anya.vane

What Big Data Brings to Health Care

Blog Post created by anya.vane on Jan 10, 2017

I’ll admit: I’m a sucker for banner ads. Their algorithms just know me so well, and offer me such compelling products. As they should: these advertising algorithms know me. As I browse social media sites, store fronts, blogs – virtually any place on the internet – I generate huge amounts of clickstream data. We all do. Marketing agencies buy and analyze that data in order to build what is called a "360° view of the consumer" This scope enables them to deliver more targeted ads and services. Now, every time anyone loads a page, they receive an experience that is totally unique. The internet knows what to show, specifically, because it knows what that person, specifically, wants and needs.

 

We call that process "scoring." As one generates data, whether through browsing or just living life, he or she generates data. This data often follows a predictable pattern of behavior. AI (artificial intelligence) data models make those predictions. The more adaptive the models are to new and changing data, the better they predict. Mapped across populations, they can discern certain patterns that identify individuals who score highly in the model. These high scorers are the audience. Certain actions, like ads, can be designed to appeal to that audience, and subsequently be executed for the individual, who will ideally behave as planned; they’ll click the ad. But to limit this potential to the commercial realm misses its potential.

 

What if our health care did what marketing does already? The algorithms that tailor ads could similarly empower doctors with a multi-dimensional, 360° view of their patients. With it, health care providers could offer their patients the care they need, when they need it, in the method they’re most receptive to.

 

Consider the powerful link between smoking and lung cancer. That a patient smokes is data. That they’ve smoked for 20 years is data. That this 20 years of smoking puts them at high risk of cancer is a model. Reaching out to them about cancer screenings is a decision. Deciding whether and how to reach out to change a patient’s behavior is a strategy. This is the same formula that an ad agency uses when determining what banner ad to show you, but instead of motivating you to buy a gadget (or in my case, organic meal delivery services), it can maximize the efficacy of the healthcare you receive.

 

It’s not perfect in its predictive power, of course, but with the ability to piece together seemingly inane data into a 360° view of a patient, the same artificial intelligence that advertises products can be an extremely valuable tool for improving health care and outcomes.

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That’s easy enough to theorize; implementation takes more effort. But this isn’t some fantastical scheme – the analytic tools needed to collect and analyze the data process it according to certain rules, and return a viable health care solution already exist. At FICO, we call it a Propensity Score. With it, healthcare providers can use a proprietary combination of rich third-party data to build a 360° view of the patient to determine the patient’s environmental health risks, likelihood to engage, and level of responsible healthcare consumption. This data can be a lot more reliable than a patient’s self-reporting.

 

After all, whether one is likely to quit their treatment protocol halfway through a prescription isn’t the sort of information that a new patient questionnaire can capture. It’s not really the sort of information that can be captured at all. Health information is, after all, extremely private, and not always obvious even to the patient. Patients don’t necessarily know that certain environmental conditions are strongly correlated with diabetes and obesity (NOTE: diet and physical activity are strong indicators of overall health, including ones’ risk for diabetes and obesity). That’s what health indicators – data beyond the scope of a patient’s secured records and self-reporting – is for, and fortunately, these can be found virtually anywhere analysis occurs.

 

Once risk is identified, providing the right messages at the right times via the right medium can dramatically improve a patients’ receptiveness to treatment. If a patient isn’t demonstrably concerned with their health, a form email is not the way to get them into the office. Direct contact, like a phone call or home visit, might be more impactful. How a system measures a patient’s likelihood to engage with a doctor’s chosen method of communication is determined by how the question is defined, but could be informed by virtually anything: medical histories, lifestyle, level and focus of education, or even online habits. Knowing how to engage the patient is almost as important as identifying health risks, and in this way can be just as data-informed.

 

Once the patient is willingly through the doors and seated on the crinkly paper-covered exam bench, care in the truest sense of the word must still be provided. Again, data processing can help the provider determine the most successful treatment plans. Now that the patient is here, a practitioner can know how to allocate their effort and resources to best serve their patient, based on that patient’s expected behavior. A skeptical patient might benefit from a few extra minutes frankly discussing risks of their illness. A complicated treatment plan might be more successful with careful take-home instructions, and a follow-up call one week into treatment. This well-informed plan of action can extend the period of wellness that follows.

 

For health insurance providers, providing effective care to patients is core to the job. Yet it can be incredibly complicated, because patients are diverse. Every patient has a different physical body and medical history, lifestyle, and personality. Data surrounds every facet of that being and can therefore provide tremendous insight. Knowing what one does about a patient – descriptive data – can predict that patient’s respective risk for any ailment – predictive data. Determining the appropriate treatment and method of communication is an act of decision modelling. United in a single program, this information and decisioning power can help actually modify patients’ behavior, and return better health outcomes.

 

Such technology is already at work in the advertisements that convince me to buy overpriced organic popcorn. Imagine how lives could improve if healthcare shared that power.

Outcomes