In developing countries, health care workers (HCWs) — doctors, nurses, pharmacists, and dentists — are as precious a commodity as gold. As Dr. Mark Rosenberg, former president and CEO of the Task Force for Global Health (TFGH) pointed out in a 2015 blog article, “In Mozambique [for example], where life expectancy is only 50 years, HCW shortages are particularly pronounced. Fewer than 1,500 doctors serve a population of 25.2 million.”

To deal with the problem of HCW shortages, the TFGH, the Centers for Disease Control and Prevention (CDC), and the Mozambique Ministry of Health (MOH) joined forces.Their efforts were supported by a team from Georgia Tech, which included graduate and undergraduate students, former ISyE faculty member Julie Swann, and was led by Professor Pinar Keskinocak, a faculty member in ISyE and the director of the Center for Health and Humanitarian Systems.

They jointly developed a solution, called the Workforce Allocation Optimization (WAO) Tool. At the core of WAO is an integer programming model, which is solved to find the best allocation of HCWs to match demand and supply, considering various constraints, objectives, and preferences.

Currently, ISyE alumna Sheereen Brown (BSIE 13, MSHS 14) serves as a business analyst for the Public Health Informatics Institute (PHII), a program of TFGH. She works with a team that led implementation of the tool in Mozambique. What makes the tool particularly special is that for the first time, HCW placement preferences are taken into account.

“Previously, in Mozambique, there was a very top-down approach to allocation, without any data to drive that process,” said Brown. “There was no preference data from the HCWs — nobody got a say in where they went.”

The problem with this approach is that once HCWs were placed somewhere, they would immediately apply for a transfer so they could go to a district of their choice.

The Excel-based WAO tool — specifically designed this way so developing countries don’t have to spend a lot of money on expensive specialized software — tackles the HCW allocation problem from two sides: where the need is greatest and where workers want to be placed.

In December 2015, Mozambique’s MOH put the allocation tool in place to great success. By asking HCWs their top three preferences for placement within the country, over 75 percent of HCWs were satisfactorily placed in one of their top three preferences. In the spring of 2018, a team from TFGH traveled to Zambia and Tanzania to train MOH officials on how to use the tool, which they received in a cloud-based portal.

“In Zambia, the allocation process typically takes 14 days annually,” Brown said. “The WAO tool will cut that down significantly, with the ability to allocate workers in minutes.” The tool has not yet been fully implemented in Zambia, so she described the tool’s impact as currently “a potential success.”

“The MOHs in all three countries are working with PHII/TFGH and African-based software development firms to integrate the tool within the MOHs’ existing information systems,” Brown added.