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J Diabetes Sci Technol. 2019 May;13(3):522-532. doi: 10.1177/1932296818798036. Epub 2018 Sep 10.

Development and Implementation of a Subcutaneous Insulin Clinical Decision Support Tool for Hospitalized Patients.

Author information

1
1 Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
2
2 Epic Information Technology Team, Johns Hopkins Health System, Baltimore, MD, USA.
3
3 Nursing Administration, Clinical Informatics, Johns Hopkins Hospital, Baltimore, MD, USA.
4
4 Johns Hopkins Hospital, Baltimore, MD, USA.
5
5 Johns Hopkins Bayview Medical Center, Baltimore, MD, USA.
6
6 Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Abstract

BACKGROUND:

Insulin is one of the highest risk medications used in hospitalized patients. Multiple complex factors must be considered in determining a safe and effective insulin regimen. We sought to develop a computerized clinical decision support (CDS) tool to assist hospital-based clinicians in insulin management.

METHODS:

Adapting existing clinical practice guidelines for inpatient glucose management, a design team selected, configured, and implemented a CDS tool to guide subcutaneous insulin dosing in non-critically ill hospitalized patients at two academic medical centers that use the EpicCare® electronic medical record (EMR). The Agency for Healthcare Research and Quality (AHRQ) best practices in CDS design and implementation were followed.

RESULTS:

A CDS tool was developed in the form of an EpicCare SmartForm, which generates an insulin regimen by integrating information about the patient's body weight, diabetes type, home and hospital insulin requirements, and nutritional status. Total daily recommended insulin doses are distributed into respective basal and nutritional doses with a tailored correctional insulin scale. Preimplementation, several approaches were used to communicate this new tool to clinicians, including emails, lectures, and videos. Postimplementation, a support team was available to address user technical issues. Feedback from stakeholders has been used to continuously refine the tool. Inclusion of the programming in the EMR vendor's community library has allowed dissemination of the tool outside our institution.

CONCLUSIONS:

We have developed an EMR-based tool to guide SQ insulin dosing in non-critically ill hospitalized patients. Further studies are needed to evaluate adoption and clinical effectiveness of this intervention.

KEYWORDS:

clinical decision support systems; diabetes mellitus; hospital management insulin; subcutaneous (SQ)

PMID:
30198324
PMCID:
PMC6501530
[Available on 2019-09-10]
DOI:
10.1177/1932296818798036

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