In his post last week, John Halamka expressed optimism:
I left HIMSS this year with great optimism. Vendors, technologies, and incentives are aligned for positive change. 2016 will be a great year.
Perhaps we were seeing different sides of HIMSS.
Yes. there is a “buzz” around the migration from volume to value. Walking the floor of the exhibit hall, it was hard to avoid companies – old and new – describing their population health / care coordination / analytics tools.
Yet I didn’t see very much that was really new – really focused on value. I saw re-configured versions of old stuff. One company has re-packaged off-the-shelf tools to create a “population health analytics toolkit.” Their marketing is fantastic – but peeling the onion – I couldn’t find anything that a smart team couldn’t put together themselves – for a fraction of the cost. Another multi-billion dollar company has re-branded the products they used to sell to the payor market – and is now pitching the same tools to the provider/ACO/CIN/DSRIP market(s).
Another facet of HIMSS that I can’t help but notice: insulting the consumer. Do they really think the market is this unsophisticated? (Dare I say “dumb?”) – massive booths, expensive displays – and cryptic product offerings abound. I listened to one company’s pitch, and walked away with my head spinning. I had no idea what they do. I’ve been involved in this industry for nearly three decades. If I didn’t understand it – I’d be very surprised if a new customer can.
This was my first HIMSS in many years where I attended with a buyer’s mindset: as acting CIO for one of the New York DSRIP PPS communities, I was viewing the market through a new lens. If we look carefully at the continuum of the current market – we see silos of activity:
- Data Entry. This is today’s EHR. Despite some rudimentary embedded decision support and quality measure reporting, the EHR is a data entry tool, and unfortunately, the physicians are the ones doing the data entry. Is the UX better than it was a decade ago? Yes. Barely.
- Data and information gathering.
- Note that I differentiate between the two. Data is reliable and based on an objective assessment of the natural world: a lab test result, a blood pressure reading, the fact that a procedure occurred such as a CABG or a BKA. Information is a byproduct of human thought (and therefore subject to a 50% error rate): a diagnosis, a patient’s past medical history, even a medication list should be considered information rather than data. Data has a much better predictive value. Information should always be viewed with suspicion.
- Data and information need to be aggregated, normalized and analyzed. This is the step that is often called “analytics” and is the domain where we see many companies currently engaging.
- Most have descriptive analytics leading their product offerings: they offer a dashboard to show us where our best opportunities for improvement are. Which diabetics are not well controlled? Which patients have used the hospital the most?
- Some companies also offer (or say they offer) predictive modeling of some kind – and will mention the use of natural language processing (NLP) and machine learning (ML) to describe how they are different from their peers. There is no shortage of such companies. If they say their peers are not doing something super special and top secret and incredibly unique – they are usually wrong. Everyone has now invested in a small (and growing) data science team. This is the future. It’s just not evenly distributed. Such tools won’t just tell us which patients were sick or poorly managed, it will tell us both who will be sick or poorly managed – and (much more important) how to prevent them from getting sick. For this patient – which intervention(s) will be best?
- Action. At the end of the analytics event – we’re still left with something abstract: a chart on a dashboard, a list of high risk patients, or even a list of things that could/should be done for a population of patients. It’s a list. A list of who and perhaps even a list of what (they need). For example – i might have a list of who needs a flu shot in my community. We know that a (much) better way to manage this opportunity every Fall would be to find a way to get them all a flu vaccine – but still – in 2016 – the vast majority of the time, we will wait for them to come in to an 8 x 10 exam room, wait for an alert to “fire” and distract a busy physician – and then hope that the alert causes an action. It’s amazing to me that we can’t do a better job than this. “Action” is the silo of the market that’s not yet been cracked. Analytics tools tell us what to do for whom – but they don’t deploy that knowledge to where it can be done. At HIMSS, I saw a tiny number of companies describing such “last mile” solutions – and yet this is the most important part. I know one thing for sure: the EHR (see above) is a terrible place to send the actions. It remains the data capture tool – and EHRs weren’t built to accept actions from elsewhere and / or deploy them to community workers, public health nurses, nutritionists, pastors or rabbis. They were built as the engine end of this value chain – not the caboose.
So if EHR was built to capture data and information, and wasn’t built to catch and deploy actions – then perhaps it’s time to focus on the caboose. Most “care management” and “population health” tools were built for insurance companies – and therefore deploy the actions to a case manager: generally a nurse sitting in an office building. These folks are effective at what they do (managing the care of the 5% of the sickest members of a population) but they don’t scale — and they don’t get out into the community — where the real humans live.
The end of the chain, then, is the community worker, the public health nurse, the individual, the family member. How can we empower these folks? How can we tell them what needs doing? Capture feedback from them (was it done? Was it not done? Why? What other barriers are there to optimal health?)
The caboose is a set of point solutions that leverage the lists generated by analytics. If analytics is the platform, the caboose is a set of applications that deploy the right actions to the right people, and then capture (new) data and information in a much more granular way. These new tools — will replace the EHR in the long run — and will feed data back to it in the short term.
Why was I not so optimistic as John about HIMSS? Because I don’t see the market creating these solutions yet. I see them re-packaging their old stuff and putting “population health” labels on it.
Which just won’t do. We deserve better.
Open the APIs. Build the platform. Trust.