Modeling patient-related workload in the emergency department using electronic health record data.
Introduction: Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The...
| Publicado en: | International Journal of Medical Informatics Vol. 150 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150299041&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150299041 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Jun2021 vid: 150 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 150299041 150299041 NLM33862507 150299041 10.1016/j.ijmedinf.2021.104451 NLM33862507 150299041 ppct: 1 formats: tig: atl: Modeling patient-related workload in the emergency department using electronic health record data. aug: au: Wang, Xiaomei Blumenthal, H. Joseph Hoffman, Daniel Benda, Natalie Kim, Tracy Perry, Shawna Franklin, Ella S. Roth, Emilie M. Hettinger, A. Zachary Bisantz, Ann M. affil: University at Buffalo, United States sug: subj: Workload Physicians Emergency Service Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Introduction: Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).Methods: One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.Results: Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R2 > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).Conclusion: The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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