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

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 relat...

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Autores principales: Pichardo-Lowden, Ariana, Umpierrez, Guillermo, Lehman, Erik B, Bolton, Matthew D, DeFlitch, Christopher J, Chinchilli, Vernon M, Haidet, Paul M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816906/
https://www.ncbi.nlm.nih.gov/pubmed/33462075
http://dx.doi.org/10.1136/bmjdrc-2020-001557
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author Pichardo-Lowden, Ariana
Umpierrez, Guillermo
Lehman, Erik B
Bolton, Matthew D
DeFlitch, Christopher J
Chinchilli, Vernon M
Haidet, Paul M
author_facet Pichardo-Lowden, Ariana
Umpierrez, Guillermo
Lehman, Erik B
Bolton, Matthew D
DeFlitch, Christopher J
Chinchilli, Vernon M
Haidet, Paul M
author_sort Pichardo-Lowden, Ariana
collection PubMed
description 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.
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spelling pubmed-78169062021-01-28 Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes Pichardo-Lowden, Ariana Umpierrez, Guillermo Lehman, Erik B Bolton, Matthew D DeFlitch, Christopher J Chinchilli, Vernon M Haidet, Paul M BMJ Open Diabetes Res Care Emerging Technologies, Pharmacology and Therapeutics 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. BMJ Publishing Group 2021-01-18 /pmc/articles/PMC7816906/ /pubmed/33462075 http://dx.doi.org/10.1136/bmjdrc-2020-001557 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Emerging Technologies, Pharmacology and Therapeutics
Pichardo-Lowden, Ariana
Umpierrez, Guillermo
Lehman, Erik B
Bolton, Matthew D
DeFlitch, Christopher J
Chinchilli, Vernon M
Haidet, Paul M
Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title_full Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title_fullStr Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title_full_unstemmed Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title_short Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
title_sort clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes
topic Emerging Technologies, Pharmacology and Therapeutics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816906/
https://www.ncbi.nlm.nih.gov/pubmed/33462075
http://dx.doi.org/10.1136/bmjdrc-2020-001557
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