GIS Mash-ups Can Help ACOs, HIEs

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Pioneering public health departments across the country are exploring using non-clinical datasets such as census data to better plan their services, but a group of researchers at Indiana University and the Regenstrief Institute also believe combining such data with clinical data could be valuable for health delivery systems and accountable care organizations, as well as supply a value-add service for health information exchanges.

The researchers are using geo-spatially enabled electronic health records to help officials in Marion County, Ind., which includes greater Indianapolis, pinpoint areas of greatest need for public health services, but the project's principal investigator said he is also planning to explain the project's goal to technology executives at the region's health delivery systems soon.

"Hospital systems realize they need to do community health assessments for their IRS tax requirements, but they tend to be going about it in the same way others have done, meaning that people come in and say 'you have to do a population-based survey' – some are doing web-based surveys and others are doing random digit dial surveys," said Brian Dixon, a researcher at Regenstrief and assistant professor of public health at the Indiana University Fairbanks School of Public Health.

The problem with surveys from a population health standpoint, of course, is that the information in them is by nature subjective, and the inherent biases in the data could be problematic in aiding planning and policy.

Dixon is leading colleagues from the university in combining EHR data from patients covered by both Medicaid and commercial plans in Marion County with geo-spatial data from the university's Social Assets and Vulnerabilities Indicator (SAVI) in a research project dubbed PEDAL ( Population EHR Data for Assessment at the Local Level). The study brings to bear quantitatively observed data that presumably can help public health officials pinpoint what diseases and determinants of health may be present in certain areas of their jurisdictions. The project, which began in 2013 and is scheduled to conclude in September, was funded by a $200,000 grant from the Robert Wood Johnson Foundation.

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The PEDAL researchers are focusing on fine-tuning HEDIS-like measures to help public health planning, and are also working on discovering the optimum amount of granularity in geo-spatial data to make their model the most useful it can be. For instance, while creating the parameters of their study, they found out that 63 percent of the public health officials who responded to a benchmarking survey said they either did not have access to sub-county level data or did not know if they did.

Data that broad is not particularly useful in planning community interventions, but Dixon said getting too granular with geo-spatially enhanced social data is also problematic – for instance, mapping population down to the census block level, or approximately 1,500 people, can yield overly noisy data, especially in high-density neighborhoods with residence units such as apartment buildings or trailer parks.

"That can make estimating a real population that lives there challenging," he said. Through PEDAL, he said, he and his colleagues have found that mapping population at the census tract level, which averages about 4,000 people, yields sufficient granularity without too much noise.

Using the SAVI platform plus EHR data culled from the Indiana Network for Patient Care, the data arm of the Indiana Health Information Exchange, the researchers calculated the diabetes rates in each of the county's neighborhoods, which they deemed as being one of the most feasible measures using the data, and presented that information in an easy-to-read map.

Win-win-win potential?

Dixon said such visually enhanced data could prove vital not only to public health officials in planning their policies, but also to health systems and even to HIEs as they seek value-add services to provide to members.

"This is a useful tool hospital systems would be able to use with their own data," Dixon said. "They can extract certain metrics from their own EHR systems, and mash that up with some of these cool tools evolving on the web that allow you to do this sort of thing, but there are a couple challenges to that. One is that many times, as  the Institute of Medicine pointed out last year, data around social determinants of health are not really being documented in EHRs today, so health systems would need to start doing a better job of capturing that data in their local EHR systems. The second thing is that most health systems, as some of our work here at Regenstrief has shown, don't always have a complete picture of health for their patients because they go outside the network to get part of their care."

Getting a handle on the healthcare access patterns of these patients could yield significant dividends on both intra-organizational and community-wide bases, Dixon said. Building such an infrastructure could be a natural niche for HIEs, which could provide that data as a value-added service.

"Do they want to understand the population they serve today?" he saiid of the quandary health systems face in planning for a more community-based delivery model. "Or do they want to understand what the community's needs are and what the opportunities are for them to expand to a new population? Because if they don't have data on that population they will still be in the dark.

"That's where I think it makes sense to look for places where there's alignment between what goals health systems have in terms of understanding social determinants and community health and what the public health departments need to understand. Where those things align, that would create a sustainable model for the HIE."

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