Integration of Facility Location and Equipment Allocation in Health Care Management

Abstract

Goal: This study aims at solving a location problem of Medical Specialties Centers (MSCs) and medical care equipment allocation. Addressing both problems simultaneously is an opportunity to improve public health service quality in the long-term since the literature traditionally treats these problems separately.
 Design / Methodology / Approach: The challenge consists in maximizing demand satisfaction with the minimum resource allocation in the public health care reality, where there is limited resource availability and high demand for medical services. For that, it was developed an integrated mathematical model, throughout mixed linear programming. The problem is a case study applied to the secondary public health care level in a Brazilian state. The method is generic and suitable to set the location and allocate resources in health care if the decision maker’s intention is to maximize the use of specialists’ assistance and medical exams.
 Results: The results reveal possible improvements in accessibility. Among the insights, the state government should hire 4% more specialists and acquire 1.5% more equipment to assist 99% of population demand for health care service on the secondary level. 
 Limitation of the investigation: Usually, one appointment can result in more than one medical exam referral. However, this study considers that each specialist meeting refers to only one exam per consultation. 
 Practical implications: This study contributes to healthcare planning, suggesting a better distribution and allocation of facilities, equipment, and professionals. Moreover, the study proposes accessibility improvements to health unit centers.
 Originality / Value: The main contribution of this work is the new integrated approach to public health care planning. The system proposes the reduction of access inequality and the improvement of the quality of health care services.

Techno-Economic Feasibility Analysis of a Fully Mobile Radiation Oncology System Using Monte Carlo Simulation

“…There are resources available to help optimize the location of a health care facility that can be applied in project planning. [25][26][27][28][29] Since the machine is built into the truck, accepted, and commissioned all before the first treatment, during setup, only connections, level adjustments, and a series of constancy checks are needed to demonstrate that the machine meets baseline performance standards set at the time of commissioning. Not incorporating the lead into the L-truck requires the need for a C-truck for safety issues, which introduces higher logistical and risk concerns but will help increase the temporal mobility of the system and reach a larger patient population Over time, the linac may incur more wear from repeated setup and travel damages.…”

An artificial neural network based mathematical model for a stochastic health care facility location problem

“…They proved that the proposed method is valid and competitive compared to the alternative methods. Sathler et al, (2019) presented a new integrated approach to the public health care planning. They proposed an integrated mathematical model to locate the health care centers and allocate the equipment in order to maximize the demand satisfaction with the minimum resource allocation.…”

Medical centers location and specialists’ allocation: a healthcare planning case study

“…Additional applications consists on the development of a system to optimize the equipment allocation (Treurnicht & Van Dyk, 2014); the tactical workforce planning and capacity allocation minimizing salary costs considering vacation and subcontracting opportunities (Van der Veen et al, 2015); and a performance evaluation of 87 private and public primary care health units (Novignon & Nonvignon, 2017). On health care location-allocation problem, examples include the location of preventive health care units (Gu et al, 2010), two-phase procedures to set the location and size of medical departments (Stummer et al, 2004), and for improving spatial accessibility (Luo et al, 2017), and an integrated approach aiming inequality reduction (Sathler et al, 2019).…”