James P d'Etienne1, Yuan Zhou2, Chen Kan3, Sajid Shaikh4, Amy F Ho5, Eniola Suley6, Erica C Blustein7, Chet D Schrader8, Nestor R Zenarosa9, Hao Wang10. 1. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA. Electronic address: jdetienn@jpshealth.org. 2. Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 701 S. Nedderman Dr., Arlington, TX 760199, USA. Electronic address: yuan.zhou@uta.edu. 3. Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 701 S. Nedderman Dr., Arlington, TX 760199, USA. Electronic address: chen.kan@uta.edu. 4. Department of Information Technology, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA. Electronic address: sshaikh@jpshealth.org. 5. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA. Electronic address: aho02@jpshealth.org. 6. Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 701 S. Nedderman Dr., Arlington, TX 760199, USA. Electronic address: eniola.suley@uta.edu. 7. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA. Electronic address: eblustei@jpshealth.org. 8. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA; Integrative Emergency Services, 4835 LBJ Fwy Suite 900, Dallas, TX 75244, USA. Electronic address: cschrade@jpshealth.org. 9. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA; Integrative Emergency Services, 4835 LBJ Fwy Suite 900, Dallas, TX 75244, USA. Electronic address: nzenarosa@ies.healthcare. 10. Department of Emergency Medicine, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA; Integrative Emergency Services, 4835 LBJ Fwy Suite 900, Dallas, TX 75244, USA. Electronic address: hwang@ies.healthcare.
Abstract
OBJECTIVE: To develop a novel model for predicting Emergency Department (ED) prolonged length of stay (LOS) patients upon triage completion, and further investigate the benefit of a targeted intervention for patients with prolonged ED LOS. MATERIALS AND METHODS: A two-step model to predict patients with prolonged ED LOS (>16 h) was constructed. This model was initially used to predict ED resource usage and was subsequently adapted to predict patient ED LOS based on the number of ED resources using binary logistic regressions and was validated internally with accuracy. Finally, a discrete event simulation was used to move patients with predicted prolonged ED LOS directly to a virtual Clinical Decision Unit (CDU). The changes of ED crowding status (Overcrowding, Crowding, and Not-Crowding) and savings of ED bed-hour equivalents were estimated as the measures of the efficacy of this intervention. RESULTS: We screened a total of 123,975 patient visits with final enrollment of 110,471 patient visits. The overall accuracy of the final model predicting prolonged patient LOS was 67.8%. The C-index of this model ranges from 0.72 to 0.82. By implementing the proposed intervention, the simulation showed a 12% (1044/8760) reduction of ED overcrowded status - an equivalent savings of 129.3 ED bed-hours per day. CONCLUSIONS: Early prediction of prolonged ED LOS patients and subsequent (simulated) early CDU transfer could lead to more efficiently utilization of ED resources and improved efficacy of ED operations. This study provides evidence to support the implementation of this novel intervention into real healthcare practice.
OBJECTIVE: To develop a novel model for predicting Emergency Department (ED) prolonged length of stay (LOS) patients upon triage completion, and further investigate the benefit of a targeted intervention for patients with prolonged ED LOS. MATERIALS AND METHODS: A two-step model to predict patients with prolonged ED LOS (>16 h) was constructed. This model was initially used to predict ED resource usage and was subsequently adapted to predict patient ED LOS based on the number of ED resources using binary logistic regressions and was validated internally with accuracy. Finally, a discrete event simulation was used to move patients with predicted prolonged ED LOS directly to a virtual Clinical Decision Unit (CDU). The changes of ED crowding status (Overcrowding, Crowding, and Not-Crowding) and savings of ED bed-hour equivalents were estimated as the measures of the efficacy of this intervention. RESULTS: We screened a total of 123,975 patient visits with final enrollment of 110,471 patient visits. The overall accuracy of the final model predicting prolonged patient LOS was 67.8%. The C-index of this model ranges from 0.72 to 0.82. By implementing the proposed intervention, the simulation showed a 12% (1044/8760) reduction of ED overcrowded status - an equivalent savings of 129.3 ED bed-hours per day. CONCLUSIONS: Early prediction of prolonged ED LOS patients and subsequent (simulated) early CDU transfer could lead to more efficiently utilization of ED resources and improved efficacy of ED operations. This study provides evidence to support the implementation of this novel intervention into real healthcare practice.
Authors: Yu-Hsin Chang; Hong-Mo Shih; Jia-En Wu; Fen-Wei Huang; Wei-Kung Chen; Dar-Min Chen; Yu-Ting Chung; Charles C N Wang Journal: BMC Emerg Med Date: 2022-05-20