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ORIGINAL ARTICLE
Year : 2019  |  Volume : 6  |  Issue : 1  |  Page : 24-31

Evaluation of technical efficiency of county referral hospitals in kenya and its determinants


1 Department of Health Records and Information Management, College of Health Sciences, Mount Kenya University, Thika, Kenya
2 Department of Applied Economics, Kenyatta University, Nairobi, Kenya
3 Departments of Health Management and Informatics, Kenyatta University, Nairobi, Kenya

Correspondence Address:
Mr. Gilbert Koome Rithaa
College of Health Sciences, Mount Kenya University, Thika
Kenya
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAMR.IJAMR_80_18

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Background: Kenya's gross national income per capita of $ 2250 is significantly lower than the global average of $ 6977. In addition, Kenya Government's health expenditure as a percent of the total government budget is approximately 7% which falls below the target of 15% recommended by the World Health Organization. It is, therefore, important that the country's health-care resources, specifically those allocated to the health sector, are optimally used. Methods: An one-stage data envelopment analysis (DEA) method was used to estimate the technical efficiency of county referral hospitals. A total of 34 county referral hospitals were randomly sampled and studied. Data analysis was performed in two stages as follows: first, input and output data were entered into MS Excel sheet after which DEA version 2.1 was used to determine the technical efficiency scores for the hospitals. In the second stage, interval regression analysis using censored interval regression model was used to identify determinants of technical efficiency for the sampled hospitals. Results: Results indicated that the mean constant return to scale technically efficient score was 82.4%, the mean variable return to scale (VRS) technically efficient score was 94.1%, and the mean scale efficiency technical score was 87.4%. The mean level of VRS technical inefficiency was 17.2%. The total inputs slacks in the inefficient hospitals were 104 beds and 840 staff which represented an input slack of 4% for the beds and 28% for the staff. Conclusions: Inefficient hospitals could have attained efficient frontiers using fewer resources, specifically 4% beds and 28% staff. The technical inefficiencies in county referral hospitals are occasioned by the use of inappropriate production functions characterized by the existence of excess production inputs and suboptimal outputs.


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