How EHR data can improve public health forecasting

As the capacity for EHRs to collect, store and analyze, large, structured datasets containing clinical data it reasons that these data could be harnessed by the public health community to enhance further their ability to conduct disease surveillance and epidemiological studies through data-sharing collaborations between private healthcare providers, public health agencies, and other community stakeholders.

The public-private model of data sharing and collaborative effort dovetails nicely with all of the community health and preventative care measures contained in the Affordable Care Act. However, despite glowing promise and alignment with current policy, harnessing EHR data for use in broad community health efforts is not as pervasive as one would hope.

Where we're at currently

Presently efforts at national disease surveillance have failed to come to fruition, as observed in a 2015 Public Health Management article surveying the state of population health surveillance using EHR reports “few of the national population health surveillance systems that rely on EHR data have progressed beyond laying the groundwork to launch and maintain EHR-based surveillance.”

However, the most progress has occurred at the local level with collaboration between state and local public health agencies and private health care providers. One notable success in this area has been in the area of Syndromic Surveillance Data. According to a report presented by the Department of Health and Human Services more than 1500 hospitals presently sending EHR data to Public Health Agencies for purposes of tracking outbreaks.

In addition to outbreak tracking, one of the more concrete examples of success in disease surveillance and epidemiology is found in the New York City Department of Health and Mental Hygiene Primary Care Information Project’s (PCIP) Hub Population Health System. PCIP has retrieved aggregate data from participating outpatient systems in New York City. The Hub includes more than 700 outpatient practices. The database provides the capacity to track the number of population health indicators and has resulted in many spin-off projects and collaborations with stakeholders to improve community health in the city.

Given the brief description provided here of the current state of data sharing between public health and private health care, what can we expect in the future in this area?

Moving from surveillance and traditional epidemiology to predictive models

As EHR data becomes more readily processed, public health agencies will be able to more beyond the conducting disease surveillance and epidemiology and move further into what is known as ‘predictive epidemiology’, which will be able to use  predictive analytics to identify individuals at risk of hospitalization or developing a chronic condition, or other comorbidities and provide specific interventions to avoid a bad outcome.

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Breaking down the 'public health vs private health' distinction

As there has been a shift away from a straight fee-for-service model that compensates providers per unit of care delivered to more of a flat payment or capitated model that rewards healthcare providers for cultivating healthier patients and for lowering the total cost required to care for patients. Public health agencies are well positioned to create policy and craft community-wide interventions that can contribute to better health outcomes, which in turn  through efforts aimed at improving community health by affecting policy measures and interventions.

Private healthcare providers, on the other hand, may not be inclined to implement the same broad encompassing measures. Collaboration between the two on the shared interest of community health may therefore become an economic necessity.

The importance of micro-trends in public health

Large scale efforts at disease surveillance and epidemiology are a staple of public health data, but a majority of the work done in the public health community is carried out at the local level. In some cases inferences must be drawn from datasets that may not generalize to a local community.   

EHRs provide micro-level data that speaks to the health status of members of that specific community. When armed with this more accurate data, public health agencies and stakeholders can examine community health trends and respond in a more focused manner.

Data security and public health data

From a legal perspective data sharing between private health care and public health agencies and other shareholders is allowed by HIPAA, which contains several mechanisms allowing EHR data to be used for public health surveillance given proper consent is given by the patient. However, providing patient data may pose problems as the patients who must consent to data sharing are overwhelmingly suspicious of it.  

A study published by ONC indicates that the privacy and security of medical records are significant, with about 7 in 10 individuals expressing concerns about privacy and security of personal health information. These pre-existing issues of trust are compounded when data is sent offsite to a government agency, nonetheless, which can potentially invoke another layer of distrust among patients who may be inclined to withhold information if they feel their clinical information is being shared for public health purposes.

Data sharing between public health agencies and private healthcare providers provide a unique opportunity to expand the role and vision of current community health efforts and bridge the gap between public health and private health care.

Armed with more timely and nuanced found in EHRs public health agencies and other stakeholders can make more informed decisions and generate more effective interventions to improve community health, which ultimately benefits private healthcare as well. However, these efforts must contend with privacy concerns held by patients whose data will be used.

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Jeff Green

About the author…

Jeff Green, MPH, JD works as a freelance writer and consultant in the Healthcare information Technology Space.

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Jeff Green

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