Clover Health, an insurance startup trying to use data to keep customers healthy

I agree there needs to be better health care data science going on towards improving wellness and health care (assuming data is collected, stored and analyzed honestly and appropriately - whatever that means these days is whole other debate).

But I think it would be helpful to everyone if there was a bit more transparency and clarity from health care related startups about their plans and what they are doing. For example:

It sounds like a couple very separate things are going on here:

1) You are a health insurance company

2) You are an organization doing medical patient data collection and data analyses and pushing real-world actions based on this analyses to try to improve wellness and prevent/reduce expensive medical problems.

The first is relatively straightforward, the insurance business is really just a math equation with a marketing department. Health insurance and Medicare are a bit of a special case because the government has said health insurance companies must pay out most of the premiums to medical care costs or give the money back. Though this government contract component creates a numbers game with a single digit profit % built in once you scale (you can also do a decent back of the envelope valuation just discount your member growth as other inputs and comps are ~standardized).

The second thing, medical data science is where it gets complicated. This is serious work that needs to involve doctors and medical research professionals and studies before anything major can happen. I think it's great if an insurance company wants to take some of its revenue and put it towards data science focused on researching better wellness and health care. (Let's ignore for a moment that the incentives from a business model and profit potential are not clearly aligned by doing this, eg you don't get to keep medical care cost savings, you give it back).

More of an issue is the ambiguity around stuff like the "promise of using technology to solve such a large and socially significant problem as health care is one of Clover’s most powerful recruiting tools" and then goes on to say a guy from Yammer and engineers from FB and Google are mining health data to "suggest ways to prevent them before they happen." What is that suggesting process and who is involved? Recommendations of brown rice and gym memberships to a couple of octogenerians is funny and relatively mild (and kinda pathetic tbh). But it's a risky slippery slope when potentially unqualified and premature medical data analyses conclusions start to be implemented in the real world, it could be even dangerous to people's health, the opposite effect, even with the best of intentions. The example from the article could go bad: have you studied a potential rise in injuries when you recommend gym memberships to 83 year olds? If this has been properly studied and implemented make it public.

Some of the mid-size companies are okay at this; but the low-end guys are very, very unsophisticated (there are a lot of local / state plans as mandated by various state laws) and the super-big guys just have so much stuff they have no idea how to integrate it all. Think 15-20 different, incompatible claims systems with overlapping numbering schemes, different versions of schema, different versions of underlying DB required, etc. Then do the same with provider management systems, contracting systems, etc. You get the point.

By the time you get halfway through scoping out a project, the architecture will be obsolete. Oh and also the federal government keeps changing the rules about ICD-10, ACA, Medicare reimbursement, and a whole bunch of other stuff, so make sure you add in that capability to all existing systems and don't get sanctioned. A lot of companies are having a hard time keeping their heads above water.

The big guys are going to eat the data-hungry guys. Or rather, the big guys will buy the best of the new data-first health plans (Clover, Oscar, etc.) for their platforms and migrate everything onto them.

100% agree. Health insurance data is about as garbage of data as you can get.

Many providers still fill out claims by hand and fax them in. A good many more have an EMR system that just automatically faxes it for them. All this faxing and handwriting and manual data entry means numbers get transposed and data is wrong all the time. Add to this the fact that the people at the doctor's offices filing the claims often have no idea what they're doing and will often file things completely incorrectly (i.e. under a provider code instead of a facility code), leading to a rejection.

I cannot overstate how shitty this data is. Across the board. EMR systems are getting better, but generally only younger doctors in independent practices use the more cloud-based/big data ones. Big hospitals and the like are still generally on proprietary EMR systems (and that doesn't mean it's any better; different departments can have different billing practices that still screw things up).

It will take AI to clean up this mess. It's just too complex for humans to tackle.

A good point to discuss. Perhaps I'm overly optimistic about people in general, but how do we know, a priori to a data-centric healthcare model applied to a patient, about their participation? These tests are only wasteful in the context of the current care model, which stipulates that once you have Type 2, there is a lockstep progression of increasingly invasive and expensive medication and interventions, graduation to insulin injection, and culminating in early death from complications? In the face of that kind of prognosis, it is not at all surprising that any additional tests are considered futile by both medical practitioners and patients alike. But if the patient was offered via data-centric care a clearer window to their condition, amplified proactive participation in care management with negative feedback loops tamping down undesirable fluctuations, and in the case of Type 2 or metabolic syndrome the clear goal of reversion (though not a cure) and drastically improved eventual outcomes, why do we assume that even elderly patients on Medicare Advantage would not statistically respond well as a whole population? I dunno, this isn't my domain expertise, so I'm honestly asking these questions; if there is the equivalent of behavioral economics studying patient behavior, then perhaps that field has the answers I'm seeking.

> There's an entire cottage industry devoted to solving this problem for insurers.

I'd like nothing more than to see these efforts succeed, look forward to following the progress.

I also didn't assume random engineers are making care decisions. I said this is ambiguous and I asked a question: What is that suggesting process and who is involved?

The article says you are "developing software to organize the trove of data coming from workers on the ground in New Jersey, mine it for likely health problems and suggest ways to prevent them before they happen." Your website says "we’re also partners in care, using patient-centered analytics and a dedicated care management team to identify potential risks and directly provide preventive care."

Well nurses generally don't know much about data science software and data scientists generally don't know much about practicing medicine or preventive care.

And of course you are not a tech-led company. You are a marketing company and government contracted middle man connecting health care services to patients and handling some of their paperwork and payments. Still this is an industry entirely living off regulatory capture.

I'm not accusing you of denying someone's valid insurance claims because they didn't take your eat brown rice suggestion.

But you yourself claim to be an insurer "trying to change the statistics". I get the preventive care and data science shtick looks good when marketing to potential members and to potential investors. But it's not clear how you are taking on the "change the statistics" part you claim to be, which is the most interesting and important part of all this to the extent it is real as improving peoples' health and wellness, and preventing serious health problems would be amazing!

So I asked a question about how the process works and who is involved as getting data and actionable data analyses to and from health care professionals in the field is not straightforward, nor is it trivial, nor is it explained anywhere.