
For the duration of my trip, I asked everyone I could: “Have you seen the marked drugs? What do you think? Is it working?” The answer was a little more complicated than I had hoped for: “Yes. But…” Most people agreed that marking the drugs did cut down on theft and there have been several high profile cases where the discovery of marked drugs for sale led to arrests. But more than anything, marking drugs didn’t completely stop theft, it just became easier to find out when drugs go missing. At first I was disappointed that such a brilliant, simple idea wasn’t in fact so simple. But simple ideas usually aren’t.
Increasing the visibility of health data is another area where the simple solution isn’t always the most straightforward. Reliable data can illuminate health system issues and become the catalyst for root cause analysis and problem solving. Just like marked supplies, data can provide insights into the supply chain processes, but what those data tells us is not always so clear. For example, when trying to figure out whether a commodity is being underused, we might start by looking for stockouts tracked in the logistics management information system (LMIS). At the same time, data from the health management information system (HMIS) about the number of cases diagnosed or clients served might tell us a completely different story about the root cause of the issue.
The UN Commission on Life-Saving Commodities for Women and Children (UNCoLSC) has been exploring ways to integrate these two systems into a single data dashboard. Having access to both HMIS and LMIS datasets simultaneously gives a more complete picture of how drugs are used while indicating how many more may be needed. The UNCoLSC has funded successful HMIS/LMIS integrations in Senegal and Tanzania and is now looking across countries to try and answer the questions – is HMIS/LMIS integration a ubiquitous need? What would people use these data for?
When answering this question, several stakeholders talked about “building a case” that certain drugs aren’t being optimally used. For example, stakeholders want to use HMIS and LMIS data in maternal death audits. If a woman dies of postpartum hemorrhage (reported through the HMIS) but the health center had stocks of misopristol at the time of her death (reported through the LMIS) you know that the issue wasn’t a lack of supply, but rather a lack of adherence to standard treatment guidelines. Data integration illuminates the problem so that it can be solved.
