The Global Health SDI

Our work in medical transportation led to our building a strong capability in Geographic Information Systems (GIS) and the development of a GIS platform with a data analytic engine. That platform has grown overtime into a fully featured Spatial Data Infrastructure (SDI) with a focus on health.

The Global Health SDI is a revolutionary platform, that can be best understood as a catalogued library that brings together all data related to health at the global and local levels, and also featuring development, analytics, and visualization tools on which applications leveraging the data and tools can be built, hosted, and shared. With this Global Health SDI, HSR.health is able to support the harvesting, curating, storing, and sharing of data related to health outcomes and costs as well as the social and environmental determinants of health.

This Global Health SDI will enable the building of innovative applications to address broad as well as specific challenges. Additionally, research, data, and possible solutions for current medical issues are streamlined onto the SDI platform to leverage the trends in data on healthcare costs and outcomes, as well as social determinants for health and wellness across multiple demographics, such as varying income levels, geographical areas, and cultural differences.

The Opioid Epidemic

Our GeoHealth Dashboard (patent pending) is a geospatial data analytic platform that offers precise population health and at-risk indicators for chronic health care and public health conditions or problems, including the Opioid Epidemic. The GeoHealth Dashboard identifies geographic regions and demographics associated with the identification of illegal drug and prescription opioid use. Leveraging this, we have developed the Patient Risk Stratification for Opioid Abuse Related Mortality (OARM) – which identifies from among any population demographic those individuals who are most at risk of addiction, diversion, and overdose. The OARM also potentially identifies those individuals who are at low risk of opioid addiction, diversion, and overdose.

This information can be used by hospital quality and safety personnel (directors, clinical staff members) to adequately anticipate and respond to patient treatment requirements based upon hospital location (e.g.: geographic region, postal code, county, municipality) together with temporal considerations (e.g.: time of day, day of week, season of year). Further, this enables outreach and intervention by identifying high-risk or at-risk individuals within a population. In this way, this becomes insight knowledge for case, population, and geographic planning.

The OARM can be used for emergency department preparation and staff in-servicing, as well as by pre-hospital staff for predicting the need for enhanced or extended emergency medical services (EMS) crew assignments, paramedic in-servicing, or to augment community paramedic programs. For example, the information provided by the OARM helps identify whether a hospital is likely to experience an influx of opioid-dependent patients so that an emergency department can plan and schedule staff, supplies, and remediation efforts around this. Such information can also be used by physicians and nursing staff to in-service, train, and participate with case management in scenarios related to drug treatment, pain management of opioid-dependent patients. From a patient perspective, ensuring the appropriate case management and healthcare resources are in place helps increase the likelihood they will receive the best care.

We also propose and seek to perform additional testing to validate that this information can assist clinicians treat patients for pain while appropriately limiting exposure to prescription opioids to those who are at risk. For instance, we believe the OARM will allow clinicians to determine whether a patient is at high risk (or medium risk or low risk) of opioid addiction, diversion, or of having an overdose in the next 12 months. That risk score can then be considered in treating the patient. For instance, a high-risk patient who needs opioids also may be provided additional monitoring (e.g., in-hospital setting) to protect against the risk of opioid induced respiratory failure, or be prescribed behavioral health counseling (e.g., out-of-hospital setting) to ensure they don’t fall into addiction. Other high-risk patients may simply be offered alternative pain management treatment. Further, patients identified as low risk may be allowed to continue their opioid-based pain management treatment if it is proving effective in addressing their pain.

Readmissions Reduction

Through our GeoHealth Dashboard, we have developed a dynamic routing solutions that helps hospital systems optimize mobile van solutions designed to engage their communities and reduce hospital readmissions.

Data Analytics in Healthcare

We believe data analytics holds tremendous potential for the practice of medicine. Applications as wide and disparate as facilitating Aging-in-Place solutions, advancing the state-of-the-art in home care solutions, as well as improving the monitoring of narcotic prescriptions can benefit from predictive analytics.

Data analytics involves providing existing or new clinician decision support tools with information gathered from the analysis of large sets of health and potentially non-health related data. Data sets can include individual and population-wide health records, potentially social media data, the

We are currently designing a research study to validate whether physicians will be in a better position to monitor opioid drug use by patients if the Prescription Drug Monitoring Program (PDMP) can be augmented with analysis derived from multiple health-related data sets.

Rural Healthcare

HSR has researched rural healthcare issues, specifically surrounding access to care and the impact of (the lack of adequate) medical transportation on overall population health in rural America.

Our research has led to the creation of a geospatial data analytic platform, called the GeoHealth Dashboard, through which we have identified the specific regions where residents have insufficient access to specific service lines (e.g., cancer treatment, behavioral health, post-operative rehabilitation care). This research has led to the creation of strategies for locating healthcare facilities that maximize access to care from the patient’s perspective.

In addition, our work has brought to light those service lines where health system overall capacity to treat does not meet anticipated healthcare needs. This allows us to work with local providers and health officials to collectively develop strategies addressing this gap in care.

Additional Research

We have researched the following issues for their potential to improve population health:

Medical Transportation

The lack of affordable and convenient transportation is one of the factors leading to hospital readmissions across the country. We have developed and are testing a solution to address this nationwide problem. Our research shows that an effective non-emergency medical transportation solution will cost hospitals less than what they will save in reduced readmissions. And reducing readmissions decreases overall healthcare spend – and improves overall population health.

Personal Health Records Systems

The use of personal health records (PHR) systems has been forwarded as a means to increase patient involvement in their own care or the care of someone in their charge. PHR systems are online portals and/or mobile apps that connect to the electronic health records (EHR) systems of a provider. There are two key issues for PHR system – security & adoption.

Systems that provide access to medical records must be secure and must also provide a compelling reason for utilization.

Our research looked into the optimal design characteristics for a secure PHR that met the use case for personal health records from the perspective providers and the overall medical community as well as patients, including the general population as well as the aging community.

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