TY - JOUR
T1 - Incorporating Patient Travel Times in Decisions about Size and Location of Dialysis Facilities
AU - Eben-Chaime, Moshe
AU - Pliskin, Joseph S.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - This report demonstrates the power and usefulness of mathematical optimization as a de cision support tool in the medical services industry by presenting an application to dialysis service planning Models to predict the number of dialysis beds in a given region are usually population-based. Dialysis planners and providers have found a need to accommodate sparsely populated regions by making some allowance for patient travel times. A formal approach to incorporating travel times into dialysis planning, based on the formulation and solution of a mixed-integer programming model, is presented The development of a method for dialysis planning serves as a platform to demonstrate the use of integer programming to support decision making Major modeling principles are presented, output interpretation and sensitivity analysis are illustrated by examples; and computational requirements are dis cussed Key words. dialysis need forecasting, population-based model, travel time; math ematical optimization, mixed-integer programming; location, allocation (Med Decis Making 1992;12:44-51)
AB - This report demonstrates the power and usefulness of mathematical optimization as a de cision support tool in the medical services industry by presenting an application to dialysis service planning Models to predict the number of dialysis beds in a given region are usually population-based. Dialysis planners and providers have found a need to accommodate sparsely populated regions by making some allowance for patient travel times. A formal approach to incorporating travel times into dialysis planning, based on the formulation and solution of a mixed-integer programming model, is presented The development of a method for dialysis planning serves as a platform to demonstrate the use of integer programming to support decision making Major modeling principles are presented, output interpretation and sensitivity analysis are illustrated by examples; and computational requirements are dis cussed Key words. dialysis need forecasting, population-based model, travel time; math ematical optimization, mixed-integer programming; location, allocation (Med Decis Making 1992;12:44-51)
UR - http://www.scopus.com/inward/record.url?scp=0026595917&partnerID=8YFLogxK
U2 - 10.1177/0272989X9201200108
DO - 10.1177/0272989X9201200108
M3 - Article
AN - SCOPUS:0026595917
VL - 12
SP - 44
EP - 51
JO - Medical Decision Making
JF - Medical Decision Making
SN - 0272-989X
IS - 1
ER -