Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0173021
Title: Development of a clinical decision support system for diabetes care: A pilot study
Authors: Sim L.L.W.
Ban K.H.K. 
Tan T.W. 
Sethi S.K. 
Loh T.P. 
Keywords: glucose
biological marker
glycosylated hemoglobin
hemoglobin A1c protein, human
low density lipoprotein
Article
clinical decision support system
computer interface
controlled study
diabetes dashboard
diabetes mellitus
human
intermethod comparison
kidney function
lipid analysis
pilot study
practice guideline
process development
trend study
blood
diabetes mellitus
electronic health record
metabolism
Biomarkers
Decision Support Systems, Clinical
Diabetes Mellitus
Electronic Health Records
Hemoglobin A, Glycosylated
Humans
Lipoproteins, LDL
Pilot Projects
Practice Guidelines as Topic
User-Computer Interface
Issue Date: 2017
Publisher: Public Library of Science
Citation: Sim L.L.W., Ban K.H.K., Tan T.W., Sethi S.K., Loh T.P. (2017). Development of a clinical decision support system for diabetes care: A pilot study. PLoS ONE 12 (2) : e0173021. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0173021
Abstract: Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention. © 2017 Sim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/166018
ISSN: 19326203
DOI: 10.1371/journal.pone.0173021
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