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The American Journal of Managed Care December 2016
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Getting From Here to There: Health IT Needs for Population Health
Joshua R. Vest, PhD, MPH; Christopher A. Harle, PhD; Titus Schleyer, DMD, PhD; Brian E. Dixon, MPA, PhD, FHIMSS; Shaun J. Grannis, MD, MS, FAAFP, FACMI; Paul K. Halverson, DrPH, FACHE; and Nir Menache
How Health Plans Promote Health IT to Improve Behavioral Health Care
Amity E. Quinn, PhD; Sharon Reif, PhD; Brooke Evans, MA, MSW; Timothy B. Creedon, MA; Maureen T. Stewart, PhD; Deborah W. Garnick, ScD; and Constance M. Horgan, ScD
Data-Driven Clinical and Cost Pathways for Chronic Care Delivery
Yiye Zhang, PhD, and Rema Padman, PhD
Accountable Care Organization Hospitals Differ in Health IT Capabilities
Daniel M. Walker, PhD, MPH; Arthur M. Mora, PhD, MHA; and Ann Scheck McAlearney, ScD, MS
Building Health IT Capacity to Improve HIV Infection Health Outcomes
Hannah Rettler, MPH; R. Monina Klevens, DDS, MPH; Gillian Haney, MPH; Liisa Randall, PhD; Alfred DeMaria, MD; and Johanna Goderre, MPH
Telemedicine and the Sharing Economy: The "Uber" for Healthcare
Brian J. Miller, MD, MBA, MPH; Derek W. Moore, JD; and Chester W. Schmidt, Jr, MD
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Kitty S. Chan, PhD; Hadi Kharrazi, MD, PhD; Megha A. Parikh, MS; and Eric W. Ford, PhD, MPH
Payer—Provider Patient Registry Utilized in a Behavioral Health Home
Michele Mesiano, MSW; Meghna Parthasarathy, MS; Shari L. Hutchison, MS, PMP; David Salai, BS; Suzanne Daub, LCSW; Mary Doyle, MHIS; and James M. Schuster, MD, MBA
US Hospital Engagement in Core Domains of Interoperability
A. Jay Holmgren, BA; Vaishali Patel, PhD; Dustin Charles, MPH; and Julia Adler-Milstein, PhD

Getting From Here to There: Health IT Needs for Population Health

Joshua R. Vest, PhD, MPH; Christopher A. Harle, PhD; Titus Schleyer, DMD, PhD; Brian E. Dixon, MPA, PhD, FHIMSS; Shaun J. Grannis, MD, MS, FAAFP, FACMI; Paul K. Halverson, DrPH, FACHE; and Nir Menache
Aligning health information technology with population health requires organizations to think differently about data needs, exchange partners, and how to leverage both for evidence-based action.
Broaden Health Information Exchange
In the pre-MU era, health IT and EHRs were data silos and repositories of information that could not easily be shared between care providers. Health information exchange (HIE) was developed to share critical patient information. To date, however, HIEs have minimally supported population health initiatives. For one, social service organizations and public health agencies are rarely partners in an HIE.17 Population health requires collaboration, partnership, and cooperation with social service organizations and public health agencies because healthcare organizations lack the services, programs, or expertise to address many of the determinants of health. Likewise, many healthcare organizations’ HIE activities are narrow in scope. Factors such as limited participation in community-based HIE organizations, the growing use of enterprise HIEs, or single-vendor mediated EHR strategies limit the widespread availability of patient data in a given market. Such strategies make it difficult—if not impossible—to assemble comprehensive patient histories, aggregate data for population health, and coordinate care.18 In addition, even when available through HIEs, information from external providers is rarely integrated into clinical systems,10 resulting in limited ability to leverage exchanged information for clinical decision making.
 
Avenues to expand organizational participation in HIEs exist. For example, healthcare organizations can identify social service partners (eg, 2-1-1 listed programs) and assist them in connecting to an HIE network or obtaining direct secure messaging accounts. Such arrangements would facilitate patient transitions to service providers capable of addressing a broader set of health determinants. Also, these arrangements could facilitate communication from the social service organizations and supplant the need for healthcare organizations to directly capture social determinants in health IT systems at the point of care.
 
Similarly, simply considering who has the ability to act upon HIE information could suggest new priority partners. For example, emerging HIE event notification systems alert providers about key patient events like hospitalizations. Although small medical practices may not have the capacity to respond to these events,19 case management and home health agencies have the expertise and staff capable of coordinating care in response. Additionally, partnerships with public health agencies, which often maintain data on geographic populations, could provide access to data on social, behavioral, and environmental factors currently absent from health IT systems.
 
Lastly, HIE will be most effective if data-sharing partnerships accurately reflect patients’ care patterns within the community. This may require healthcare organizations to consider HIE needs beyond a single vendor and novel approaches for engaging new partners (and even competitors) to the mutual benefit of a given population.
 
Translating Data to Actionable Information

Today, healthcare organizations typically estimate risk using only clinical and care utilization measures, even though social, behavioral, and environmental factors are also relevant. Thus, prediction models, such as those for hospital readmission, often perform poorly.20 Armed with expanded and widely shared data reflecting the contexts and behaviors that influence health, the next step is to transform these data into actionable information to achieve population health goals. A clear application is to augment current risk stratification approaches, which attempt to divide populations into groups for targeted interventions. New data sources and information-sharing partners may lead to better-performing models, and they may allow us to characterize and predict more population health–relevant outcomes. For example, many existing risk models predict outcomes like death, care costs, or care utilization. Although these are important, they are distal from the basic goals of population health. Instead, data on health behaviors and other social determinants may be a means to predict upstream factors, such as physical function and quality of life, which are more relevant to population health goals of widespread physical, mental, and social well-being.21 Finally, this new information must be put in front of the users and shared with partner organizations so they can take action.
 
Conclusions
A shift in how healthcare leaders think about data collection, data sharing, and translating data into actionable information is neither insurmountable nor technologically difficult. The capabilities exist to collect, integrate, and analyze large bodies of data relevant to human health, including social, behavioral, public health, and environmental factors. Instead, as healthcare organizations establish population health goals, leaders must ensure their organizations’ data collection and analytic capabilities align with their changing business needs. As organizations become accountable for population health, their leaders will need to initiate collaborations and agreements with nontraditional partners to obtain, share, and use social indicators and service information in order to optimally leverage health IT resources in pursuit of enhanced healthcare and population health. 


Author Affiliations: Richard M. Fairbanks School of Public Health (JRV, CAH, BED, PH, NM), and School of Medicine (TS, SJG), and Center for Health Services and Outcomes Research (BED), Indiana University, Indianapolis, IN; Center for Biomedical Informatics, Regenstrief Institute, Inc (JRV, CAH, TS, BED, SJG, NM), Indianapolis, IN; Center for Health Information and Communication (BED), Department of Veterans Affairs, Indianapolis, IN.

Source of Funding: Support for this publication was provided by the Robert Wood Johnson Foundation through the Systems for Action National Coordinating Center, ID 73485.

Author Disclosures: Dr Schleyer is an employee of the Indiana University School of Medicine, which engages in research and design of health information exchanges (HIEs) through his appointment at the Regenstrief Institute. As a part of his full-time position, he participates in a wide variety of HIE and health IT–related projects. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BED, SJG, CAH, PKH, NM, TS, JRV); drafting of the manuscript (BED, SJG, CAH, TS, JRV); critical revision of the manuscript for important intellectual content (BED, SJG, CAH, NM, TS, JRV); administrative, technical, or logistic support (BED, CAH, PKH); and supervision (PKH, NM).

Address Correspondence to: Joshua R. Vest, PhD, MPH, Richard M. Fairbanks School of Public Health at Indiana University–Purdue University Indianapolis, 1050 Wishard Blvd, Rm 5124, Indianapolis, IN 46202-2872. E-mail: joshvest@iu.edu.
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