TY - JOUR
T1 - Constrained Optimization Methods in Health Services Research—An Introduction
T2 - Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force
AU - Crown, William
AU - Buyukkaramikli, Nasuh
AU - Thokala, Praveen
AU - Morton, Alec
AU - Sir, Mustafa Y.
AU - Marshall, Deborah A.
AU - Tosh, Jon
AU - Padula, William V.
AU - Ijzerman, Maarten J.
AU - Wong, Peter K.
AU - Pasupathy, Kalyan S.
N1 - Publisher Copyright:
© 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR)
PY - 2017/3/13
Y1 - 2017/3/13
N2 - Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of “regular” and “severe” patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.
AB - Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of “regular” and “severe” patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.
UR - http://www.scopus.com/inward/record.url?scp=85015046068&partnerID=8YFLogxK
U2 - 10.1016/j.jval.2017.01.013
DO - 10.1016/j.jval.2017.01.013
M3 - Article
C2 - 28292475
AN - SCOPUS:85015046068
SN - 1098-3015
VL - 20
SP - 310
EP - 319
JO - Value in Health
JF - Value in Health
IS - 3
ER -