Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening

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Abstract

Objective ?With the increasing prevalence of type 2 diabetes (T2D) in youth, primary care providers must identify patients at high risk and implement evidence-based screening promptly. Clinical decision support systems (CDSSs) provide clinicians with personalized reminders according to best evidence. One example is the Child Health Improvement through Computer Automation (CHICA) system, which, as we have previously shown, significantly improves screening for T2D. Given that the long-term success of any CDSS depends on its acceptability and its users' perceptions, we examined what clinicians think of the CHICA diabetes module. Methods ?CHICA users completed an annual quality improvement and satisfaction questionnaire. Between May and August of 2015 and 2016, the survey included two statements related to the T2D-module: (1) CHICA improves my ability to identify patients who might benefit from screening for T2D and (2) CHICA makes it easier to get the lab tests necessary to identify patients who have diabetes or prediabetes. Answers were scored using a 5-point Likert scale and were later converted to a 2-point scale: agree and disagree. The Pearson chi-square test was used to assess the relationship between responses and the respondents. Answers per cohort were compared using the Mann-Whitney U -test. Results ?The majority of respondents (N = 60) agreed that CHICA improved their ability to identify patients who might benefit from screening but disagreed as to whether it helped them get the necessary laboratories. Scores were comparable across both years. Conclusion ?CHICA was endorsed as being effective for T2D screening. Research is needed to improve satisfaction for getting laboratories with CHICA.

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El Mikati, H. K., Yazel-Smith, L., Grout, R. W., Downs, S. M., Carroll, A. E., & Hannon, T. S. (2020). Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening. Applied Clinical Informatics, 11(2), 350–355. https://doi.org/10.1055/s-0040-1710024

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