The Effectiveness of an Electronic Decision Support Algorithm to Optimize Recommendations of SGLT2i and GLP-1RA in Patients with Type 2 Diabetes upon Discharge from Internal Medicine Wards

TL;DR

A digital tool helped doctors recommend better diabetes medications after hospital discharge, significantly increasing the use of beneficial drugs.

Contribution

A clinical decision support system was developed and shown to significantly increase the recommendation rate of SGLT2i and GLP-1RA in type 2 diabetes patients.

Findings

The recommendation rate of SGLT2i and GLP-1RA increased from 8.5% to 22.7% after implementing the algorithm.

Heart failure patients saw a notable increase in recommendation rates from 14.6% to 49.02%.

The odds ratio of recommendation in the post-algorithm group was 3.151 compared to the pre-algorithm group.

Abstract

Background/Objectives: Despite the established cardiovascular benefit of sodium–glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs), these medications are under-prescribed in patients with type 2 diabetes. Our study aims to examine the effectiveness of a clinical decision support system (CDSS) in improving the recommendation rate of SGLT2i and GLP-1RA upon discharge. Methods: We developed an algorithm to automatically recommend SGLT2is and GLP-1RAs for eligible patients with type 2 diabetes upon discharge, based on current guidelines. Data were collected from electronic medical records of all eligible patients ≥18 years old hospitalized in one of five internal medicine wards at Beilinson Hospital. The primary outcome was to evaluate the rate of physician recommendation of SGLT2is and GLP-1RAs at discharge, before and after algorithm implementation. Results: Our study included 1318 patients in the pre-algorithm group and 970 in the post-algorithm group. The recommendation rate of SGLT2is and GLP-1RAs was 8.5% in the pre-algorithm group and 22.7% in the post-algorithm. The odds ratio (OR) of recommendation in the post- vs. pre-algorithm group was 3.151 (95% CI: 2.467–4.025, p < 0.0001). Recommendation rates increased in all subgroups analyzed, notably in patients hospitalized due to heart failure (recommendation rate pre-algorithm: 14.6% vs. post-algorithm: 49.02%). Conclusions: This study demonstrates the benefit of a CDSS in improving the recommendation rate of SGLT2is and GLP-1RAs in patients with type 2 diabetes upon discharge from hospitalization. Future studies should assess the impact of the algorithm on recommendation rates in other wards, medication utilization, and long-term outcomes.

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