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. 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

Affiliations

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

Nestoras Mathioudakis et al. J Diabetes Sci Technol. 2019 May.

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).

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
CDS system development and implementation process and timeline.
Figure 2.
Figure 2.
Branching medical Logic for SQ Decision Support Tool. A total of 13 discrete recommendation types are provided based on user responses. ICR, patient will report his or her insulin to carbohydrate ratio; Med, medium; TDD, total daily dose (eg, 1 unit for each 10 grams of carbohydrates consumed).
Figure 3.
Figure 3.
Epic workflow for insulin management. This workflow is shown for only one insulin type (basal insulin), but the same workflow would apply to nutritional insulin. Orange boxes indicate best practice approach (recommended) in which clinicians complete the SQ insulin decision support tool prior to entering insulin doses. Blue boxes indicate alternative approach which bypasses the SQ insulin decision support tool. Disch, discharge; FYI, for your information; MAR, Medication Administration Record; POCT, point of care testing; SQ, subcutaneous; TPN, total parenteral nutrition. *If SQ decision support tool not completed, this section remains blank in the order “summary report” (see Figure 5).
Figure 4.
Figure 4.
Screenshots of the subcutaneous (SQ) Insulin clinical decision support (CDS) tool recommendations (see Figure 2 for branching logic). SQ Insulin CDS tool is accessed from a “SQ Insulin” tab under Orders shown at top of screen. SQ CST tool window can be minimized. Example shown here is for a patient with type 2 diabetes not on insulin at home who is eating regular meals. Step 1: User selects indication for insulin therapy. Step 2: Weight-based insulin total daily dose (TDD) is automatically calculated from weight (in this case 63 kg), with recommended dose shown in green based on the BMI (in this case 27 kg/m2). Step 3: User reviews TDD recommendations from Step 2 to determine an insulin TDD, which is manually entered. Step 4: User selects nutritional source (in this case, “eating meals or receiving bolus tube feedings”) and specifies whether patient uses carbohydrate counting at home (default selected to “no”). Step 5: Insulin recommendations are provided and may be reviewed by user here on in the order summary report in the corresponding insulin order (ie, recommendations carry forward). Step 6: User opens insulin order set (see Figure 5 for remaining steps).
Figure 5.
Figure 5.
Screenshot of a subcutaneous (SQ) insulin order showing how recommendations from SQ insulin clinical decision support (CDS) tool are displayed above where the provider is to enter the desired insulin dose. Red box indicates order “Summary Report,” which captures recommended doses from the SQ insulin CDS tool and all administered SQ insulin doses in previous 24 hours. User can adjust dose of all insulin orders at this step. User can also directly access the SQ Insulin CDS Tool with reference link at top of the order.

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