Achieving Population Wellness by Expanding Data Horizons
Big data is often held out as the panacea for healthcare’s ills. Broad adoption of clinical systems is providing informatics experts with access to a greater volume and variety of data than ever before, yet the solution to healthcare’s most intractable challenges remains frustratingly elusive—because their analytics fail to take into consideration data generated outside the healthcare system.
Consider that 60 percent of overall health is determined by socioeconomic and behavioral determinants, but make up just 4 percent of the national health budget. Conversely, just 10 percent of an individual’s overall health is determined by medical needs, yet approximately 88 percent of U.S. healthcare spending goes towards patient care.
That disparity demonstrates a clear need in healthcare to begin leveraging socioeconomic data to more effectively divvy up finite resources to achieve maximum population wellness. Doing so requires looking beyond electronic medical records (EMRs) to tap into non-clinical data sources that help paint a more complete patient picture, then use that information to create an actionable solution to address all the factors impacting the individual’s health.
Socioeconomic Data Analytics
When utilizing socioeconomic data to form a comprehensive patient profile, it is important to understand that different variables have different weights depending on the condition and population in question. For example, social isolation compounds any diagnosis. It is more difficult for seniors living alone with limited access to transportation to get to medical appointments and the pharmacy. The ability to effectively treat these patients is severely limited, which consequently puts them at higher risk for serious complications.
But how does data identify which patients are socially isolated?
The answer won’t be found in the patient’s EMR. Rather, social isolation is revealed through information such as how often someone shops online, commutes alone, and/or votes. These are among the thousands of variables, like the value of a person’s home, the number of credit cards, vehicle or boat ownership, etc., that reveal clues to lifestyle and quality of life. Information on environmental factors, such as air quality and potential for exposure to radon and/or lead, help determine whether a patient is at risk for developing serious conditions later in life.
When these data points are analyzed in conjunction with clinical information, it enables validation of the overall impact of social and environmental circumstances and health behaviors on lifetime care needs. The challenge is finding ways to capture and correlate that data with non-obvious behaviors that indicate medical risks. This enables more informed care decisions, as well as better allocation of financial and clinical resources.
Mitigating Risk, Improving Patient Engagement
The more risk a provider organization assumes, the more value it will realize from a detailed analysis of socioeconomic and environmental data. Having a clear view of a population’s medical, social and environmental data allows healthcare organizations to focus resources where they are most needed, such as on patients with uncontrolled diabetes or asthma, or who are at high risk for stroke or cardiac events. This improves outcomes and, subsequently, the overall health of the population. It also drives collection of an even higher volume and wider variety of data, enabling more effective population health management.
However, data can’t drive the behavioral change necessary to improve outcomes and reduce costs. That requires identifying the most effective way to engage patients in their care, which can often be found by examining behavioral data to identify which channels of contact—email, landline, mobile phone, text, home visit or direct mail—will yield the greatest response. Once identified, individuals can be grouped into subcategories based on their preferred method of contact and how likely they are to change their behavior. Outreach can then be customized to maximize engagement.
This was the process undertaken by North Memorial Hospital (NMH) when it sought to improve mammogram rates for cancer screening. The facility first identified those women within its service areas who were likely to need mammograms, then ranked them based on which were most likely to respond to offers at one of its four clinics. This information would enable NMH to increase its geographic service area by 704 percent—projecting a 117 percent increase in response over traditional attempts to reach the target audience.
In addition to increasing market share, the right combination of clinical and socioeconomic data can help healthcare organizations more effectively connect patients with essential services. For example, when NMH opened a new urgent care center, the hospital mined its patient data to identify users and non-users of its other centers. This information was then analyzed against a database of social, environmental and behavioral determinants to create an advanced predictive analytical model to determine who within the patient population was most likely to use the new urgent care center.
This predictive model enabled identification of 71.5 percent of urgent care users by targeting just 30 percent of the total market. For NMH, using predictive analytics could reduce marketing costs by $68.57 per acquired patient over traditional marketing models.
A Page from Retail’s Book
The retail sector has been successfully utilizing these engagement models for years. Through the aggregation and analysis of environmental and social data and consumer buying habits, retailers can refine and target outreach, reducing marketing costs and increasing ROI. It’s time for healthcare to follow suit.
Pay-for-performance is quickly becoming the dominate reimbursement model. To compete, healthcare organizations must be willing to take a lesson from the retail sector and expand their data horizons.
Leveraging solutions that allow providers to mitigate environmental and behavioral risks can improve patient wellness, reduce healthcare costs and revolutionize population health management nationwide.