Data-Driven Patient Outreach: Financially Efficient or Morally Compromised?

by | Sep 23, 2013

I recently spoke with a clinical analytics vendor about one of their provider customer’s bizarre decisions – to no longer encourage a subset of their diabetic population to take the HbA1C test. The reason? These diabetics were covered under a new P4P payment contract, which no longer rewarded such testing.

I am not naïve to the fact that, since the beginning of the practice of medicine, patients have been treated according to different care standards…with payment to blame more often than not.

Now our payment models are evolving and population health management (PHM) has become a word du jour. In addition, the payer-provider line is blurring, and data is flowing more freely than ever between the two former adversaries. From a PHM-centric standpoint, new incentives now present themselves to treat similar patients differently based on these newly acquired datasets.

Different Payment, Different Data, Different Outreach

Take for example, massive healthcare systems such as Intermountain and Geisinger. These HCOs own their own health plans, but unlike Kaiser Permanente, also accept outside insurance. Patients can therefore be covered be under FFS, P4P contracts, risk-based contracts, as well as the HCO-specific payer plan. In addition, these HCOs have developed their own advanced care models that should, in theory, override the influences of payment on care. However, things soon get tricky with regards to PHM outreach:

  • If Debbie the Diabetic is covered under FFS, she will get zero outreach or, if she is lucky, outreach based on her sophisticated HCO’s advanced care models — with the HCO acting against its own short term financial interests.
  • If Debbie is covered under an all-upside P4P contract, she will get reminders in order for the HCO to tick off certain boxes in her payer’s P4P contract (process or outcomes-based), for example, to encourage her to come in for HbA1C testing, foot exams, etc. Perhaps there will be minimal effort invested in helping her control her blood glucose better.
  • If Debbie is covered under an at-risk contract that incentivizes global cost reduction (MSSP ACO, ACO with potential downside, capitation), someone will be trying to figure out how to get her cost utilization down. Her claims data will be run through a risk assessment model, and her costs will be projected. If Debbie is sufficiently out of control and really racking up the costs, she will be assigned a high touch case manager.

Note: the important enabler within these different outreach models is different datasets and the analytics software that runs on top. P4P Registries run on top of billing or EHR data. Risk-based analytics runs on top of adjudicated claims data. Within each analytics system, care gaps and associated outreach are tailored toward entirely different payment models.

Giving up on “Hopeless” patients (because the data says so)

There are many more usecases ahead in which data can be leveraged to perform unequal PHM-outreach. Take, for example, the ability of a risk-bearing HCO to predict which patients will actually comply with outreach, and which can be assigned to the “Hopeless” bucket.

Every medical professional can recount various patient horror stories. The mentally ill woman who visits the ED every weekend even though there is nothing wrong with her physically. The morbidly obese diabetic who has just lost a foot but continues to eat himself into oblivion. Despite the repeated pleas of multiple case managers, these patients are unable or unwilling to modify their respective trajectories.

If there is absolutely nothing the HCO can do to change certain patients’ behavior (within a given budget), should the HCO cut its losses, ignore the Hopeless, and focus resources on other patients?

Shiny new predictive models could be used to this end. Think of the growing number of variables with predictive power beyond ICD, CPT, and charges: BMI, race, zipcode, past outreach responses, self-generated fitness data. Let’s enter morally shady territory and include variables sold by data scrapers: real estate transactions, courthouse documents, credit worthiness of facebook friends, etc.

Running Debbie the Diabetic through this predictive model, we might learn that there is a 1% chance that she will respond to automated outreach, and a 15% change that she will respond to high touch outreach. It may be financially efficient to focus care management resources on other patients, but what would Debbie’s doctor say about this?

The medical establishment isn’t exactly in love with unfamiliar predictive models that use sensitive data sources to tell them which patients will respond to outreach. Clinicians usually already know who these patients are within their panels — and believe in a more equal approach to outreach.

As providers continue to disrupt themselves via value-based reimbursement, PHM-outreach inequalities will be just one of the many contentious problem areas going forward…and the desires of risk managers and clinicians will continue to collide. In my conversations with vendors and end-users for the Clinical Analytics Market Report research, I for one was surprised at how nonchalant my interviewees were in discussing this morally ambiguous territory — and I have yet to hear the patient’s voice.



1 Comment

  1. MedicalQuack

    It does get interesting and the profit making machine is complicated by all means. I will keep my focus on the data selling where banks and companies are striking it rich in a world where there’s no regulation and no indexing as to who they are, reason I keep promoting licensing all who sell data as by comparison, you can’t get married, practice medicine, sell real estate and so on without a license so there’s no indexing to identify the masses making billions and yes there is the marketing that goes along with this.

    Sure there are P4P programs out there like with what United is doing with Walgreen’s pharmacists, and Walgreens by the way makes about a billion a year selling add on banks and tons of other companies out there, easy money..running some data mining algorithms, create new analytics for sale and off you go. There is a big push to identify consumers with “pre” conditions as it creates more data for sale and like you indicated doctors know and with the move to give pharmacists more interactions with patients with knowledge is a good thing, but remember the doctor works for you and the pharmacist works for the drug store:)

    A good ethics discussion is what occurred at Penn State with penalizing members for not participating with using the WebMd portal and entering their data. Since the outrage and press coverage it was removed and many wellness programs are owned by insurers to boot. Insurers make big money on data as you can see with the United mining through the records of Mayo Clinic now to create analytics and studies to sell. Kind of really competes with the FDA Sentinel initiative but they figured out why give away data when you can sell it. This is why I tell everyone to pay attention to the subsidiaries of insurers, ever looked? Breathtaking..
    If you follow the money and the data you get some answers. I looked at the WebMD portal that Penn State wanted all to use and let’s go back to PHRs, how many consumers jumped on that band wagon, not a lot and no role models either from HHS to help promote that either, other than saying you should get one:)

    The WebMD portal is all about behavior modification to the point of being a broken records..and in essence the marketing here is stating that all members at Penn State need this and the portal is the answer to fix all your members:) Penn State needs money, WebMD needs money and thus the push to get as much data in there as that relates directly to what they can sell.

    It also interesting to look at the big fundings in mobile health and when you look at them most have a business model to sell data, and how this gets implemented is when we go back to motivation and context.

    There’s absolute good reason to be a skeptic as to the context of the data queries and sold and just yesterday I see ZDNet joining in on that as well and I have been talking about context for a couple years now and keep one of the absolute best videos from Charlie Siefe in the footer of my blog as it makes you think about context as our biggest fear is having data used against us, out of context. The ZDnet video said yes it will happen and software is about ready to consume itself, and I agree with that as a few months ago I said half of all the analytics sold will be a waste with agreeing with some Australian bankers on that topic:)

    Great video, “It’s all about context”…and there’s 3 other great videos in my blog that also introduces the layman to how mechanics of the data and math models play in here and the message is be a skeptic when you think you need to be one as we don’t get to see behind closed server doors as to what models are running and what degrees of segmentation are created for profits.

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