During National Health IT Week, champions from across the industry are uniting to share their voices on how health IT is catalyzing change in U.S. healthcare. The following post from a National Health IT Week Partner is one of the many perspectives of how information and technology is transforming health in America.
You probably won’t find many clinicians or healthcare executives who will disagree that the ready availability of fast food and unhealthy snacks from consumer package goods (CPG) companies has been a major contributor to many of America’s health issues. The incidence of obesity, diabetes, and many other conditions has risen sharply since the 1950s, when speed and convenience began to outweigh nutritional value.
Yet the good news is that these industries may also hold a solution that can reverse these trends, helping clinicians improve the health of those populations. That solution is the development of personas – profiles that are developed using big data analytics and then applied to cohorts of individuals who share similar characteristics or attributes.
CPG companies are particularly adept at developing personas, which they use to make marketing decisions to increase sales. By looking at socioeconomic data about age, gender, geography, household income, education and other factors about their customers, then analyzing that data along with purchasing patterns, CPG companies can predict what product mixes will sell in what quantities in a given store, helping them maximize shelf-space profits.
Healthcare organizations can now apply the same principles. They already have an incredible wealth of data about their patients and their health. What they need now is to bring in the same type of socioeconomic and geographic information combined with clinical and financial risk to help them gain a 360-degree view of who these patients are and how they live, then run all this data through behavioral analytics to learn how best to get them engaged in their own care.
The care team as the “trusted messenger” can talk for days about how patients should eat right, get more exercise, fill prescriptions and use their medications, and take other steps to improve their health. But the reality is none of that has any effect until patients are actually motivated to action.
Take the example of a patient with diabetes. The clinical and claims data show the condition exists but does nothing to look at the underlying factor. An examination of zip code data (especially household data such income and education), however, shows the challenges a patient who fits that persona will have. Perhaps they are lower income with a high school education or less. An examination of that zip code shows that the options for obtaining fresh, healthy food are far fewer in that neighborhood or town than fast food options. Socioeconomic data further shows patients who live in these areas tend to hold lower-paying jobs that demand long hours, making it even more difficult to prepare healthy meals or exercise regularly.
The conversation a physician, nurse, or care manager should have with this diabetic patient should be very different than the conversation with a patient who lives in an affluent neighborhood where only one spouse works and they tend to have domestic help available.
Another example of an area where geographic data can impact medication compliance revolves around mailboxes. While it makes sense for patients with chronic conditions to order medications through mail order pharmacies, those who live in apartments or other housing where mailboxes tend to be very small are less likely to do so because their prescriptions won’t fit into the narrow mail slot in the box. The result is those patients will need to pick up their prescriptions at the post office.
But what if patients whose personas include mobility issues live in an area where there is no convenient public transportation to travel the eight blocks to the post office? The prescription is likely to go unfilled and the patient’s condition will worsen. Understanding the challenges these patients face can help clinicians suggest alternatives that have worked for other patients who share that persona, improving engagement with the plan of care and, more importantly, the health outcomes those patients achieve.
Data that shows credit card purchasing patterns can also deliver helpful clues as to how motivated patients with different personas will be around different suggestions – and how clinicians can motivate them.
For example, credit card data that shows a patient belongs to a gym, makes frequent purchases at sporting goods stores or online outlets, subscribes to outdoor publications, participates in 5K races and so on are strong indicators that exercise suggestions will be well received and incorporated into a patient’s daily or weekly routine. The chances for success are much higher than for a morbidly obese patient with no record of interest in such activities – especially if patients with that persona tend to avoid exercise entirely.
We also know that when patients have high FICO scores they show behaviors of compliance with their bills and hence tend to more complaint with their care plans and recommended and treatment regimens. In addition, healthcare organizations can leverage the emerging area of gaming which uses financial incentives to drive behavioral changes with certain patient populations. In a recent analysis of Medicaid patients for every $1 earned from a compliance/wellness incentive they took $0.90 cents in points to redeem for gifts.
Personas have long been used in ways that contribute to health issues. Now it’s time to turn the tables on this technology to help motivate patients to do the things they need to do to get healthier.
Using predictive, behavioral analytics to create patient personas that are prescriptive at the patient level can help time-challenged clinicians gain a better understanding of patient needs and motivations faster, enabling them to determine the best ways to engage patients in their own care.
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
Healthcare Transformation | Access to Care | Economic Opportunity | Healthy Communities
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