Implementing Machine Learning to Glean Insights from Real-World Data Sources in Low- and Middle-Income Countries

Healthcare researchers have increasingly implemented machine learning approaches to help process large sources of real-world data (RWD). Oftentimes, this approach is held back by messy RWD or RWD architecture. In a new study, researchers from IntraHealth International applied machine learning to RWD from low- and middle-income countries. In the process, the team learned several lessons, including the importance of having a data science expert on hand.

According to Wayan Vota, “While much of the work of machine learning can be carried out by a consultant, we are committed to increasing the use of data science at our organization, which requires a full-time employee to develop and iterate on these approaches. We built this initial approach by contracting a doctoral-level public health researcher from a local university with training in international population health and data science as a lead technical expert. We later hired her as a senior data scientist and placed her on the digital health team and since have grown the team to add an associate data scientist who had been working in the private sector but was attracted to international development by the lure of data and opportunities for improving health outcomes.”

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(Source: ICT Works, November 9^th, 2022)