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Data envelopment analysis applications in primary health care: a systematic review.
Family Practice 2019 October 13
BACKGROUND: Strategic management of primary health care centres is necessary for creating an efficient global health care system that delivers good care.
OBJECTIVES: To perform a systematic literature review of the use of data envelopment analysis in estimating the relative technical efficiency of primary health care centres, and to identify the inputs, outputs and models used.
METHODS: PubMed, MEDLINE Complete, Embase and Web of Science were searched for papers published before the 25 March 2019.
RESULTS: Of a total of 4231 search results, 54 studies met the inclusion criteria. The identified inputs included personnel costs, gross expenditures, referrals and days of hospitalization, as well as prescriptions and investigations. Outputs included consultations or visits, registered patients, procedures, treatments and services, prescriptions and investigations. A variety of data envelopment analysis models used was identified, with no standard approach.
CONCLUSIONS: Data envelopment analysis extends the scope of tools used to analyse primary care functioning. It can support health economic analyses when assessing primary care efficiency. The main issues are setting outputs and inputs and selecting a model best suited for the range of products and services in the primary health care sector. This article serves as a step forward in the standardization of data envelopment analysis, but further research is needed.
OBJECTIVES: To perform a systematic literature review of the use of data envelopment analysis in estimating the relative technical efficiency of primary health care centres, and to identify the inputs, outputs and models used.
METHODS: PubMed, MEDLINE Complete, Embase and Web of Science were searched for papers published before the 25 March 2019.
RESULTS: Of a total of 4231 search results, 54 studies met the inclusion criteria. The identified inputs included personnel costs, gross expenditures, referrals and days of hospitalization, as well as prescriptions and investigations. Outputs included consultations or visits, registered patients, procedures, treatments and services, prescriptions and investigations. A variety of data envelopment analysis models used was identified, with no standard approach.
CONCLUSIONS: Data envelopment analysis extends the scope of tools used to analyse primary care functioning. It can support health economic analyses when assessing primary care efficiency. The main issues are setting outputs and inputs and selecting a model best suited for the range of products and services in the primary health care sector. This article serves as a step forward in the standardization of data envelopment analysis, but further research is needed.
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