When it comes to public health, and identifying the events and behaviors that affect it, agencies charged with protecting the health and welfare of citizens need to leverage every available tool at their disposal to quickly and effectively respond to growing threats. Data mapping is a tool to answer the call.
Data mapping isn’t a new technique in the healthcare spectrum. The earliest use of mapping for public health was in the 1850s by John Snow when he used hand-drawn maps to determine and show the locations of cholera deaths, proving to disbelieving physicians that the disease’s source was a tainted water pump. The advances made over time are significant, allowing agencies to track diseases and more easily anticipate the impact they would have on a specific area. HealthMap is a prime example of this capability, utilizing online sources for disease outbreak monitoring and real-time surveillance of emerging public health threats.
In the case of emergencies, planning and crisis response, open-source information is readily available via the CDC. Data in and of itself is not enough. Optimally, healthcare would have three main pieces: needs and supplies of quality resources (data), analytics tools to configure data variables and geospatial intelligence models to map these variables. Using analytics tools to visualize information gives users more insight into the patterns within the data. By adding in geospatial intelligence models and tools, users can factor in geography to find patterns and trends, allowing them to reach a conclusion faster.
Analytics tools require an abundance of information to draw small, detailed conclusions about the general population from which they have gathered data. One benefit is visualization; real-time dashboards are created and allow various elements to be applied to the bigger picture. Most importantly, analytics is scalable. Conducting longitudinal studies or looking at open-source data collections provides more insight for program effectiveness, building on best practices, or even driving policy development. As Dr. David C. Goodman, a Dartmouth Institute professor of pediatrics and health policy, says, "In healthcare, geography is destiny. Where you live and where you receive care makes a tremendous difference."
Dartmouth Institute, which has collected over 100TB of Medicare data, has analyzed trends which are then referred to by policy makers to assess clinical quality measures and costs. As more information becomes available, so do the insights that are pulled from it.
When looking at healthcare data to gauge public health, several aspects must be considered: patient records and trends, facility resources, monetary factors such as income and access to insurance and environmental elements like the EPA’s Toxic Release Inventory and the Superfund National Priorities List. Combining this information into an analytics tool shows the relationships and trends to draw deductions about how federal funding should be allocated, whether it is to supply antivirals, plan for resource logistics or to assess public outreach programs for preventative measures.
Geospatial intelligence models and tools have been adopted by multiple agencies to track trends. For example, the National Oceanic and Atmospheric Administration (NOAA) uses geospatial intelligence tools to show migratory patterns in fish and wildlife, while the Department of Defense (DOD) employs these models for strategy and in-field combat analysis. By merging healthcare information, analytics and geospatial intelligence into one platform, organizations are better able to see and dissect the specifics of population health and manipulate data for specific uses. Incorporating visualized data and superimposing information onto maps gives deeper and faster insight into a public health problem.
By coupling analytics and health reports, organizations can see certain trends appear in times of disease outbreak. And, by adding another visual layer to the picture and including geographical placement – with the same dataset – they can now see if this outbreak is related to travel or if it is location-specific, as was the case with the measles outbreak in Disneyland. With this example, by adding another element to the dataset (in this case, zip code), organizations can trace the infection to certain food sources.
What can agencies do with the data? With the right datasets, the information is almost limitless. Combining analytics models, healthcare data and geospatial intelligence provides a clear way to identify healthy populations or those who are in need of help. This combination can also show the supply and demand of materials and resources which could reduce the time and money spent on logistics during crises. It is important to know that while organizations may have data, marrying that information with compatible mapping software applications makes it useful for non-experts who are utilizing it.
Analytics alone does not provide the answer, rather it helps decision makers reach a conclusion faster by giving accurate information. Decisions that can lead to a greater impact on population health and on the health industry at large.