech.2010.120477.5 ◽

2010 ◽

Vol 64 (Suppl 1) ◽

pp. A35-A35

Author(s):

J. Akanuwe ◽

E. Goyder ◽

R. O'Hara ◽

S. Julious

Keyword(s):

Health Care ◽

Diabetes Care ◽

Quality Of Diabetes Care ◽

Quality And Outcomes ◽

The Impact ◽

Quality And Outcomes Framework ◽

Health Care Inequalities

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

Journal of Diabetes Science and Technology ◽

10.1177/19322968211000831 ◽

2021 ◽

pp. 193229682110008

Author(s):

Alexander Turchin ◽

Luisa F. Florez Builes

Keyword(s):

Systematic Review ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Real World ◽

Diabetes Care ◽

Large Fraction ◽

Real World Data ◽

Quality Of Diabetes Care

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.

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