We’re gearing up to launch the Kuunika: Data for Action project in Malawi. To date, we’ve had the pleasure of working with some of Malawi’s finest across government, private, and not-for-profit sectors.
As we work on the project’s implementation plan, a gap in the landscape became clear. Kuunika’s overall goal is to increase access to and use of high-quality health data at multiple levels. However, given the vast array of actors, skillsets, and data systems, where do we start? We want the project to target those users, systems, and activities we expect will have the greatest return on investment for improving HIV outcomes. As such, we realized we need better information on what critical decisions are made that lead to HIV program outputs and outcomes. In particular: Who are the decision-makers and what do we know about them? What HIV-related data is being used for decision making and where does it reside? What information is missing?
We could answer these questions anecdotally, but didn’t understand the full picture.
It turns out, little has been done (or written about) to systematically document the critical decisions at various levels of the health system and indicate how users, data, and systems interact to produce action. If our goal is for clinicians and policy makers to make more informed decisions using empirical data (which it should be), we need a better way of cataloguing the decisions and events where the right data need to be at the right users’ fingertips.
Cooper/Smith is helping to answer these questions in the Malawi context. We undertook a rapid-fire study: Strengthening Routine Use of Information to Improve HIV and Health Outcomes in Malawi: Systematic analysis of key data users and decision points. We know, it’s mouthful, so we will refer to it going forward as the Data Users Study. Our objectives were twofold:
- Systematically document, relate, and validate assumptions for key data elements (indicators), users, and systems that manage Malawi’s HIV response
- Identify the critical decisions/events encountered by decision-makers and the information used or needed to improve HIV program effectiveness
It took a couple of months from conception to execution to obtain the data needed from communities, service facilities, districts, and central offices. We collected information from a wide array of actors, coded/analyzed responses, and extracted some initial gems to inform Kuunika implementation design.
For example, study respondents identified a total of 335 unique decisions typically made in their job functions. We grouped these into 85 common categories. Of those 85, the top 5 categories accounted for over 40% of all decisions identified. These categories included drug supply, treatment initiation, defaulter follow-up, program performance and referrals. If we want to maximize return on investments in health information systems in Malawi, we should prioritize based on which decisions occur most frequently, which data are most valued, and which systems can/should be linked to efficiently produce this information when needed.
Please take a moment to look through the initial findings from the study. We will continue to add to this analysis over time. We are also working on a Phase 2 of the study, which will examine health worker preferences for different incentives associated with promoting data use.
As always, we love to talk about data! Please share any thoughts or comments below or email us.