Tarumi, ShinjiTakeuchi, WataruQi, RongNing, XiaRuppert, LauraBan, HideyukiRobertson, Daniel H.Schleyer, TitusKawamoto, Kensaku2023-11-172023-11-172022-05Tarumi, S., Takeuchi, W., Qi, R., Ning, X., Ruppert, L., Ban, H., Robertson, D. H., Schleyer, T., & Kawamoto, K. (2022). Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources. Journal of Biomedical Informatics, 129, 104001. https://doi.org/10.1016/j.jbi.2022.104001https://hdl.handle.net/1805/37119Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution—combining data from multiple sources—faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus (T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average (WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalArtificial intelligenceClinical decision support systemHealth information interoperabilityDisease managementChronic diseaseArticle