Health workers are the backbone of any health system. When the health workforce is not aligned with demand at a particular facility it can lead to long patient waiting times, poor quality or lack of services, and overworked health workers. While many countries are aware that they face a general shortage of health workers, it can be difficult to quantify exactly how many are needed to fill the gap.
Determining the number of health workers needed is critical, but it is also essential to understand where they are needed most. For example, urban areas may have a heavy concentration of health workers, while rural areas have very few relative to the population size. Or, certain regions of the country may have better health worker to population ratios.
In light of these potential issues, governments must consider how health workers can best be distributed throughout the country.
Not all types of health workers offer the same skills and abilities within the facilities where they work, and each health facility or area must have an appropriate representation of various skills in order to meet patient demand. For example, some health facilities may have plenty of nurses and environmental health technicians to support health promotion and the treatment of basic illnesses, but there may be no health worker that is trained to manage complications in childbirth or complex problems related to medication side effects. That is why governments must also consider how to achieve the right mix of health workers and skills.
In order to address these questions regarding the size, distribution, and skill mix of the health workforce, CHAI partnered with the Ministry of Health (MOH) of Zambia beginning in 2008. While it may have been possible to do an in-depth assessment of each facility to respond to these evidence needs, the government was interested in adopting a more high-level approach that could be applied consistently across the whole country and repeated in future years. The result of this collaboration was the development of a Workforce Optimization Model (WFOM).
This innovative model uses existing government data sources, along with expert consultations and facility observations, to calculate the optimal number of health workers needed in each administrative district or facility to meet demand for services. The data collected includes the number of health services provided, activity times and roles, health worker time, and the scale of the current workforce. These optimal staffing levels can then be compared with actual and funded staffing levels.
When this model was first deployed in Zambia, it helped to inform decisions between 2009 and 2011 about where to send 949 new health workers. Instead of being deployed at random, or just to the facilities making requests, health workers were sent to districts with fewer than one health worker per 1,000 people. The addition of these health workers increased overall staffing by 25% in those areas.
To better understand how the addition of these health workers changed the number of health services provided per facility, CHAI conducted an analysis in 2012. The findings, recently published in Health Affairs, showed that at facilities with low staffing levels in 2009, the addition of a skilled provider was associated with an additional 103 outpatient consultations per quarter — a 15% increase in service delivery. In facilities with a high volume of prenatal care visits, adding a skilled health worker was associated with a 10% increase in births at medical facilities.
Due to the success of the model in Zambia (which it has since updated to get a more current understanding of staffing requirements in the country), CHAI has helped adapt its use in Lesotho, Swaziland, and Malawi. It has also been used to estimate the increase in health workers needed to accommodate policy changes such the expansion of eligibility criteria for HIV treatment.
The WFOM can be a useful tool for governments when making decisions about how to manage the health workforce in support of broader health goals. Using existing national data sources to inform decisions is an important way to not only produce better policies but also to improve the quality of the data sources themselves.
