|Leveraging Electronic Health Records to Promote Population Health|
In response to the 2010 Affordable Care Act, financial reimbursement for clinical intervention is increasingly shifting from volume (how many patients were treated) to value (how was the quality of care provided). In order for healthcare systems to thrive under this model, administrators must identify new strategies to improve population health by preventing and managing chronic diseases outside of the hospital setting. These forms upstream interventions are shown to several long-term clinical benefits, including decreased levels of chronic disease and an increased number of quality-adjusted life years (Eddington et al, 2012).
This clinical approach also has several financial implications. Intuitively, if diseases are prevented (or disease progression is mitigated), there are fewer hospitalizations within a community, and fewer medical costs incurred. However, it is very difficult to generate the necessary return-on-investment analyses needed to support these interventions (de Bruin et al, 2011). There are several barriers to this form of economic evaluation. First, it is difficult to delineate cost savings for disease prevention; it is difficult to measure a health event that has not occurred. It is much more common to see cost-savings associated with chronic disease management programs, because it is easier to define a comparison group: individuals who have the same chronic disease and are receiving standard care. A recent study of the Healthcare Information Management Systems Society (HIMSS)- Dorenfest survey predicted that 20% compliance with disease management programs for the nation’s top 5 chronic diseases would amount to more than $40 billion in net savings (Hillestad et al, 2015).
However, there are limitations to these disease management program assessments as well. Traditionally, the effectiveness of disease management programs is based on short-term clinical outcomes and cost-savings. These programs have not been in-place long enough to track disease progression throughout a lifetime, nor is there the proper documentation infrastructure to monitor patients across multiple health platforms (e.g., Skilled Nursing Facilities, Rehabilitation Centers, etc). Accordingly, establishing a universal Electronic Health Record (EHR) is a pivotal step in generating the longitudinal data necessary to track patient outcomes throughout the continuum of care and support population health management efforts.
Currently, it is possible to track an individual’s demographics and medical history throughout the same health care network. For example, healthcare collaboratives that include both primary and acute care can monitor patients’ inpatient stays, emergency room encounters, and outpatient visits over time (assuming all care is received within the network). Analyzing this type of data can give information key information about: 1) whether interventions that occur in the primary care settings truly mitigate disease progression over time, 2) whether these interventions incurred cost savings and/or 3) what social and demographic factors impact disease progression and healthcare utilization.
There are some efforts to promote this form of data exchange state-wide and nationally (e.g.: Hawai‘i Health Information Exchange and Meaningful Use, respectively); however, there are very few published studies that leverage this data. Long term, healthcare executives should continue support EHR standardization efforts to promote population health management. This emphasis on EHR standardization and data exchange across multiple care setting can lead to an abundance of quality and safety outcomes, including: predictive modeling algorithms to identify patients in need of services, physician reminders to promote preventative measures, and higher coordination of transitional care between acute care and home health facilities (Hillestad et al, 2015). In the interim, we need to increase efforts to assess—and disseminate-- the longitudinal data presently available. This effort will not only maximize the effectiveness of the upstream interventions already in place, but it will set publication precedence for future studies as longitudinal data collection readily available.
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