Health Workforce Data Use:
Lessons Learned and Recommendations

  Lessons Learned and Recommendations

  • Open source approaches are effective. Through building a virtual community, iHRIS has become a widely applied and extended solution demonstrating many development aid priorities including local ownership and partnership.
  • CapacityPlus’s experiences with the University of Dar es Salaam in Tanzania, Makerere University in Uganda, and Luanar University in Malawi clearly indicate that universities have the best infrastructure for capacity-building in informatics and ensuring sustainability of capacity-building efforts. Returns on investments in local universities are magnified when they start working beyond the borders of a single country. Twinning between global and local universities may yield unique benefits to local development and support.
  • Regional organizations such as WAHO are the strongest vehicles for disseminating and supporting uptake of HRIS best practices, tools, and technologies. 
  • Interoperability assists with uptake and leveraging of health information system strengthening activities. By ensuring that iHRIS is interoperable with other leading national health information systems, investments in those systems will benefit implementation of iHRIS, and vice versa. Once the systems are linked with quality data (e.g., DHIS 2), important correlations across health domains (e.g., services, supply chain) can be identified and incorporated into solution planning.
  • National HRH stakeholders benefit by working through a stakeholder leadership group to develop a common strategy, policy, and standards for a national health workforce information architecture as well as to promote increased use of data for decision-making. One-time data use training can increase stakeholder buy-in and goodwill; however, mentorship and sustained collaborative development of skills with real-life examples are often needed to change data use behaviors.
  • Countries should be encouraged to use iHRIS with the WHO’s Workload Indicators of Staffing Need (WISN) tool to generate data on how many health workers are needed (from WISN) along with how many workers are present or missing from established positions (from iHRIS).