Post authored by Maria Ma, Impact Analyst and Helen Olsen, Impact Manager.
At Medic, we recently hit a big data milestone – over 1 million community health worker actions were recorded in a single month using our mHealth tools! This number represents both the highest single month of community health worker (CHW) engagement in Medic’s history and also the large volumes of data collected by those CHWs.
The Research & Learning Team at Medic is responsible for defining methods, building systems, and conducting analyses to measure and understand the impact of Medic’s programs. We are obsessed with figuring out how to make sense of our program data, and how to use it to increase the impact of our interventions.
Data gathered via Medic’s project deployments are used in many different ways to achieve these goals, both internally and externally. For example, we use these data to make sure things are running smoothly for our project partners; to report on Medic’s overall progress and understand how we can improve; and to look at metrics across projects for future learning opportunities.
In this multi-part blog series, members of the Research & Learning Team will be sharing some of the different ways in which we use data to understand and measure impact at Medic. We will cover our impact monitoring data and we collect it; our database and visualization tools; examples of our strategic data analyses, including a case study from one of our partner organizations; and a look into the future of our data science partnerships.
Our blog series will cover some of the following examples of data use at Medic:
We build dashboards for partners and regularly generate reports about trends that we’re seeing over time.
These tools help our project partners understand how their deployments are doing at a glance. Using our visualization tools, they can quickly see details such as how many pregnancies have been registered in a given community or how many women have been counseled about modern family planning methods in a given month.
When we see unexpected differences in the data, we get to dig in and ask why!
Medic also builds internal dashboards for many of the use cases we deploy from Antenatal Care (ANC) to Postnatal Care (PNC) to Acute Malnutrition. Looking at this data helps us understand how our tools are working to support CHWs in various contexts. Across projects, if we see that one area is having dramatically different results than others, the data (both qualitative and quantitative) can help point to the cause and help inform potential solutions.
We use data to help us ask the right questions during our design work and deployments.
Medic’s approach to tool development is user-focused and employs an iterative human-centered design approach. By designing with and for our end users, CHWs, we are better able to contextualize the data we collect from our deployments. Our data analysis efforts enable us to ask better questions of our users to ultimately build a better app!
We engage with partners through strategic analyses and data-sharing initiatives.
Collaboration is a key component of Medic’s approach to addressing access to care at the last mile. Through long term partnerships and strategic collaborations, we work to better harmonize our impact data across like-minded organizations and to share our findings with the broader digital health community.
We also build innovative data science initiatives with our partners.
As Medic and our partners build stores of community health data, we are finding opportunities to put that data to use to improve health outcomes and health coverage. We are predicting adverse health events to deploy care to patients who need it right now and have plans to help CHWs meet health coverage goals by using our data to set screening targets and identify new cases of diseases.
This series will be posted monthly both here on the Medic blog and on the CHT Forum. We welcome your thoughts, comments, and suggestions about our work! Thank you for reading and looking forward to seeing you next month.