Emmanuelle Daviaud1, Mickey Chopra. 1. Health System Research Unit, Medical Research Council, Western Cape, South Africa. emadav@mweb.co.za
Abstract
OBJECTIVE: To quantify staff requirements in primary health care facilities in South Africa through an adaptation of the WHO workload indicator of staff needs tool. METHODS: We use a model to estimate staffing requirements at primary health care facilities. The model integrates several empirically-based assumptions including time and type of health worker required for each type of consultation, amount of management time required, amount of clinical support required and minimum staff requirements per type of facility. We also calculate the number of HIV-related consultations per district. The model incorporates type of facility, monthly travelling time for mobile clinics, opening hours per week, yearly activity and current staffing and calculates the expected staffing per category of staff per facility and compares it to the actual staffing. FINDINGS: Across all the districts there is either an absence of doctors visiting clinics or too few doctors to cover the opening times of community health centres. Overall the number of doctors is only 7% of the required amount. There is 94% of the required number of professional nurses but with wide variations between districts, with a few districts having excesses while most have shortages. The number of enrolled nurses is 60% of what it should be. There are 17% too few enrolled nurse assistants. Across all districts there is wide variation in staffing levels between facilities leading to inefficient use of professional staff. CONCLUSION: The application of an adapted WHO workload tool identified important human resource planning issues.
OBJECTIVE: To quantify staff requirements in primary health care facilities in South Africa through an adaptation of the WHO workload indicator of staff needs tool. METHODS: We use a model to estimate staffing requirements at primary health care facilities. The model integrates several empirically-based assumptions including time and type of health worker required for each type of consultation, amount of management time required, amount of clinical support required and minimum staff requirements per type of facility. We also calculate the number of HIV-related consultations per district. The model incorporates type of facility, monthly travelling time for mobile clinics, opening hours per week, yearly activity and current staffing and calculates the expected staffing per category of staff per facility and compares it to the actual staffing. FINDINGS: Across all the districts there is either an absence of doctors visiting clinics or too few doctors to cover the opening times of community health centres. Overall the number of doctors is only 7% of the required amount. There is 94% of the required number of professional nurses but with wide variations between districts, with a few districts having excesses while most have shortages. The number of enrolled nurses is 60% of what it should be. There are 17% too few enrolled nurse assistants. Across all districts there is wide variation in staffing levels between facilities leading to inefficient use of professional staff. CONCLUSION: The application of an adapted WHO workload tool identified important human resource planning issues.
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