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Connecting the Dots with Payments and Patient Data

I know that this isn’t a healthcare blog, but I think it’s important to provide some context on the healthcare industry in order to make connections between it and financial institutions.  Last week I started the discussion around health reform and I’d encourage you to revisit my previous blog post.  Healthcare reform is really health insurance reform, since so many of the new regulations hit that sector.  Payors (insurance companies) have only themselves to blame for the new regulations; they’ve had every tool at their disposal for years to make most of the changes required by Obamacare while still making and saving money.

Common Ground

Under new laws, it’s not just the payors that want to see costs reduced.  Thanks to the establishment of Accountable Care Organizations (ACO: a network of hospitals/clinics that are responsible for the care of a certain patient group), providers are forced to lower costs and improve overall health in order to get Medicare reimbursement.  This idea is called fee for health (vs fee for service). The big metric for providers is re-admittance, meaning that if a patient is readmitted within a certain time period for the same condition, the provider won’t be eligible for reimbursement and will likely face a fine.  So what does that mean?  Basically, quality of care is in and quantity is out, which should mean far fewer redundant tests as well as the unnecessary, expensive procedures. HC analytics

So how does the healthcare industry cut costs and foster a healthier patient population?  Well, there are plenty of ideas that the industry can borrow from both financial services and retail examples.  Because retailers are in the business of selling, they’ve made a proactive decision that they’re going to know their customers.  What they ended up doing was getting to know their customers better than they know themselves by aggregating massive quantities of data.  They know what (goods and services), when (time, weekend vs weekday, payday, season), and how (credit, debit, mobile wallet, online, Groupon) you bought something.  Through loyalty programs, you—the customer—voluntarily give them your information, which is used to push offers, or even remind you that the toothpaste you usually buy is on-sale.  Retailers are now beginning to push location-based offers through mobile channels.

The healthcare industry—specifically payors—have every bit of data it needs to control costs; the problem is that they’re not using it correctly.  They know your complete medical history, which is about as intimately as you can know a person.

Applying Payment Analytics

So take the retail example above, but let’s say that instead of Joe buying toothpaste, we’re talking about Linda, who’s a diabetic.  She’s supposed to refill her insulin prescription every four weeks, and her current—or thanks to Obamacare, soon-to-be—insurance company likely assists with those costs.  Her busy lifestyle and schedule cause her to forget to refill her prescriptions on-time, putting her eyes, liver, kidney function, and other keys bodily functions at risk.

Now what if her healthcare providers were sharing data, specifically her payment data from pharmacy transactions?  What if she had a mobile app that linked that data together?  What if it also offered the ability to store payment info, and at participating pharmacies, she’s able to use that mobile wallet to pay (or even act as an authorization to bill the insurer)?  If that were the case, Linda’s insurer could push notifications reminding her to pick up her prescription, even using location-based data saying, “Your prescription is past due to be picked-up, and Walgreens is .2 miles away”.  By notifying Linda, the insurance company can head-off a potentially catastrophic and expensive medical episode; predictive analytics, anyone?  They may even save her life (insurers have a financial incentive to keep their customers alive).

Like so many non-health insurers, cell phone carriers, and cable companies, health insurers could reward their customers for on-time transactions (chronic prescription pick-up) by using payment data.  Chances are, if you’re continually refilling your cholesterol medication, you’re taking it.  When you aggregate that data to an entire population group, the savings—both costs and human—are exponential.

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