Muge Capan1, Julie S Ivy2, Thomas Rohleder3, Joel Hickman3, Jeanne M Huddleston4. 1. Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Campus Box 7906, Raleigh, NC 27695-7906, USA; Health Care Delivery Science, Value Institute, Christiana Care Health System, Newark, DE 19718, USA. 2. Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Campus Box 7906, Raleigh, NC 27695-7906, USA. 3. Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN 55905, USA; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA. 4. Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN 55905, USA; Division of Hospital Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: huddleston.jeanne@mayo.edu.
Abstract
AIM: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. METHODS: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). RESULTS: Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. CONCLUSIONS: EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.
AIM: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. METHODS: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). RESULTS: Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. CONCLUSIONS: EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.
Authors: Alexandra Lang; Mark Simmonds; James Pinchin; Sarah Sharples; Lorrayne Dunn; Susan Clarke; Owen Bennett; Sally Wood; Caron Swinscoe Journal: JMIR Med Inform Date: 2019-03-06