What pop health needs to learn from consumer marketing

In the US, consumers reign supreme. American retailers understand their customers and use data to influence what they buy, what they pay, and when they return for more. With more than 68 percent of the U.S. economy driven by consumer spending, retailers clearly know how to identify and motivate consumers to take action.

The techniques and information technology tools used by retailers offer a model for healthcare providers to deliver effective population health management.

Retailers are gaining insights through two specific actions: applying analytics to a segment the population and, within it, individuals requiring attention; and conducting patient engagement activities to continuously influence the behavior of the targeted individuals.

Retailers use multiple sources of data to predict buying patterns, including electronic records of items purchased in stores, and websites visited during internet sessions. Tracking tools include browser cookies, which record websites visited and links clicked, and mobile phone location mapping. Cookies enable retailers to offer customized ads while browsing. Location tracking generates customized offers sent through text messages to consumers traveling through a particular retail location. Consumer market research firms and large retailers (such as Amazon) know more about the buying patterns of consumers than even the individuals themselves.

The sophistication of data collection and analytics tools for tracking consumer behavior expanded with technological advancement and broader distribution of consumer technology. As more consumer data became available, the ability to influence behavior became more sophisticated and impactful.


Your patient is your customer: engagement strategies that improve outcomes are thus essential.

Similarly, the availability of actual patient data recorded in electronic medical records (EMR) offers a rich source of data that helps stratify populations into risk categories. Previous efforts to identify patients who would benefit from care interventions relied upon untimely claims data lacking in clinical detail. Prediction models used healthcare services utilization as a surrogate for illness severity and to determine of the need for particular interventions. Although relatively effective at identifying patients already ill and in immediate need of disease interventions, these older models proved less reliable in their ability to identify those most likely to see their illnesses worsen in the near future.

Current predictive analytics draw on actual patient data and more accurately identify what interventions would most likely impact clinical and financial outcomes in a broad range of patient disease states. Organizations use this information to focus their disease management programs to intervene both with moderately ill patients and those with a high probability of worsening illness. This enables interventions when the cost is relatively low and potential benefits are significantly high.

Retailers use information technology tools to reach consumers on their desktop and mobile devices, individually customizing “touch events” to engage individuals in retail transactions. With each passing year, specificity of the messages increases, and impact is heightened as advertising and promotion are targeted to individual consumers.

Healthcare organizations can similarly engage with individuals by applying these techniques when applying health management strategies. Healthcare touch events can include text messaging, automated outbound calling and gamification (such as Fitbit) to encourage healthy behaviors. Touch events can reflect the information available in the EMR to personalize the message for the patient.

Unlike patient portals that require individuals to actively access the narrow set of information contained within the portal, “push” technology that actively reaches out to patients on a variety of technology platforms better mimics the successful efforts employed by retailers. In addition, combining data from an EMR with data available from other point-of-care sites enables the compilation of enough patient data to drive effective, customized care interventions.

Merging patient health with consumer data offers a potentially effective intervention strategy not widely employed by providers. It is interesting to think how these two data sources might interact and the value they might provide. Clearly Apple, Fitbit and Samsung see this as a promising area to pursue. Apple actively promotes their Healthkit developer platform, while both Fitbit and Samsung continually look for ways to engage patients through wearables.

Famous department store merchant John Wanamaker said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” Fortunately, by using modern predictive analytics, machine learning models and patient engagement techniques, providers of population health do know where to invest their effort to improve the health of the populations they serve.