TY - GEN
T1 - Simulating triage of patients into an internal medicine department to validate the use of an optimization-based workload score
AU - Agor, Joseph
AU - McKenzie, Kendall
AU - Mayorga, Maria E.
AU - Ozaltin, Osman
AU - Parikh, Riddhi S.
AU - Huddleston, Jeanne
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - This study describes a simulation model that was used to evaluate a proposed workload score. The score was designed to assist in triaging patients into the hospital services of the Division of Hospital Internal Medicine at Mayo Clinic in an effort to more equitably balance workload among the division's provider teams (or services). The first part of this study was the development of a score, using Delphi surveys, conjoint analysis, and optimization methods, that accurately represents provider workload. A simulation model was then built to test the score using historical patient data. Preliminary simulation results reported the proportion of time that each provider team spent working at or above 'maximum utilization,' as defined by Mayo Clinic experts. The model yielded a 12.1% decrease (on average) in the proportion of time provider teams spent at or above maximum utilization, while simultaneously displaying a more balanced workload across provider teams.
AB - This study describes a simulation model that was used to evaluate a proposed workload score. The score was designed to assist in triaging patients into the hospital services of the Division of Hospital Internal Medicine at Mayo Clinic in an effort to more equitably balance workload among the division's provider teams (or services). The first part of this study was the development of a score, using Delphi surveys, conjoint analysis, and optimization methods, that accurately represents provider workload. A simulation model was then built to test the score using historical patient data. Preliminary simulation results reported the proportion of time that each provider team spent working at or above 'maximum utilization,' as defined by Mayo Clinic experts. The model yielded a 12.1% decrease (on average) in the proportion of time provider teams spent at or above maximum utilization, while simultaneously displaying a more balanced workload across provider teams.
UR - http://www.scopus.com/inward/record.url?scp=85044522866&partnerID=8YFLogxK
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U2 - 10.1109/WSC.2017.8248011
DO - 10.1109/WSC.2017.8248011
M3 - Conference contribution
AN - SCOPUS:85044522866
T3 - Proceedings - Winter Simulation Conference
SP - 2881
EP - 2892
BT - 2017 Winter Simulation Conference, WSC 2017
A2 - Chan, Victor
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Winter Simulation Conference, WSC 2017
Y2 - 3 December 2017 through 6 December 2017
ER -