Using information to improve allocation and management of HRH: the Zambia optimization model
Author: Ministry of Health, Zambia
Country: Zambia
Challenges
Zambia has a large land area but a relatively small population (12.9 million), which inequity of access to health services in the rural areas. Zambia has 7 healthcare workers per 10,000 in the rural areas but 15.9 healthcare workers per 10,000 in the urban areas. The average vacancy rate is 61% among public sector doctors, nurses, clinical officers and midwives. A major contributor to the HRH crisis is the inability to train enough staff. This is primarily due to financial constraints.
Policy description
In 2006-2010, as Zambia implemented its HRH Strategic Plan, it identified the need to improve data management in order to institutionalise evidence-based decision-making and enhance shared learning. Zambia recognized that evidenced-based decision tools could help answer the following HRH questions:
- Given the limited financial resources, what cadres should be invested most heavily in?
- Where are health services in greatest demand relative to the staffing levels?
- How can financial resources be most effectively mobilized to align with Ministry of Health (MoH) priorities?
To answer these questions an Optimization model using the MoH Health Management Information System data was created. The model calculates the number and type of health workers needed at each facility to meet the actual health demands. It is an integrated tool to estimate “optimal” size, composition, and distribution of health care workers (HCWs) for every public health facility in Zambia, based on the level of service utilization at each facility.
Outcomes
The Optimization Model provides data for every level of the health sector. At the summary level it provides vacancy rates and target staffing levels for HRH at national, provincial and district levels (Figure 1).
At the Deployment Prioritization level, it ranks each of Zambia’s 72 districts by extent of need for additional HRH. This is shown in Figure 2 below, which illustrates that some districts are in critical need while others have moderate need.
At Facility Prototypes Level the model depicts current, funded and optimal staffing targets for doctors, clinical officers, midwives, nurses, pharmacists and lab specialists for every public health facility in Zambia, as shown in Figure 3.
The optimization analysis helped prioritize the deployment of new HCWs against vacancies and gap areas, which led to faster absorption of graduates into the public healthcare workforce and the decongestion of the central Ministry so that it can focus on strategic and policy-level decisions. Zambia has hosted two formalized recruitment processes and using the Optimization model has posted over 1,200 students and sent over 50% of graduates to areas in greatest need. 94% of healthcare workers posted through the formalized process remain at their posts.
Conclusions
Although it is too early to measure the impact of the model, it is expected to contribute to improved access to health providers in areas that have the highest demand for health services and reduced disparity between urban and rural health worker distribution. It has also helped with efficient resource usage to by prioritizing cadres for training. Ultimately, these should improve health indicators where supply of HCWs meets demand and contribute towards meeting the MDGs.