A practical measure of health facility efficiency: an innovation in the application of routine health information to determine health worker productivity in Ethiopia

Costs and resource needs for primary health care in Ethiopia: evidence to inform planning and budgeting for universal health coverage

Introduction

The Government of Ethiopia (GoE) has made significant progress in expanding access to primary health care (PHC) over the past 15 years. However, achieving national PHC targets for universal health coverage will require a significant increase in PHC financing. The purpose of this study was to generate cost evidence and provide recommendations to improve PHC efficiency.

Methods

We used the open access Primary Health Care Costing, Analysis, and Planning (PHC-CAP) Tool to estimate actual and normative recurrent PHC costs in nine Ethiopian regions. The findings on actual costs were based on primary data collected in 2018/19 from a sample of 20 health posts, 25 health centers, and eight primary hospitals. Three different extrapolation methods were used to estimate actual costs in the nine sampled regions. Normative costs were calculated based on standard treatment protocols (STPs), the population in need of the PHC services included in the Essential Health Services Package (EHSP) as per the targets outlined in the Health Sector Transformation Plan II (HSTP II), and the associated costs. PHC resource gaps were estimated by comparing actual cost estimates to normative costs.

Results

On average, the total cost of PHC in the sampled facilities was US$ 11,532 (range: US$ 934-40,746) in health posts, US$ 254,340 (range: US$ 68,860-832,647) in health centers, and US$ 634,354 (range: US$ 505,208-970,720) in primary hospitals. The average actual PHC cost per capita in the nine sampled regions was US$ 4.7, US$ 15.0, or US$ 20.2 depending on the estimation method used. When compared to the normative cost of US$ 38.5 per capita, all these estimates of actual PHC expenditures were significantly lower, indicating a shortfall in the funding required to deliver an expanded package of high-quality services to a larger population in line with GoE targets.

Discussion

The study findings underscore the need for increased mobilization of PHC resources and identify opportunities to improve the efficiency of PHC services to meet the GoE's PHC targets. The data from this study can be a critical input for ongoing PHC financing reforms undertaken by the GoE including transitioning woreda-level planning from input-based to program-based budgeting, revising community-based health insurance (CBHI) packages, reviewing exempted services, and implementing strategic purchasing approaches such as capitation and performance-based financing.

Measuring and improving performance of clinicians: an application of patient-based records

BACKGOUND

Efforts to measure performance and identify its driving factors among clinicians are needed for building a high-quality clinician workforce. The availability of data is the most challenging thing. This paper presented a summary performance measure for clinicians and its application on examining factors that influence performance using routine patient-based records.

METHODS

Perfomance indicators and difficulty score were extracted from electronic medical records (EMRs). Difficulty adjustment and standardized processing were used to obtain indicators which were comparable between specialties. Principal component analysis (PCA) was used to estimate the summary performance measure. The performance measure was then used to examine the influence of person-job fit and burnout through a mediator effect model and cluster analysis.

RESULTS

A valid sample of 404 clinicians were included in this study, and 244 of them had valid response in the questionnaire. PCA explained 79.37% of the total variance presented by the four adjusted performance indicators. Non-performance attributes and performance driving factors help distinguish different clusters of clinicians. Burnout mediates the relationship between person-job fit and performance in a specific group of clinicians (β = 0.120, p = 0.008).

CONCLUSIONS

We demonstrated the analytical steps to estimate clinicians' performance and its practical application using EMRs. Our findings provide insight into personnel classified management. Such practice can be applied in countries where electronic medical record systems are relatively less developed to continuously improve the application of performance management.