Cargando…
Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries
BACKGROUND: The strength of a health system—and ultimately the health of a population—depends to a large degree on health worker performance. However, insufficient support to build, manage and optimize human resources for health (HRH) in low- and middle-income countries (LMICs) results in inadequate...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733429/ https://www.ncbi.nlm.nih.gov/pubmed/34991604 http://dx.doi.org/10.1186/s12960-021-00692-y |
_version_ | 1784627802904985600 |
---|---|
author | Deussom, Rachel Mwarey, Doris Bayu, Mekdelawit Abdullah, Sarah S. Marcus, Rachel |
author_facet | Deussom, Rachel Mwarey, Doris Bayu, Mekdelawit Abdullah, Sarah S. Marcus, Rachel |
author_sort | Deussom, Rachel |
collection | PubMed |
description | BACKGROUND: The strength of a health system—and ultimately the health of a population—depends to a large degree on health worker performance. However, insufficient support to build, manage and optimize human resources for health (HRH) in low- and middle-income countries (LMICs) results in inadequate health workforce performance, perpetuating health inequities and low-quality health services. METHODS: The USAID-funded Human Resources for Health in 2030 Program (HRH2030) conducted a systematic review of studies documenting supervision enhancements and approaches that improved health worker performance to highlight components associated with these interventions’ effectiveness. Structured by a conceptual framework to classify the inputs, processes, and results, the review assessed 57 supervision studies since 2010 in approximately 29 LMICs. RESULTS: Of the successful supervision approaches described in the 57 studies reviewed, 44 were externally funded pilots, which is a limitation. Thirty focused on community health worker (CHW) programs. Health worker supervision was informed by health system data for 38 approaches (67%) and 22 approaches used continuous quality improvement (QI) (39%). Many successful approaches integrated digital supervision technologies (e.g., SmartPhones, mHealth applications) to support existing data systems and complement other health system activities. Few studies were adapted, scaled, or sustained, limiting reports of cost-effectiveness or impact. CONCLUSION: Building on results from the review, to increase health worker supervision effectiveness we recommend to: integrate evidence-based, QI tools and processes; integrate digital supervision data into supervision processes; increase use of health system information and performance data when planning supervision visits to prioritize lowest-performing areas; scale and replicate successful models across service delivery areas and geographies; expand and institutionalize supervision to reach, prepare, protect, and support frontline health workers, especially during health emergencies; transition and sustain supervision efforts with domestic human and financial resources, including communities, for holistic workforce support. In conclusion, effective health worker supervision is informed by health system data, uses continuous quality improvement (QI), and employs digital technologies integrated into other health system activities and existing data systems to enable a whole system approach. Effective supervision enhancements and innovations should be better integrated, scaled, and sustained within existing systems to improve access to quality health care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12960-021-00692-y. |
format | Online Article Text |
id | pubmed-8733429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87334292022-01-06 Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries Deussom, Rachel Mwarey, Doris Bayu, Mekdelawit Abdullah, Sarah S. Marcus, Rachel Hum Resour Health Review BACKGROUND: The strength of a health system—and ultimately the health of a population—depends to a large degree on health worker performance. However, insufficient support to build, manage and optimize human resources for health (HRH) in low- and middle-income countries (LMICs) results in inadequate health workforce performance, perpetuating health inequities and low-quality health services. METHODS: The USAID-funded Human Resources for Health in 2030 Program (HRH2030) conducted a systematic review of studies documenting supervision enhancements and approaches that improved health worker performance to highlight components associated with these interventions’ effectiveness. Structured by a conceptual framework to classify the inputs, processes, and results, the review assessed 57 supervision studies since 2010 in approximately 29 LMICs. RESULTS: Of the successful supervision approaches described in the 57 studies reviewed, 44 were externally funded pilots, which is a limitation. Thirty focused on community health worker (CHW) programs. Health worker supervision was informed by health system data for 38 approaches (67%) and 22 approaches used continuous quality improvement (QI) (39%). Many successful approaches integrated digital supervision technologies (e.g., SmartPhones, mHealth applications) to support existing data systems and complement other health system activities. Few studies were adapted, scaled, or sustained, limiting reports of cost-effectiveness or impact. CONCLUSION: Building on results from the review, to increase health worker supervision effectiveness we recommend to: integrate evidence-based, QI tools and processes; integrate digital supervision data into supervision processes; increase use of health system information and performance data when planning supervision visits to prioritize lowest-performing areas; scale and replicate successful models across service delivery areas and geographies; expand and institutionalize supervision to reach, prepare, protect, and support frontline health workers, especially during health emergencies; transition and sustain supervision efforts with domestic human and financial resources, including communities, for holistic workforce support. In conclusion, effective health worker supervision is informed by health system data, uses continuous quality improvement (QI), and employs digital technologies integrated into other health system activities and existing data systems to enable a whole system approach. Effective supervision enhancements and innovations should be better integrated, scaled, and sustained within existing systems to improve access to quality health care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12960-021-00692-y. BioMed Central 2022-01-06 /pmc/articles/PMC8733429/ /pubmed/34991604 http://dx.doi.org/10.1186/s12960-021-00692-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Deussom, Rachel Mwarey, Doris Bayu, Mekdelawit Abdullah, Sarah S. Marcus, Rachel Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title | Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title_full | Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title_fullStr | Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title_full_unstemmed | Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title_short | Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
title_sort | systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733429/ https://www.ncbi.nlm.nih.gov/pubmed/34991604 http://dx.doi.org/10.1186/s12960-021-00692-y |
work_keys_str_mv | AT deussomrachel systematicreviewofperformanceenhancinghealthworkersupervisionapproachesinlowandmiddleincomecountries AT mwareydoris systematicreviewofperformanceenhancinghealthworkersupervisionapproachesinlowandmiddleincomecountries AT bayumekdelawit systematicreviewofperformanceenhancinghealthworkersupervisionapproachesinlowandmiddleincomecountries AT abdullahsarahs systematicreviewofperformanceenhancinghealthworkersupervisionapproachesinlowandmiddleincomecountries AT marcusrachel systematicreviewofperformanceenhancinghealthworkersupervisionapproachesinlowandmiddleincomecountries |