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: should we provide digoxin to patients with heart failure? how many infant doses of amoxicillin should we stock? how many community health workers do we need in a new set of villages? are we performing too few or two many cesarean sections? what should the per capita cost of our model entail?
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:
1)Biometric Identification. Biometric identification is an essential component of surveillance systems, a fact that our team directly realized in the course of trying to track patients. It proved impossible, particularly in a country lacking a robust national identity system, to track patients accurately and efficiently. Of the various options (cornea, retina, fingerprinting) for biometrics, we chose fingerprinting owing to its low cost, ease of use, and acceptability to users. Simprints’device was the most affordable and robust for the setting of rural Nepal.
2) Reliable platform for community based care.The platform needs to be efficient, simple, and resilient for CHWs to use for home-based carein remote communities. The platform for community-based care needed to be modular, affordable, easy to use, and able to integrate with the hospital-based EHR. We chose the Dimagi’s Commcare.
3) Integration with hospital-based EHR. The solution we had chosen was Bahmni, developed by Thoughtworks. A primary innovation of the Bahmni system is that it provides a stripped-down user-friendly interface for the robust and widely-used OpenMRS architecture. In a resource-limited setting such as in rural Nepal, most providers have had limited prior exposure to computers. A well-tuned user-experience without non-essential features is thus of critical importance for physicians to be able to use the EHR at the point of care without feeling overwhelmed. Commcare and Bahmni communicate via the cloud platform with MOTECH and the government reporting system via DHIS2, as seen above.
4) Robust Provider Management System. 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: 1) active and passive surveillance of conditions in the community; 2) triage and referral care with facilities; 3) community-based diagnosis and treatment. 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:
1) Stepped wedge, cluster-controlled, non-randomized design with patient-oriented, focused outcomes
2) Prospective longitudinal cohort for measuring patients’ management control status
3) Mixed methods (quantitative plus qualitative) implementation research to understand mechanism, causality, and scalability in a feasible, iterative, and ethical fashion
As we scale up our work within the Nepali public sector, we will do so in a phased manner, which is dictated for the practicalities of solid management, iterative improvement, and human resources. 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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