Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BACKGROUND: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing

Challenges to Diabetes Self-Management in Emerging Adults With Type 1 Diabetes

PURPOSE: The purpose of this qualitative descriptive study undergirded by Meleis\u27s Transition Framework was to explore developmental, situational, and organizational challenges experienced by a diverse group of emerging adults (18-29 years old) with type 1 diabetes (T1DM). Their perspectives on creating a developmentally informed diabetes self-management (DSM) program that supports transitional care were also explored. METHODS: A purposive sample of emerging adults with T1DM was recruited from the pediatric and adult diabetes clinics of an urban academic medical center. Those who consented participated in either a single focus group or a single interview. Self-reported demographic and clinical information was also collected. RESULTS: The sample was comprised of 21 emerging adults, with an average age of 23.6 +/- 2.6 years, diabetes duration of 14.7 +/- 5.0 years, and 71% female. Four main themes emerged: (1) finding a balance between diabetes and life, (2) the desire to be in control of their diabetes, (3) the hidden burden of diabetes, and (4) the desire to have a connection with their diabetes provider. Use of insulin pumps and continuous glucose monitors and attendance at diabetes camp decreased some of the DSM challenges. Different groups of individuals had different perspectives on living with diabetes and different approaches to DSM. CONCLUSIONS: The emerging adults in this study had a strong desire to be in good glycemic control. However, all participants described having a hard time balancing DSM with other competing life priorities. They also desired personalized patient-provider interactions with their diabetes care provider in clinical follow-up services. Even though the study sample was small, important themes emerged that warrant further exploration

A Methodological Review Of Faith-Based Health Promotion Literature: Advancing The Science To Expand Delivery Of Diabetes Education To Black Americans

Non-traditional avenues, such as faith-based organizations (FBOs), must be explored to expand delivery of diabetes self-management education (DSME) to benefit Black Americans with type 2 diabetes (T2D). The purpose of this study was to methodologically review the faith-based health promotion literature relevant to Blacks with T2D. A total of 14 intervention studies were identified for inclusion in the review. These studies detailed features of methods employed to affect health outcomes that DSME similarly targets. Analysis of the faith-based studies\u27 methodological features indicated most studies used (1) collaborative research approaches, (2) pre-experimental designs, (3) similar recruitment and retention strategies, and (4) culturally sensitive, behaviorally oriented interventions with incorporation of social support to achieve positive health outcomes in Black Americans. Findings indicate FBOs may be a promising avenue for delivering DSME to Black Americans. Informed by the findings, a focused discussion on advancing the science of faith-based interventions to expand delivery of DSME to Black Americans with diabetes is provided. © 2011 Springer Science+Business Media, LLC