It Doesn’t Make Sense

When we formed Health Monitoring Systems in 2006, Regional Health Information Organizations (RHIOs) were a big deal.  As a young company, we plotted an aggressive course and developed an internal White Paper on the topic.

For two months we researched, emailed and made calls — that was a significant investment for fresh, barely hatched company.  The result of our white paper was clear.

RHIOs didn’t make sense.  CHINs/HINs didn’t make sense.  Even dressed-up tricked out, new-fangled language HIEs don’t make sense.  They would just never become self-sustaining organizations.  They couldn’t.

The benefit from the exchange of data was always tomorrow.  Always over the river through the woods and 1/2 of the way to grandma’s house.

Since that time, we have done a lot of work exchanging healthdata.  EpiCenter collects data from over 400 hospitals and submits it to public health for analysis.  That’s health information being exchanged right there. MediCenter builds on that, reverses the data flow and provides medication history in an acute care setting.

So why does EpiCenter work?

EpiCenter is a purpose-driven example of health information being exchanged.  Between previous experience and work here at Health Monitoring Systems, our staff has worked with about 600 hospitals, pushing and pulling syndromic data getting better and better results.

More importantly, it works because public health receives benefit and public health is willing to pay.

And that is the key point — it works because someone who is willing to pay receives a specific benefit and takes a real interest in it.

The current model being advanced for health information exchange doesn’t do that.  So, it is hard for me to believe that it is somehow going to work.

What would work?

That is a big question.  It might even be a loaded one.  To answer it we need to peel back a couple of layers and determine what information needs to be shared and who benefits from the sharing.

Take the two together and if the benefit outweighs the cost, then you have a formula that works.

I’ll put some thoughts together in another entry here.  Until then email me (editor@hmsinc.com) or add your comment with your thoughts on what will make HIE work.

Andrew Walsh, PhD is Health Monitoring Systems resident expert is public health.  A Johns Hopkins graduate, he attended this years annual International Society of Disease Surveillance conference and has this tongue-in-cheek report.  — kjh

Who doesn’t love a good tag cloud these days? If there had been a tag cloud for 2010 ISDS conference, the largest words would certainly be “meaningful use”, “social network”, and “stakeholder” in some order. So because no one demanded it, here are my musings on these topics, plus a few more that I thought should have been given higher billing.

Meaningful use – Maybe it’s just the timing of the conference, but these conversations reminded me a little of my kids when the toy catalogs come at Christmastime. There was a sense of “You mean we get to ask for stuff and someone will actually give it to us?!?”-level excitement, a belief that they’d get everything on their list, and very little discussion of whether they’d be better off if they actually did.

Out of all the discussion, I thought the most interesting comment was that many providers and facilities are being encouraged to go after public health reporting options with the expectation that public health won’t be able to receive the data, an outcome that lets the senders off the hook. That seems like an important issue, but it was essentially a throwaway comment; there was no discussion on how to be ready to receive the data. Now maybe that’s because the various health departments are confident that they are ready, but I still thought it would have come up more.

Also, after two talks from the ISDS Meaningful Use Workgroup, I’m still confused about what the purpose of their document is. As I understand things, it can’t become part of the federal requirements, at least for Stage I. So is it meant to be a guideline for states when they are deciding what they will actually accept? Or is it just to give providers and facilities a notion of what syndromic surveillance is all about so they know what will be done with the data and what data actually needs to be sent? I would love to be enlightened.

Social Network – It turns out that this meant different things to different people, which led to an amusing panel discussion on “Harnassing social networks for public health surveillance” that wound up being something of a non-sequitor since not all the panelists had the same interpretation. There’s the original notion of a social network as a set of people and the physical contacts that exist between them, which can be used to understand the spread of certain diseases (generally less contagious diseases which require significant contact that can actually be quantified). This overlaps somewhat with the second notion of a social network like Facebook where the connections now exist in a virtual realm, but might also give some information about who interacts with whom in physical space. But then some people are interested in Facebook and Twitter because they are a place where people talk about being sick, which might be another indicator of disease prevalence. And finally, there were social networking platforms like Facebook but specifically set up for people to post data on their own health and talk to other people about specific health-related issues. Talk about overloaded terminology; maybe next year there will be a panel discussion about vectors.

Stakeholder – This word was used constantly, and yet not one presenter made the obvious Buffy the Vampire Slayer joke; I’m not sure if I’m pleased or disappointed.

And now, EpiSanta, I’ve been a good boy, so here is my list of things I’d like to hear more about next year.

Validation – A lot of people were building quantitative, predictive models from data, and many of them paid lip service to validation as a good thing to do and something they hoped to get around to, but very few actually did anything about it. When everyone works in their own corner on their own dataset, overfitting is a major concern. There was even a prime example of it at last year’s conference – someone from Google Flu gave a plenary talk in which they revealed that their trend line showed no signal from H1N1 flu in the Spring of ’09. Why? They didn’t say in exactly these terms, but basically they had overfit to keywords related to “normal” flu patterns. If we’re going to be in the business of making predictions, we need to pay more than just lip service to seeing if those predictions bear any resemblence to reality.

Standard data sets – Ostensibly, much of the research in syndrome surveillance is on detection algorithms. Algorithms are the domain of computer science, and in computer science they compare algorithms on the same data so that they actually have some basis for making comparisons and judgements. If we’re going to focus on algorithms, perhaps we should borrow more ideas from the folks who lead that field. I heard several pleas from public health practitioners for help in assessing the value of using one algorithm over another; such assessments will never be possible until we start making apples-to-apples comparisons.

Something other than the flu! – Everywhere I looked, someone was doing something with the flu – modeling it, detecting it, predicting it, DiSTRIBuTing it. And all of that is perfectly understandable since it is a major public health concern. But it seems like any time a new data stream is created/found/summoned from the ether, we see if it predicts the flu, and when it does we declare victory and move on. And that just makes me wonder – Why do we need yet another data source that shows the same trends? And if everything predicts the flu, what does that tell us about the bar we’ve set?

Case in point – everyone was agog over a research talk about using Twitter to track the flu season (Zut alors, surely such a thing cannot be done!). Given the response, you would have thought we had seen lead turned to gold before our very eyes, and yet all they had was one year of data that was so heavily smoothed that it could have been approximated quite nicely with a second order polyomial. Then they showed their fitted curve, which was clearly a higher-order polynomial that had more structure than their data (overfitting, anyone?) and declared victory without quantifying the fit in anyway or doing any serious validation.

(And no, I’m not bitter at all that everyone thought Mr. Twitter was both brilliant and hilarious, while my abstract was summarily rejected for lack of rigor.)

Hospitals are evaluating their strategies on how to achieve meaningful use criteria.  Part of this evaluation is the three optional criteria geared toward public health.  The question for public health to answer is how to get hospitals and other eligible providers to select these criteria.

The introduction of meaningful use, the impact of HITECH, and the push for health information exchange have made a healthcare IT a dynamic, challenging environment.  IT leaders are looking at strategies to comply with these initiatives, understand the impact on clinical care, and ensure full reimbursement.  No small feat.  In talking to healthcare IT personnel over the last few months, there were a few points of insight gained that benefit public health.

1. Communication. IT personnel have a lot going on.  Don’t assume that they understand the optional criteria for public health.  Don’t assume that hospitals already participating in an established system realize that theyalready meet some of the optional criteria.   Health departments need to communicate with healthcare providers to ensure they understand the approach being taken in their region to meet the public health criteria.
2. Clarity. Healthcare IT personnel need to make plans on how to achieve the meaningful use criteria.  They need clear explanations of how the process will work to contribute to the public health system and demonstrate compliance.
3. Simplicity. IT personnel are looking for an easy win.  The want  a step-by-step guide for participating in the public system, that can be assigned directly to a member of the IT team.  Straightforward processes and answers will go a long way to influencing IT decision makers to select the optional criteria.

The single biggest challenge is bridging the communication gap.  For most of the meaningful use criteria, healthcare has to demonstrate that their system complies.  This means using a certified software system or implementing functionality that can be certified and approved.  Working with a public health data system doesn’t follow the same process.   What process is to be used needs to be made clear to healthcare IT if they are to select the optional criteria.

–kjh