Safe and Sufficient Glycemic Control by Using a Digital Clinical Decision Support System for Patients With Type 2 Diabetes in a Routine Setting on General Hospital Wards

Abstract

The aim was to investigate the applicability of a clinical decision support system in a real-world inpatient setting for patients with type 2 diabetes on general hospital wards. A total of 150 patients with type 2 diabetes requiring subcutaneous insulin therapy were treated with basal-bolus insulin therapy guided by a decision support system (GlucoTab) providing automated workflow tasks and suggestions for insulin dosing to health care professionals. By using the system, a mean daily blood glucose (BG) of 159 ± 32 mg/dL was achieved. 68.8% of measurements were in the target range (70 to <180 mg/dL). The percentage of BG values <40, <70, and ≥300 mg/dL was 0.02%, 2.2%, and 2.3%, respectively. Health care professionals’ adherence to suggested insulin doses and workflow tasks was high (>93% and 91%, respectively). The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing.

Efficient and safe glycaemic control with basal‐bolus insulin therapy during fasting periods in hospitalized patients with type 2 diabetes using decision support technology: A post hoc analysis

“…Data from two prospective interventional studies performed at a tertiary centre were pooled. The blood glucose (BG) levels, the administered insulin doses and the fasting periods collected during two clinical trials that used an eDSS for diabetes management allowed us to assess the impact of BBT on glycaemic control in patients who underwent fasting periods during their hospitalization 9,10 …”

“…Data included in the analysis were extracted from the database and were initially collected as part of two clinical studies 9,10 . Data include insulin dose suggestions calculated by the BBT algorithm, administered insulin doses, BG values and meal consumption, including time and date for each of the above.…”

“…Baseline characteristics were retrieved from the electronic clinical trials database 9,10 . These include patient age, sex, ethnicity, body mass index (BMI), kidney function, HbA1c, length of inclusion, diabetes duration, pre‐existing diabetes treatment and type of hospital admission (acute or scheduled).…”

“…The blood glucose (BG) levels, the administered insulin doses and the fasting periods collected during two clinical trials that used an eDSS for diabetes management allowed us to assess the impact of BBT on glycaemic control in patients who underwent fasting periods during their hospitalization. 9,10 Overall, the study population of this post hoc analysis consisted of 249 patients with type 2 diabetes admitted to internal medicine (Divisions of Endocrinology and Diabetology; Cardiology; Nephrology) and surgery (Division of Plastic, Aesthetic and Reconstructive Surgery) wards. The studies that provided the data for this analysis were performed according to Good Clinical Practice and the Declaration of Helsinki.…”

Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

“…CDSs improve healthcare professionals' adherence to suggested insulin doses and workflow tasks. The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing [22]. The CDSs integrated with the Epic EHR at the University of Utah enable clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.…”