Experience Sharing on Information Technology related Patient Safety Program

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<p>現今資訊發展及流通迅速，確保資料的正確性及維護醫院資訊系統和相關設備，是「病人安全」中的重要課題。本院於2021年參與財團法人醫院評鑑暨醫療品質策進會「醫療科技問題與病人安全風險學習平台(Information Technology related Patient Safety, ITPS)」，經過一年的專案學習和運作，於2022年由醫療品質部、資訊室、護理部及醫工室正式組成團隊研究調查小組，訂定小組運作及檢討機制，針對內外部專家之建議予以改進，進而達到品質提升之目的。本院也藉由收集之通報案件，加入相關單位人員的參與討論，提升團隊合作及單位安全之風氣，讓醫院的資訊系統能更符合使用者的需求，提供更有效率的安全把關。</p> <p> </p><p>Purpose: The prevalence of diabetes mellitus (DM) continues to increase worldwide. We built a machine learning model and developed a prediction system that is based on an optimal model to effectively predict blood sugar changes in patients with diabetes. Our findings contribute to the implementation of long-term patient nutrition interventions.</p> <p>Method: Data of outpatients with type 2 DM who were 20 years or older and underwent nutrition education under a diabetes pay-for-performance program were obtained from the Nutrition and Health System Database of the outpatient clinic of the Chi Mei Hospital network; the data spanned the years from 2007 to 2019. On the basis of literature findings and professional experience, 20 characteristic variables and multiple machine learning algorithms were applied to build a model to predict whether the glycosylated hemoglobin (HbA1c) of the outpatients improved by more than 7% after 1 year. The optimal model (model with the highest area under the curve [AUC]) was selected and used to develop a prediction system for use in clinical settings.</p> <p>Results: The accuracy levels of the developed models ranged from 0.735 to 0.749; the supportvector- machine model with a sensitivity of 0.757, a specificity of 0.739, and an AUC of 0.828 was the optimal prediction model. The prediction system was tested by three dietitians, who affirmed its usefulness for diabetes meal planning and patient health education.</p> <p>Conclusion: The prediction model based on machine learning algorithms performed excellently, and it is a promising tool for diabetes meal planning and patient health education. It is also an effective supporting tool for clinical disease care and dietary health education interventions. We believe that the model can help patients maintain favorable long-term blood sugar control, reduce their incidence of diabetes-related complications, improve the quality of medical care and promote shared decisionmaking.</p> <p> </p>