Danny T Y Wu, Tripura M Vithala, Hoang Vu, Chen Xin, Lezhi Li, Amy Roberto, Adam Alexander, Devendra P Sohal, Thomas J Herzog, James J Lee
{"title":"Development of a Clinical Decision Support System to Predict Unplanned Cancer Readmissions.","authors":"Danny T Y Wu, Tripura M Vithala, Hoang Vu, Chen Xin, Lezhi Li, Amy Roberto, Adam Alexander, Devendra P Sohal, Thomas J Herzog, James J Lee","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Unplanned 30-day cancer readmissions are an important outcome of cancer hospitalization and can significantly raise mortality rates and costs for both the patient and the hospital. This paper aimed to develop a predictive model using machine learning and electronic health records to predict unplanned 30-day cancer readmissions and further develop it as a clinical decision support system. The three-stage study design followed the 2022 AMIA Artificial Intelligence Evaluation Showcase. In the first stage, the technical performance of the model was determined (81% of AUROC) and contributing factors were identified. In the second stage, the technical feasibility and workflow considerations of using such a predictive model were explored through semi-structured interviews. In the third stage, a decision tree analysis and a cost estimation showed that the model can reduce unplanned readmissions significantly if timely action is taken and that preventing a single readmission may significantly reduce costs.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148334/pdf/7007.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA ... Annual Symposium proceedings. AMIA Symposium","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Unplanned 30-day cancer readmissions are an important outcome of cancer hospitalization and can significantly raise mortality rates and costs for both the patient and the hospital. This paper aimed to develop a predictive model using machine learning and electronic health records to predict unplanned 30-day cancer readmissions and further develop it as a clinical decision support system. The three-stage study design followed the 2022 AMIA Artificial Intelligence Evaluation Showcase. In the first stage, the technical performance of the model was determined (81% of AUROC) and contributing factors were identified. In the second stage, the technical feasibility and workflow considerations of using such a predictive model were explored through semi-structured interviews. In the third stage, a decision tree analysis and a cost estimation showed that the model can reduce unplanned readmissions significantly if timely action is taken and that preventing a single readmission may significantly reduce costs.