By Mary Lou Mangan-Lamb
As consumers, we are profiled and analyzed every time we go online. A few years ago, that idea would have been overwhelming to the average consumer. Today, it’s expected.
Companies tap into what people like to eat and drink, how we purchase consumables, where we like to shop, what shows we might like to stream, whether we vote, and so on. If you have ever created a profile on a streaming application (think Netflix or Amazon), you will receive recommended books, movies and other items just as soon as you start surfing. Gartner defines this type of customer analytics as the use of datato understand the composition, needs and satisfaction of the customer. Also, the enabling technology is used to segment buyers into groups based on behavior to determine general trends or to develop targeted marketing and sales activities.
If you’ve followed analytics for some time, you might be familiar with the story about a statistician at Target named Andrew Pole. Pole was a member of the Guest Marketing Analytics team at Target in the 2000s and 2010s. He was tasked with using data to predict whether a woman was pregnant, so that Target could then start marketing to that woman with pregnancy- and baby-specific items. The marketing team specifically wanted to pinpoint women in their second trimester, as that is when their baby-related shopping kicks in. The goal was to capture that woman as a customer, so that she would do more of her day-to-day shopping at Target. Pole’s work was both successful and concerning to some Target shoppers, and Target eventually made changes to their algorithms to market a little less specifically to this segment of shoppers.
Target’s focused analytics can be used in any type of business, including healthcare. Gartner’s principles of definition and segmentation, for example, apply in healthcare, just as they do in the Target story. It makes sense that healthcare organizations would want to tap into consumer analytics. Determining how and when to engage a patient is critical information.
Analytics supports care coordination
A perusal of the analytics section of the Healthcare Innovation website returns results related to scanning electronic health records for signs of chronic kidney disease, leveraging artificial intelligence to address avoidable inpatient readmissions, utilizing AI to identify those patients who are at risk because of delayed care, and the utilization of machine learning to predict mortality outcomes.
The effective application of health analytics could greatly improve the coordination of care for a patient, particularly crucial for people who see multiple providers for complicated, chronic conditions. The Agency for Healthcare Research and Quality (AHRQ) defines care coordination as deliberately organizing patient care activities and sharing information among all the participants concerned with a patient’s care to achieve safer and more effective care. This means the patient’s needs and preferences are known ahead of time and communicated at the right time to the right people to provide safe, appropriate and effective care to the patient.
Should we apply consumer analytics to patient care?
Some of the challenges involved in using consumer analytics to influence health-related decisions include a lack of trust, as well as a general feeling about healthcare not being as exciting—though certainly more important—as other sectors. A consumer who might be excited to learn about her new film recommendations on Netflix might not be quite as excited about her health provider using her data to effectively remind her about her annual mammogram. Clinical teams will need to help ”normalize” this with their patients. One digital health consumer survey showed “55% of consumers surveyed said trusted healthcare professionals would motivate them to take a more active role in managing their health, yet only 11% said their provider recommended the use of digital tools for patient health management.”
Patients are a critical part of the clinical team. One might compare the patient to being the quarterback of their own care team. Nothing gets set in motion until the patient is handed the ball. These reminders can easily be seen as “the ball.”
When healthcare providers use consumer data to effectively connect with patients, those providers might be more likely to encourage people to seek the right care at the right times. Might it be possible for a payer or provider to better know a patient, so that they could effectively reach that patient to come in for preventive care? Could a person’s zip code or marital status affect their medication adherence? The data taken from a wearable device might help a provider understand a patient/consumer’s behavior, thereby creating a more direct route to education for that patient. There’s significant potential to improve patient care for individuals based on their consumer data.
There are inherent issues and challenges in collecting and analyzing many types of patient data, as well. Privacy is a major concern, although we expect our healthcare providers to have all the details about our care and conditions. In 2020, the same digital health consumer survey cited above found that trust in hospitals and doctors to keep digital healthcare information secure is still high at 84% and 83%, respectively. Nevertheless, we may be surprised and even upset when we get a text or phone call saying we may need a certain procedure or test based on where we live or what we buy.
The thought of using analytics may not be on everyone’s radar, but it is becoming more and more commonplace in healthcare today. In the future, as we do more online and more information about our lifestyles become available as data points, there’s a good chance it will become very specific and very targeted.
The big question: are healthcare consumers ready?
Mary Lou Mangan-Lamb is Director of Product Consulting at MedeAnalytics.