TY - JOUR
T1 - Target times for inpatient discharge scheduling
AU - Matis, Timothy
AU - Farris, Jennifer
AU - McAllister, Marlene
AU - Dunavan, Chad
AU - Snider, Alan
N1 - Publisher Copyright:
© 2015, Copyright © “IIE”.
PY - 2015/1/2
Y1 - 2015/1/2
N2 - The discharge scheduling process presented in this paper proposes a new paradigm for discharging inpatients from a hospital and was developed through collaboration with the Medical Center Hospital in Odessa, Texas. An optimization model that incorporates systematic capacities and patient preferences into generating target discharge times for patients is presented together with a system design to achieve the practical implementation of such. The mathematical model is an extension of an assignment model whose inputs and outputs may be incorporated into patient tracking software through a series of queries. The model considers physician loads and rounding patterns, available transporters, nursing staff workloads, and patient characteristics and preferences in assigning a time for which a patient is scheduled for discharge. In addition, the model has built in the ability to relax constraints so as to prevent solution infeasibility in practice.
AB - The discharge scheduling process presented in this paper proposes a new paradigm for discharging inpatients from a hospital and was developed through collaboration with the Medical Center Hospital in Odessa, Texas. An optimization model that incorporates systematic capacities and patient preferences into generating target discharge times for patients is presented together with a system design to achieve the practical implementation of such. The mathematical model is an extension of an assignment model whose inputs and outputs may be incorporated into patient tracking software through a series of queries. The model considers physician loads and rounding patterns, available transporters, nursing staff workloads, and patient characteristics and preferences in assigning a time for which a patient is scheduled for discharge. In addition, the model has built in the ability to relax constraints so as to prevent solution infeasibility in practice.
KW - Patient flow
KW - healthcare management
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=84981240703&partnerID=8YFLogxK
U2 - 10.1080/19488300.2014.993445
DO - 10.1080/19488300.2014.993445
M3 - Article
AN - SCOPUS:84981240703
SN - 1948-8300
VL - 5
SP - 33
EP - 41
JO - IIE Transactions on Healthcare Systems Engineering
JF - IIE Transactions on Healthcare Systems Engineering
IS - 1
ER -