GLOBAL HEALTH & SOCIAL MEDICINE
I am an epidemiologist and physician trained in internal medicine and pediatrics who conducts implementation science research on strategies to improve the delivery of evidence-based healthcare interventions in settings of extreme poverty. The focus of my research is in rural Nepal with a non-profit healthcare company I co-founded, Possible. Over the last seven years, Possible has delivered care to over 500,000 patients through a public-private partnership with the Nepali government. Within this arrangement, Possible manages an integrated healthcare system in one of Nepal's most remote districts, and the government provides medicines, facilities, and financing. Presently, Possible cares for 130,000 patients per year via over 250 full time employees and an annual budget of $8 million. The research arm of our team, the Healthcare Systems Design Group, conducts implementation research studies through pragmatic study designs. In brief, our implementation research approach is as follows. It starts with the basic tenant that Possible as a healthcare company has decided to invest heavily in data. That's because we believe that intrinsic to delivering effective, patient-centered healthcare is using quantitative analytics to drive decision-making.
Throughout our team, managers daily, monthly, quarterly, annually, have to make decisions across a wide-range of challenges:
Particularly as healthcare companies scale, it is critical to have a single, unified electronic medical record to which the staff throughout the organization can turn for metrics. Ours, built with partners from Ministry of Health of Nepal, includes the following components:
A frequently overlooked aspect of technology solutions is the management system of the healthcare providers who are delivering care and using the technology. In our system, we have developed a professional cadre of community-based healthcare providers, Community Healthcare Workers (CHWs) who undertake three core functions:
They are women who are recruited from the catchment area under service, receive full-time salary and benefits, and are supervised by a Community Healthcare Nurse. The same metrics backbone is used for scientific studies, quality improvement initiatives, and strategic planning and donor reporting. We must create a data and analytics platform to meet the needs to be rigorous for scientific evaluation, clear and simple for strategic planning, and real-time for quality improvement. The scale and design of interventions within delivering healthcare in our public-private partnership goes hand-in-hand with scientific evaluation. We cannot design interventions that cannot be rigorously evaluated; we cannot place perceived scientific rigor over the pragmatic concerns of healthcare delivery. As such, we deploy three primary study design principles:
This phased approach represents an opportunity to study control and intervention groups over time. For ethical and pragmatic reasons, however, we will not randomize village clusters. Still, the cluster-controlled, prospective design allows us to rigorously test hypotheses about aspects of our intervention.
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