Capacity in Thai public hospitals and the production of care for poor and nonpoor patients

Type Journal Article - Health Services Research
Title Capacity in Thai public hospitals and the production of care for poor and nonpoor patients
Author(s)
Volume 39
Issue 6p2
Publication (Day/Month/Year) 2004
Page numbers 2117-2134
URL http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1361115/
Abstract
Objective

To assess the capacity of Thai public hospitals to proportionately expand services to both the poor and the nonpoor. This is accomplished by measuring the production of services provided to poor, relative to nonpoor, patients and the plant capacity of individual public hospitals to care for the patient load.

Study Setting

Thai public hospitals operating in 1999, following the economic crisis when public hospitals were required to treat all patients irrespective of ability to pay.

Study Design and Data Collection

Input and output data for 68 hospitals were collected using databases and questionnaire surveys. A distinction was made between inpatient and outpatient services to both poor and nonpoor patients and the data were assessed statistically.

Data Analysis

Congestion and capacity indices to measure poor/nonpoor service trade-offs and capacity utilization were estimated. The analysis was undertaken by data envelopment analysis (DEA), a nonparametric linear programming approach used to derive efficiency and productivity estimates.

Principal Findings

Increases in the amount of services provided to poor patients did not reduce the amount of services to nonpoor patients. Overall, hospitals are producing services relatively close to their capacity given fixed inputs. Possible increases in capacity utilization amounted to 5 percent of capacity.

Conclusions

Results suggest that some increased public hospital care can be accomplished by reallocation of resources to less highly utilized hospitals, given the budgetary constraints. However, further expansion and increase in access to health services will require plant investments. The study illustrates how DEA methodologies can be used in planning health services in data constrained settings.

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