Health Solutions Research

Hsr developed models for the risk of transmission of disease, chronic, social, or infectiouos, as well as a model for the risk of mortality or critical case of disease. These risk indices have both been tailored to address sars-cov-2. These model were deployed throughout the us, as well as by the who in central america and in africa. Our core Health risk index has been further developed to produce a medical supply needs index and medical device needs index that identify the level of need of medical supplies (e.g., staffing, ppe) and devices (e.g., equipment, ventilators, beds) during a natural or man-made disaster and Health emergency. In addition, the Health risk index has been further developed into a vaccine needs index to assess the necessary level of vaccines as well as identify the populations at greatest risk and who will have the greatest benefit. This solution has also been extended to assess the risk of zoonotic spillover.

Hsr has built a geospatial application, with the support of the u.s. federal emergency management agency (fema) and the u.s. geological survey (usfs) that merges Health, social, and environmental data from numerous disparate sources to identify in advance the Health needs of a disaster impacted population and communicate that information to emergency response managers (for planning, resource allocation, and tracking purposes) as well as to on-the-ground emergency medical responders (for care delivery purposes). This application resides on the geomd platform.

Hsr is developing a geographic information system into a geospatial data analytic platform called the geomd platform. We are leveraging this platform to address population Health challenges including healthcare access, non-emergency medical transportation, the opioid epidemic, maternal and child Health, hospital readmission rates, etc. We continue to develop the geomd platform and have used it to also identify the Health and medical needs of a disaster impacted population, the healthcare cost management for a capitated population (e.g., medicare populations), as well as identifying and addressing the root causes of Health inequity. We have expanded its machine learning and ai capabilities, as well as its capabilities to ingest novel data sources, such as satellite imagery.