Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May;15(3):553-560.
doi: 10.1177/19322968211000831. Epub 2021 Mar 19.

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

Alexander Turchin  1 , Luisa F Florez Builes  1
Affiliations

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

Alexander Turchin et al. J Diabetes Sci Technol. 2021 May.

Abstract

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.

Keywords: diabetes; electronic health records; natural language processing.

PubMed Disclaimer

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Alexander Turchin: equity in Brio Systems; consulting fees from Proteomics International; research funding from Astra Zeneca, Edwards, Eli Lilly, Novo Nordisk, Pfizer and Sanofi.

Luisa F. Florez Builes: none.

Figures

Figure 1.
Figure 1.
PRISMA flow diagram.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Sherman RE, Anderson SA, Dal Pan GJ, et al.. Real-world evidence: what is it and what can it tell us? N Engl J Med. 2016;375(23):2293-2297. - PubMed
    1. Krause JH, Saver RS. Real-world evidence in the real world: beyond the FDA. Am J Law Med. 2018;44(2-3):161-179. - PubMed
    1. Klonoff DC. The new FDA real-world evidence program to support development of drugs and biologics. J Diabetes Sci Technol. 2020;14(2):345-349. - PMC - PubMed
    1. Califf RM, Robb MA, Bindman AB, et al.. Transforming evidence generation to support health and health care decisions. N Engl J Med. 2016;375(24):2395-2400. - PubMed
    1. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544-551. - PMC - PubMed

Publication types