The past 20 years have incurred profound increases in funding for the use of health management information systems and national demographic health surveys in the developing world. This has included collecting routine administrative and operational data from the health system and survey-based data collection on a more or less annual basis. The idea is, if we provide donor, implementer, ministry and provider officials with actionable, timely and accurate data, we can improve program performance.
The report card isn’t great. While countries like Rwanda excel in this regard there is a sizable group of countries where the quality of this data is poor or it simply doesn’t exist. Many donors, recognizing the need to optimize coverage, quality, availability and access to health services, have turned to complementary health data.
What is Complementary Health Data?
Complementary health data, in this context, is data sourced from outside formal routine administrative and operational data collection efforts for the purposes of improving health service and policy decision-making.
The Performance Monitoring and Accountability (PMA) 2020 is one of the best examples of complementary health data’s power. PMA 2020 uses rapid-turnaround surveys that monitor key indicators for family planning, water, sanitation and hygiene, and other health and development indicators—using cadres of trained volunteers and university students to collect the data. PMA 2020’s utility lays in its quick execution—tracking these indicators in real time allows for implementers to monitor trends and adjust interventions in line with the data. This is a large improvement over cumbersome national health surveys that are expensive to execute and are old before they’re even aggregated.
But PMA 2020 doesn’t go nearly far enough; it still struggles from a one-size-fits-all approach to data collection and use. While the data is cleaned and aggregated quickly, it’s still not collected frequently enough to drive optimization. More importantly, the effort isn’t really designed to help implementers differentiate activities at a low geographic level (e.g. Health Zone in the Democratic Republic of Congo [DRC]) and instead focuses on describing national level trends quickly.
Designing Complementary Health Data Collection for Optimization Instead of Reporting
The next great stride in global health involves differentiating activities at a low geographic level and optimizing those activities based on real-time data. Below are three key components to designing complementary health data collection for optimization instead of reporting:
- A lot of data is required. PMA 2020 was right to enlist the help of volunteer networks to collect their data at higher volumes than survey firms can. Proven crowdsourcing solutions can do this at an even greater scale and frequency.
- Data collection must be driven by what matters for service delivery optimization. Clear KPIs exist in global health already (coverage, access, availability, quality etc.)—they’re just not being used enough to drive optimization. Those KPIs must drive data collection design and dashboard development. Read more about how Premise is doing this with vector control.
- Data should be collected and visualized at the lowest geographic unit at which important decisions are made. Using the DRC as an example, this means monitoring KPIs like immunization coverage by health zone and refreshing the data every month instead of through annual surveys. It also means monitoring things like every single immunization session to see if it happens at all and in accordance with standards.
Are your programs using complementary health data? Email me (firstname.lastname@example.org) if you want to talk more!