Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes

• Type of Material: Article
• Authors: Ariana Pichardo-Lowden, Penn State Health Milton S Hershey Medical Center; Guillermo Umpierrez, Emory University; Erik B. Lehman, Penn State College of Medicine; Matthew D. Bolton, Penn State Health Milton S Hershey Medical Center; Christopher J. DeFlitch, Penn State Health Milton S Hershey Medical Center; Vernon M. Chinchilli, Penn State College of Medicine; Paul M. Haidet, Penn State Health Milton S Hershey Medical Center
• Language: English
• Date: 2021-01-01
• Publisher: BMJ Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © Author(s) (or their employer(s)) 2021.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1136/bmjdrc-2020-001557
• Title of Journal or Parent Work: BMJ Open Diabetes Research & Care
• Volume: 9
• Issue: 1
• Grant/Funding Information: AP-L is supported by a career development award from the National Institute of Diabetes Digestive and Kidney Disease K23DK107914-05 and by The Eberly Medical Research Innovation Fund, which permitted the conduct of this work.
• Keywords: hyperglycemia; Care; Technology; decision support techniques; Endocrinology & Metabolism; Association; type 2; Life Sciences & Biomedicine; Systems; diabetes mellitus; Glucose management; electronic health records; Science & Technology; Inertia; Hyperglycemia; Noncritically ill patients
• Research Categories: Health Sciences, Public Health; Health Sciences, Health Care Management; Health Sciences, Medicine and Surgery

Abstract

INTRODUCTION: Innovative approaches are needed to design robust clinical decision support (CDS) to optimize hospital glycemic management. We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations. RESEARCH DESIGN AND METHODS: The tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9-4.4 mmol/L (70-80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. The EMR CDS was active (ON) for 6 months for all adult hospital patients and inactive (OFF) for 6 months. We prospectively identified and compared gaps in care between ON and OFF periods. RESULTS: When active, the hospital CDS tool significantly reduced events of recurrent hyperglycemia in patients with type 1 and type 2 diabetes (3342 vs 3701, OR=0.88, p=0.050) and in patients with stress hyperglycemia (288 vs 506, OR=0.60, p<0.001). Hypoglycemia or impending hypoglycemia (1548 vs 1349, OR=1.15, p=0.050) were unrelated to the CDS tool on subsequent analysis. Inappropriate use of SSI monotherapy in type 1 diabetes (10 vs 22, OR=0.36, p=0.073), inappropriate use of SSI monotherapy in type 2 diabetes (2519 vs 2748, OR=0.97, p=0.632), and in stress hyperglycemia subjects (1617 vs 1488, OR=1.30, p<0.001) were recognized. CONCLUSION: EMR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes, and inappropriate insulin use in patients with type 1 diabetes.