My Five-Part Series on Interoperability Is In Healthcare IT News This Week

  1. Achieving task and workflow interoperability in healthcare (Monday)
  2. A look at what healthcare task interoperability means (Tuesday)
  3. Laying down a definition of workflow interoperability
    (Wednesday)
  4. Bridging the gap between healthcare data and healthcare workflow
    (Thursday)
  5. Achieving workflow interoperability among healthcare organizations
    (Friday)

Coincidently, tonight’s Healthcare Leadership (#HCLDR) tweetchat is about interoperability! My answers to its four questions is sort of a highly condensed executive summary of my entire five-part series: Healthcare Data Interoperability and Workflow Interoperability: Four Questions (and Answers).

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Healthcare Data Interoperability and Workflow Interoperability: Four Questions (and Answers)

This post is prompted by this week’s Healthcare Leadership (#HCLDR) tweetchat. It’s at 8:30 EST on Tuesday. The following questions come from @JoeBabiain’s tee-off post the Challenge of True Interoperability and Why It Matters. (By the way, please also check out my five-part series on Healthcare IT News this week: Achieving Task and Workflow Interoperability in Healthcare.)


T1: How urgent is the need for true healthcare data interoperability and why?

Even more urgent than data interoperability is workflow interoperability. The latter is a layer of interoperability above the former. Data interop is about getting messages from one system to another and having them mean the same thing in both systems. Workflow interop is about messages having the intended effect of the sender of the message. Much health IT investment and software development activity today is putting down a layer on top of legacy EHR and health IT systems. Much of this activity is about getting to workflow interoperability.

To some extent, data interoperability is a prerequisite for workflow interoperability, but not completely. Workflow interop can strategically compensate for problems at the data interoperability level. For example, workflow technology can escalate data interop problems for more intelligent automated handling or even human intervention. Consider this extreme example. Before data interoperability existed between EHR and health IT systems, some degree of workflow interoperability already existed. How is this possible? When a physician clicked a button to send a document to another organization, she or he did not care how this was accomplished, merely that was accomplished.

Before any data interop even existed, humans did the necessary work to achieve workflow interoperability. It was inefficient — involving copy machines, faxes, phone calls and sneaker net — but it was intelligent. Workflows were intelligent because the humans carrying them out were intelligent. They understood the purpose of the communications, so they tried to do what was necessary to achieve the intent of the communications. Today we have increasing data interoperability, but we’ve lost some of that intelligent workflow processing along the way. We need to marry together both data and workflow interoperability to get where we need to go.

Workflow interoperability essentially requires models of workflow and work, and their automated interpretation by workflow engines, AKA orchestration or process engines. Therefore workflow interoperability requires workflow technology.

T2: What experiences have you had with lack of interoperability?

My experience with lack of interoperability is that of a programmer working on EHR interoperability for a vendor. Typically, our EHR customer would approach us about interfacing with some source or recipient of patient data, such as clinical labs, e-prescribing, vaccine registries and such. So my experience was that of moving from a state of data and workflow non-interoperability to a state of data and workflow interoperability via use of interface engines, message parsers, and configuration of incoming and outgoing patient data workflows.

T3: Do you see incumbent providers willingly getting “on board” or will further market forces/regulation come into play?

First of all I’ll assume by “getting ‘on board’” you mean “Ready to participate or be included; amenable.”

I’m not sure if you mean for “providers” to mean clinicians, or providers of EHRs and interoperability solutions. However, it is an interesting question in all three respects. In all three cases, providers are essentially already “on board.” Healthcare interoperability is like motherhood, apple pie, and the American flag. Everyone is for healthcare interoperability. The main exception is that EHR vendors get a lot of flack because they are perceived to be “information blocking.”

While I am sure that there is some of this going on, on the whole, I don’t believe this is the main, root cause of healthcare’s “interoperability problem.” (I also know I am probably in the minority in this view.)

The contentious nature of healthcare interoperability, especially regarding blaming corporations and/or the government, reminds me of some discussions of why HIEs (Health Information Exchanges) aren’t as successful as everyone hoped. The reasonable, and accurate in my opinion, view has been that there’s been a lack of sustaining business models. Exactly the same point can be made about into why we’ve not better achieved healthcare data (and workflow) interoperability. If we can figure out how to better incent healthcare interoperability, then we’ll make better progress, goes an increasingly popular view.

Part of the reason we lack sustaining healthcare interoperability business models is that obsolete workflow-oblivious technology makes even the possibility of interoperability too expensive. This is the link between business models and technology models I’ve written about before.

The point I’d like to make here is that business models do not exist in a vacuum. They rely on technology models. Our current health IT infrastructure is notorious (in my mind, and in more-and-more other minds as well) “workflow-oblivious.” Without what academics called “process-aware” information systems, data and workflow interoperability are simply too difficult a problem to solve effectively and efficiently. In other words, a major reason healthcare interoperability has not been forthcoming, is that our fundamental health IT infrastructure lacks necessary architecture characteristics for transparent and flexible workflows within and among healthcare organizations. So, the healthcare interoperability problem still boils down to lack of sufficiently sophisticated workflow technology within and among healthcare data-exchanging partners.

T4: What can we as healthcare leaders do today to change the current state of interoperability? Can it be done?

Yes, workflow (and data) interoperability in healthcare can indeed be accomplished. Open discussion forums such as the weekly #HCLDR (Healthcare Leadership) tweetchat are extraordinarily important to getting to true workflow interoperability in healthcare.

I am sure most readers of this post are familiar with the healthcare Triple Aim (and related Quadruple Aim). I have a Healthcare Workflow Triple Aim, which I wrote about in my Health IT Workflow Silo post in HL7Standards.

  1. Educate healthcare leaders, clinicians, health IT, and healthcare social media influencers about healthcare workflow and workflow technology.
  2. Highlight healthcare workflow and workflow interoperability successes, in terms of healthcare organizations, IT vendors, and stakeholders.
  3. Recruit the best minds in workflow technology, from both inside and outside of healthcare, into accelerating use of process-aware technologies to facilitate true workflow interoperability.

Are we making progress in regards to the Healthcare Workflow Triple Aim?

Yes!

I see plenty of evidence that all three legs of the Workflow Triple Aim are materializing into existence and lots of wonderful synergies are occurring between them! (Boy is that a mixed and mangled metaphor!)

My evidence? Too much to go into here, but I’ll leave you with this mornings’ Today’s Thought… :)

… which I wrote before I knew I would be writing this post.

Anyway, I look forward to tomorrow evening’s #HCLDR discussion of interoperability!


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Healthcare Trade-offs, Shared Decision Making, Vulcan Mind-melds, and a Marriage Metaphor

This post is prompted by Colin Hung’s excellent introduction to healthcare trade-offs setting the stage for this week’s #HCLDR tweetchat.

If you’re not familiar with each Tuesday’s 8:30PM EST Healthcare Leadership tweetchat, you should be. It covers everything and anything in healthcare, and often health IT related subjects. Moderators are Joe Babaian (@JoeBabaian), Colin Hung (@Colin_Hung), Bernadette Keefe (@nxtstop1).

I keep an eye on upcoming #HCLDR topics, for, off course, anything workflow! If workflow is mentioned and I’m not there, someone invariably pokes the “workflow bear” to alert me.

When I tweet or write or talk about workflow, I tend to be quite emphatic. I’ve been thinking about healthcare workflow and workflow technology for decades. Have had lots of practical experience, both successful and unsuccessful. So I’m very opinionated on the topic.

I’m not that way about healthcare trade-offs. There are many different perspectives and practices and what I know, or think I know, is literally several decades old! However, sharing it may be of some use, some useful grist to chew on, in a larger multi-disciplinary and heterogamous discussion and community. Plus, I’d love someone to update me with where I can go next.

I first became aware of, and interested in, the very idea of “trade-off” during my freshman year in engineering at the University of Illinois. I believe the course was Engineering Economics. Here is its definition from Wikipedia.

“Engineering economics … is a subset of economics for application to engineering projects. Engineers seek solutions to problems, and the economic viability of each potential solution is normally considered along with the technical aspects.”

It was so interesting that after a couple years I switched majors, out of engineering into Accountancy (yep, I was a pre-med accounting major, only one I’ve ever heard of). I considered economics, but much of it seemed, well, removed from reality! :) My advisor mentioned I should consider “Financial Engineering’” i.e. Accountancy. At the time healthcare cost inflation was constantly in the news, so that was when I began thinking about healthcare costs and benefits and decision-making. Even thought I was an “Accy” major, I got my fill of Econ though, around a half-dozen courses, mostly requirements for the Accountancy degree.

Why am I going on about these courses? Because economics is frequently touted as the “science of trade-offs.” Colin’s post and upcoming #HCLDR tweetchat subject reminded me of that phrase, which is why I start here, regarding my interest in healthcare trade-offs.

I say “start here” because I’ve meandered and strayed a far and wide distance from purely economic models of trade-off. Into psychological models of decision making, into behavior economics, into computer models of decision making, and even into anthropology, neuroscience, and medical ethics.

It was in my medical school medical ethics class that I first encountered the idea of shared decision-making (though it wasn’t called that yet). I was fascinated to see that some of these models where based on the economic models of decision-making I’d been exposed to as an undergrad and then as a grad student in Industrial Engineering (yep, I sort of returned to the engineering fold).

By the way, if you are an economist or a expert on shared decision making, the following is what my wife calls the “Cat, Dog, Tree” version: the simplest and smallest number of ideas that can only go together in one way and achieve a goal. (If you put a cat and a dog and a tree together, the only way the story can end is with the cat up the tree and the dog barking at its base.)

A decision tree is a flowchart (hey! That’s sounds like workflow!) leading from a decision-requiring situation, such as whether or not to have surgery, through decision alternatives (have surgery, don’t have surgery), and finally to possible outcomes (surgery is successful, surgery is unsuccessful, condition resolves, condition does not resolve). Each of these possible outcomes has a probability of happening (given that the decision that flows into it was made) and a utility, or value, sometimes measured in monetary terms. The following is an example of a decision tree from Wikipedia.

decision-tree-elements

By following every possible path, and multiplying probabilities times utilities, expected utility for each possible decision is estimated. The “correct” decision is the decision with the largest expected utility. If you’re uncertain about some of the numbers you plugin, then you’ll play around with them to see if it matters or not (sensitivity analysis). With respect to shared decision making, about potential benefits and costs of contemplated medical course of action, there are several important points to be made (for my purposes).

First, the probabilities should come from evidence-based medicine and/or estimated from clinical expertise and experience (I’ll not get into that debate, remember, Cat, Dog, Tree?) but the utilities/values should come from the patient. This is the Vulcan “cognitive system” mind-meld I mention in the following tweet (from a previous #HCLDR tweetchat).

Second: It’s complicated. Waaaaaay more complicated. Above is just about the simplest possible decision tree. I’ve seen decision tweets with many more layers (decision, probability, … decision, probability, utility) and way more branchy (instead of just two possible decisions, many more). The decision trees necessary to represent even slightly complicated clinical situations (say, two interacting co-morbidities) “blow up” and are basically almost impossible to explain to a patient.

Where do the numbers come from? The probabilities? Perhaps we’ll mine them from interoperable EHRs, eventually. The utilities? Turns out you can’t just ask a patient what their utility or dis-utility (negative utility) for a particular state of affairs. Well, you can, but when you plug the numbers and check them for consistency and validity, there are all sorts of problems. There have been many proposed solutions and work arounds for both problems. Meta-analysis from literature. Delphi techniques. Indirect ways to estimate patient attitudes toward risk. And more. I am favor of these and related important projects. My point is merely that formal approaches, such as I’m automatically inclined to support due to my training, are works in progress. And I look forward to hearing the reports of more progress I am sure are coming down the shared decision making pike.

(Hey, Jimmie, thank you for the following tweet!)

(”Option Grids are brief easy-to-read tools made to help patients and providers compare alternative treatment options.”)

I can recall when I first began to re-understand the role of formal models of decision making. My graduate advisor had a Ph.D. in Applied Mathematics from Johns Hopkins. I was struggling with some mathematical model of healthcare decision making. It’s hazy, but it might have been trying to figure out how to optimally pre-position air ambulances in the State of Illinois. I said something like, “I put in all the numbers and turned the crank but I disagree with what the decision model is telling me!” To which my advisor replied, that’s not how it works. If you disagree with the decision model, then go back and understand the problem better. Do this over-and-over, until you understand the problem. The purpose of the decision model is not to tell anyone what to do, but rather to help you better understand the real question you should be asking.

Anyway, back to the present. Perhaps in response to intrinsic limitations of formal decision models, or perhaps simply as part of getting to progressively better understanding of what the real problems and questions are, I see many informal but also more practical models being put forward. I’ve been researching those today, in preparation for the #HCLDR tweetchat, and tweeting some (see below). I am not qualified to offer a summary of the state of the art and science of shared decision making. I’m just qualified to tell you how I came to be interested in it, that it continues to fascinated me, and I look forward to tonight’s discussion of healthcare trade-offs, as well as future #HCLDR discussions.

I will end on a metaphorical note: marriage! Everything I’ve talking about so far reflects my economic and engineering training. So, naturally, I like mathematical models and computer simulations (which I’ve not discussed here, but are pretty interesting, to me that is.)

Compare the following tweet to my previous tweet. It’s really the same tweet! Goals, values, preferences of the patient combined with knowledge, competence, and experience of the clinician. The only thing different is the metaphorical means of combination, coordination, and harmonization. The marriage metaphor is such a rich sources of ideas. Think about the give-and-take, the trade-offs, and the evolution of married “cognitive systems.” I am sure there are many other potentially rich and useful metaphors out there.

The only real point I’m making is that the formal mathematical engineering models I favor (though am admittedly rusty) are just a small corner of a rich tapestry. I acknowledge this. I believe they have a role to play, in understanding and managing healthcare trade-offs. But also they have much to learn from other traditions and schools of thought. If they don’t “feel” right, we need to understand better.

P.S. The following are some recent papers I stumbled across while preparing to write this blog post. What others do you suggest?

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Periscope Helped Me Change My Hubcaps! Real-time Crowdsourced Healthcare Social Media Problem-Solving?

I’m fascinated by Twitter’s Periscope real-time video streaming to self-selecting viewers. If privacy, security, and real-time search challenges can be met, Periscope (and similar services like Meerkat) may become real-time crowdsourced social problem-solving models for healthcare. Before you jump all over me, because I’m suggesting something so ridiculous, check out the following thirteen-minute Periscope I archived to Youtube.

More or less as a joke, I simply tweeted “replacing my hubcaps” from Periscope on my smartphone…

…and 43 people showed to watch! So I kept the camera rolling, as my bro-in-law helped me put on new replacement hubcaps. (Below is annotated Periscope screenshot. Youtube video comes after.)

43-live-views3

The magic begins about 10 minutes in (where the following video starts). We are frustrated and about to give up, when Anton Kleban, from Manchester, UK (was 2AM there!), steps in. Anton starts giving suggestions. He pleads with us, “Listen!!”, “I’m in the motor trade!” By the end of the video my new hubcaps were installed and I’m offering to buy Anton a beer, if he’s ever in Washington, DC. (Increase your Youtube video resolution and size if you want to read the incoming commentary helping us install the hubcaps!)

You can hear me talking to myself at the end of the video (though who knows how many were still listening in!). What an interesting example of real-time crowdsource social-media enabled problem solving!

Could this work, or be adapted, in healthcare? Obviously, there are all kinds of privacy, security and real-time search issues. But just imagine, if those obstacles could be overcome…. A doctor could Periscope, “I’m looking at a skin lesion I’ve never seen before #dermatology” and a thousand physicians tune in. A patient could Periscope, “I’m about to get a diagnosis I’m afraid of…. #cancer” and a thousand patients tune in, take notes, and offer support. Of course, I know, privacy, security and real-time search… but… What if?!

P.S. I’d just like add a few links here. Over the last few years I’ve experimented with almost real-time video uploaded/streamed from camera’s clipped to ball caps (Hatcam!) and Google Glass (10 Top Uses For Google Glass In Health IT Marketing). The following are just some recent Periscopes, archived as Youtube videos. Plus associated tweets.

@wareFLO admits he’s clueless noobie on Periscope

My Wearable Workflow Presentation at 2015 BPM Summit #BPMCM15

(In the above video, the combo of slides and me works pretty well. Usually I have to choose one or the other to emphasize, in similar past videos.)

I #HITmc tweetchat & #periscope at same time!

World War 2 Memorial Periscope Archive

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Idea Sex About Care Management Systems, Distributed Task Management, And Workflow Technology

During the next three days, Monday through Wednesday, I’m attending (and speaking at) the Business Process Management and Case Management Summit (#BPMCM15 on Twitter) here in Washington, DC. I’ll be speaking about “wearable workflow” and the Internet of Things in healthcare, on Tuesday. But I’ll be thinking about the title of this blog post during the summit. There is perhaps no more concentrated venue of discussion of intelligent and flexible task management than the yearly BPM and Case Management Summit. During the next couple years, “Care Management Systems” will come to dominate the health IT vendor landscape. I have strong opinions about which classes of IT architectures will work best for flexible, scalable, distributed, intelligent healthcare task management (basically, they need to be “process-aware). Since the summit is such an excellent place to ask true experts on BPM and case management about how to leverage their technologies in healthcare, I decided to telegraphy my punch, so to speak, and archive the following Sunday morning, talking to myself tweets. Feel free to reply to any of them, from either the workflow tech industry or from health IT. I look forward to the conversation and the “idea sex.” :)

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