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

Abstract

Background: A simple indicator of technical efficiency, such as productivity of health workers, measured using routine health facility data, can be a practical approach that can inform initiatives to improve efficiency in low- and middle-income countries. This paper presents a proof of concept of using routine information from primary healthcare (PHC) facilities to measure health workers' productivity and its application in three regions of Ethiopia.

Methods: In four steps, we constructed a productivity measure of the health workforce of Health Centers (HCs) and demonstrated its practical application: (1) developing an analytical dataset using secondary data from health management information systems (HMIS) and human resource information system (HRIS); (2) principal component analysis and factor analysis to estimate a summary measure of output from five indicators (annual service volume of outpatient visits, family planning, first antenatal care visits, facility-based deliveries by skilled birth attendants, and children [< 1 year] with three pentavalent vaccines); (3) calculating a productivity score by combining the summary measure of outputs and the total number of health workers (input), and (4) implementing regression models to identify the determinant of productivity and ranking HCs based on their adjusted productivity score.

Results: We developed an analytical dataset of 1128 HCs; however, significant missing values and outliers were reported in the data. The principal component and factor scores developed from the five output measures were highly consistent (correlation coefficient = 0.98). We considered the factor score as the summary measure of outputs for estimating productivity. A very weak association was observed between the summary measure of output and the total number of staff. The result also highlighted a large variability in productivity across similar health facilities in Ethiopia, represented by the significant dispersion in summary measure of output occurring at similar levels of the health workers.

Conclusions: We successfully demonstrated the analytical steps to estimate health worker productivity and its practical application using HMIS and HRIS. The methodology presented in this study can be readily applied in low- and middle-income countries using widely available data-such as DHIS2-that will allow further explorations to understand the causes of technical inefficiencies in the health system.

Keywords: Efficiency; Ethiopia; Factor analysis; Health centers; Health information management system; Health personnel; Low- and middle-income countries; Primary healthcare; Productivity; Technical efficiency.

Fig. 1

A simplified diagram presenting the health service provision process and indicators of technical efficiency. Note: The framework is adapted from Cylus et al., 2017; * Cost per quality-adjusted life year is an allocative efficiency indicator

Fig. 2

Development of the composite index of output. Note Principal component analysis and factor analysis was used to develop the composite index of output

Fig. 3

Distribution of the output and input measures from the health centers (N = 1128)

Fig. 4

Consistency between the summary measure of outputs—PCA and FA measures. Note The red line indicates the locally weighted smoothing (lowess) curve

Fig. 5

Relationship of total staff and summary measure of outputs of the health centers. Note (b) and (c) examine the staff and output relationship at lower staffing levels in more detail. For the ease of visualization, the range of total staff and output was truncated from 0 to 40. The red line indicates the locally weighted smoothing (lowess) curve. The density curves in (b) and (c) represent the observations between the sections of 0–10, 11–20, 21–30, and 31–40

Fig. 6

Relationship of the productivity with the summary measure of outputs and total staffs. Note Red lines indicate the locally weighted smoothing (lowess) curve for the total sample, the black dashed line represents the average productivity score of the health centers (0.98)

Fig. 7

Ranking of health centers, woredas, and zones based on health centers productivity. Note The red line indicates the mean adjusted productivity of the health workforce at the level of health centers (0.86), woredas (1.01), and zones (1.01)