TY - JOUR
T1 - A practical measure of health facility efficiency
T2 - an innovation in the application of routine health information to determine health worker productivity in Ethiopia
AU - Hasan, Md Zabir
AU - Dinsa, Girmaye D.
AU - Berman, Peter
N1 - Funding Information:
This work was supported by the Bill & Melinda Gates Foundation through the Disease Control Priorities-Ethiopia project [Grant No. OPP1162384] and Fenot-Harvard T. H. Chan School of Public Health, Ethiopia Project [Grant No. OPP1135922]. Following Dr. Berman’s relocation, the Fenot grant has been administrated by UBC since July 1, 2020. However, the donor was not involved in the research nor writing of this manuscript.
Funding Information:
We want to thank the Partnership and Cooperation Directorate (PCD) and the Policy and Planning Directorate (PPD) of the Federal Ministry of Health, Ethiopia, for their support with the HMIS and HRIS data. We are also grateful to Shelly Keidar, our research manager of the Fenot project, for valuable comments and edits on the manuscript.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Efficiency
KW - Ethiopia
KW - Factor analysis
KW - Health centers
KW - Health information management system
KW - Health personnel
KW - Low- and middle-income countries
KW - Primary healthcare
KW - Productivity
KW - Technical efficiency
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U2 - 10.1186/s12960-021-00636-6
DO - 10.1186/s12960-021-00636-6
M3 - Article
C2 - 34353335
AN - SCOPUS:85112640934
SN - 1478-4491
VL - 19
JO - Human resources for health
JF - Human resources for health
IS - 1
M1 - 96
ER -