Mohammed A Mahyoub, Kacie Dougherty, Ravi R Yadav, Raul Berio-Dorta, Ajit Shukla
{"title":"Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.","authors":"Mohammed A Mahyoub, Kacie Dougherty, Ravi R Yadav, Raul Berio-Dorta, Ajit Shukla","doi":"10.3389/fdgth.2024.1455446","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Discharge date prediction plays a crucial role in healthcare management, enabling efficient resource allocation and patient care planning. Accurate estimation of the discharge date can optimize hospital operations and facilitate better patient outcomes.</p><p><strong>Materials and methods: </strong>In this study, we employed a systematic approach to develop a discharge date prediction model. We collaborated closely with clinical experts to identify relevant data elements that contribute to the prediction accuracy. Feature engineering was used to extract predictive features from both structured and unstructured data sources. XGBoost, a powerful machine learning algorithm, was employed for the prediction task. Furthermore, the developed model was seamlessly integrated into a widely used Electronic Medical Record (EMR) system, ensuring practical usability.</p><p><strong>Results: </strong>The model achieved a performance surpassing baseline estimates by up to 35.68% in the F1-score. Post-deployment, the model demonstrated operational value by aligning with MS GMLOS and contributing to an 18.96% reduction in excess hospital days.</p><p><strong>Conclusions: </strong>Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1455446"},"PeriodicalIF":3.2000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471729/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fdgth.2024.1455446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Discharge date prediction plays a crucial role in healthcare management, enabling efficient resource allocation and patient care planning. Accurate estimation of the discharge date can optimize hospital operations and facilitate better patient outcomes.
Materials and methods: In this study, we employed a systematic approach to develop a discharge date prediction model. We collaborated closely with clinical experts to identify relevant data elements that contribute to the prediction accuracy. Feature engineering was used to extract predictive features from both structured and unstructured data sources. XGBoost, a powerful machine learning algorithm, was employed for the prediction task. Furthermore, the developed model was seamlessly integrated into a widely used Electronic Medical Record (EMR) system, ensuring practical usability.
Results: The model achieved a performance surpassing baseline estimates by up to 35.68% in the F1-score. Post-deployment, the model demonstrated operational value by aligning with MS GMLOS and contributing to an 18.96% reduction in excess hospital days.
Conclusions: Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.
背景介绍出院日期预测在医疗管理中起着至关重要的作用,它有助于有效的资源分配和患者护理规划。准确估计出院日期可以优化医院运营,促进改善患者预后:在本研究中,我们采用了一种系统方法来开发出院日期预测模型。我们与临床专家密切合作,确定有助于提高预测准确性的相关数据元素。特征工程用于从结构化和非结构化数据源中提取预测特征。预测任务采用了强大的机器学习算法 XGBoost。此外,所开发的模型被无缝集成到一个广泛使用的电子病历(EMR)系统中,确保了实用性:结果:该模型的 F1 分数比基线估计值高出 35.68%。部署后,该模型与 MS GMLOS 保持一致,使超常住院日减少了 18.96%,从而体现了其操作价值:我们的研究结果凸显了所开发的出院日期预测模型在临床实践中的有效性和潜在价值。通过提高出院日期预估的准确性,该模型有望加强医疗资源管理和患者护理规划。其他研究工作应优先评估该模型在不同情况下的长期适用性,并全面分析其对患者预后的影响。