Xiaomei Wang1, H Joseph Blumenthal2, Daniel Hoffman2, Natalie Benda2, Tracy Kim2, Shawna Perry3, Ella S Franklin2, Emilie M Roth4, A Zachary Hettinger5, Ann M Bisantz6. 1. University at Buffalo, United States. Electronic address: xiaomeiw@buffalo.edu. 2. National Center for Human Factors in Healthcare, MedStar Institute for Innovation, United States. 3. University of Florida, United States. 4. Roth Cognitive Engineering, United States. 5. National Center for Human Factors in Healthcare, MedStar Institute for Innovation, United States; Georgetown University School of Medicine, United States. 6. University at Buffalo, United States.
Abstract
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.
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.
Authors: Alexandra Mullins; Renee O'Donnell; Mariam Mousa; David Rankin; Michael Ben-Meir; Christopher Boyd-Skinner; Helen Skouteris Journal: J Med Syst Date: 2020-10-19 Impact factor: 4.460
Authors: Alice Noblin; Kendall Cortelyou-Ward; John Cantiello; Thomas Breyer; Leonardo Oliveira; Mariana Dangiolo; Maria Cannarozzi; Tina Yeung; Stephen Berman Journal: J Med Syst Date: 2013-06-19 Impact factor: 4.460