Clinical decision support systems with team-based care on type 2 diabetes improvement for Medicaid patients: A quality improvement project

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

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.

Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions

Objective

Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician.

Methods

We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines.

Results

Eight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case.

Conclusions

Despite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

Harnessing the power of clinical decision support systems: challenges and opportunities

Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.