A toolbox to improve algorithms for insulin-dosing decision support

Background: Standardized insulin order sets for subcutaneous basal-bolus insulin therapy are recommended by clinical guidelines for the inpatient management of diabetes. The algorithm based GlucoTab system electronically assists health care personnel by supporting clinical workflow and providing insulin-dose suggestions. Objective: To develop a toolbox for improving clinical decision-support algorithms. Methods: The toolbox has three main components. 1) Data preparation: Data from several heterogeneous sources is extracted, cleaned and stored in a uniform data format. 2) Simulation: The effects of algorithm modifications are estimated by simulating treatment workflows based on real data from clinical trials. 3) Analysis: Algorithm performance is measured, analyzed and simulated by using data from three clinical trials with a total of 166 patients. Results: Use of the toolbox led to algorithm improvements as well as the detection of potential individualized subgroup-specific algorithms. Conclusion: These results are a first step towards individualized algorithm modifications for specific patient subgroups.