Medical Innovation & Healthcare IT Challenges: A Trip Report (Over 200 Viewers of Cybersecurity Hub Periscope!)

[I wrote this trip report while thinking about today’s #HITsm tweetchat, Top 10 Challenges for Healthcare Executives. In my opinion, the top challenge for healthcare executives is managing innovation. In fact, all five #HITsm topics easily pivot to innovation in healthcare. At the end of this post I’ve (only slightly) rewritten them to emphasize the importance of innovation.]

Imagine combining the 40 best annual HIMSS conference presentations and the 2000 most interesting attendees and speakers. Mix in lots of cool science and conversation about innovation. Then add same night opening games for 2016 NBA Champion Cavaliers (before which they received championship rings) and baseball’s World Series (Indians versus Cubs). Then add robust social media (#MIS2016 on Twitter, over 73 million impressions). You might, might begin to approach the vibe at last week’s 2016 Medical Innovation Summit in Cleveland.

When I was CMIO for an EHR vendor, every time I came back from a conference I’d email around a detailed “trip report”: with whom I spoke, industry trends, specific market intelligent, impressions of demos of competing products, that sort of thing. This trip report is more about local color and vibe. First are ten tweeted photos and a bit of commentary. But there are some “deep thoughts,” to which you are welcome to skip! (Compromise? Slowly scroll though the photos and videos while admiring them?)

In no particular order, here are my top ten highlights.

1. Touring the HIMSS Cybersecurity Hub with question-answering viewers on Periscope.

180+ viewers! (and still rising)

2. “Chuck, I hope this book inspires your love of workflow!” Martin Harris, MD, CIO, Cleveland, Clinic, author, IT’s About Patient Care, McGraw-Hill, 2017.

3. #GoTribe!

4. More than 73 million Twitter impressions on the conference hashtag #MIS2016

5. My annual selfie with John Sharpe

6. Facing fear using virtual reality in the operating room

(I wasn’t even wearing the VR googles and my forehead began sweating during VR simulation of a cardiac arrest!)

7. Experiencing at first hand the raw force of Jonathan Bush…

8. Meeting my hero, nurse maker (maker nurse?) Anna K Young. Here is a link to her TedMed video and Wired article.

9. “Innovation of systems & processes are as important as innovations in pure tech” Cleveland Clinic, Chief Clinical Transformation Officer, Dr Michael Modic

This was a common refrain, as in innovating workflow around medical devices is as important as medical device innovation.

10. Beautiful fall colors on the way to the Medical Innovation Summit from my home in Columbus, Ohio.

Courtesy of Google Glass (which, by the way, will be back, better than ever, however an NDA prevents me from divulging more…)

OK, a series of tweeted images hardly constitute systematic and incisive analysis of the 2016 Medical Innovation Summit. So I will close with these thoughts.

As I mentioned at the beginning of this post, in the old days my trip reports were detailed and blow-by-blow. Truth-be-told, I not sure how many of my colleagues actually read my entire lengthy emails. So I’ll close with more of rumination on innovation in medical technology and health IT.

The name of the conference was Medical Innovation Summit. A synonym of “innovative” is “creative”. I studied computational models of creativity during my graduate degree in artificial intelligence. Every student of creative starts with Wallas’s four stages of creative thought:

  1. preparation,
  2. incubation,
  3. illumination, and
  4. verification.

The Wallas model has been endlessly elaborated, into five, six and more stages. But I like original model the most. During preparation we immerse ourselves in a topic. We learn everything we can. We turn over every rock, figuratively. Eventually, we run out of rocks to turn over, and then we enter a frustrating phase during which we think nothing is happening. Every creative artist, novelist, and scientist has experienced this funk. However, incubation cannot be rushed. Under the surface, subconsciously connections are being made. Finally, often suddenly, a lightbulb goes on over our head. What actually turns it on can seem like a random environmental cue. This is illumination. But having a bright idea is insufficient. It has be vetted and turned into something useful and sustainable. An actual piece of art or fiction. A successful experiment and then, perhaps, eventually, a disruptive industry technology.

I thought of the Wallas model of creativity during the MIS2016 session, The State of Healthcare Innovation. Someone, perhaps from the audience via the MIS2016 mobile app, asked “Why can we get money out of ATM globally but not share med info?”

Panelists went down the line, addressing this question. Then Carla Smith (EVP, HIMSS) pointed out that the first ATM was installed at the beginning of the seventies. And that it has taken almost a half a century to get to the network of ATMs we take for granted today.

Let’s apply the Wallas model of creativity to an entire industry, AKA innovation in health IT.

I think some current frustration with the state of health IT (you know, with interoperability, usability, safety, patient engagement, and so on) is because we are collectively in the important but frustrating preparation/incubation phase. While progress may seem slow, under the surface, under our collective radar, so to speak, important connections and synapses are forming. At venues such as the Medical Innovation Summit and the HIMSS annual conference, and in between, in startups and hubs and pilots, we see illumination. Bright ideas click “ON” (like those figurative cartoon lightbulbs over our heads), but then must be vetted and designed and deployed.

Umm, I think that’s about as far as I will drive that particular analogy, between a four-stage model of human creativity and health IT innovation… But I would like to point the widely displayed logo slash symbol at the Medical Innovation Summit: a lightbulb!

See you today’s #HITsm tweetchat! (every Friday, noon, EST)

HITsm Topics (emphasizing innovation)

Topic 1: What do you think are the main issues and concerns facing healthcare organizations? #HITsm

#1 Innovation

Topic 2: What are some ways to identify and prioritize challenges and issues specific to (innovation in) your healthcare organization? #HITsm

Topic 3: How can you establish an environment that communicates the importance of change innovation and welcomes opinions and new ideas? #HITsm

Topic 4: What are some ways to inform & engage others – in your firm & broadly across industry – in large transformational (AKA innovative) initiatives? #HITsm

Topic 5: Why do you think healthcare innovation lags that of other industries? And what can be done to ameliorate that? #HITsm (this topic didn’t even need to be rewritten!)

@wareFLO On Periscope!


Actuarial Science, Accountable Care Organizations, and Workflow

Today’s Actuarial Challenge tweetchat is a welcome opportunity to ponder the future of actuarial science, health IT, and accountable care organizations, in a single blog post.

For background, see:

How will Accountable Care Organization (ACO) IT look in 10 years? How will Actuarial Science fit into that infrastructure? How can workflow technology get us there?

I am not an actuary, but I did get Accountancy and Industrial Engineering degrees (on the way to med school!). In doing so I studied fundamental Actuarial Science concepts: economic risk, random variables, time value of money, and modeling and optimizing stochastic processes (stock and trade for actuaries). Eventually I designed and deployed health IT software. But I’ve kept an interest in financial security systems. From Wikipedia:

“A financial security system finances unknown future obligations. Such a system involves an arrangement between a provider, who agrees to pay the future obligations, often in return for payments from a person or institution who wish to avoid undesirable economic consequences of uncertain future obligations.[1] Financial security systems include insurance products as well as retirement plans and warranties.[2]”

Here are my ten-year “What Will ACO IT” Look Like” predictions:

1. ACO enterprise SW will be ‘process-aware’ (workflow engines executing declarative process models). It will be essential for turning actuarial insight into automated ACO workflows, in almost real-time.

2. Stochastic simulation will literally be built into ACO enterprise software. Simulation is already built into many Business Process Management (BPM) workflow platforms. Actuarial simulation and workflow simulation will increasingly complement and even merge.

3. Virtual ACO enterprises will be built across workflow interoperable healthcare subsystem organizations. For extended discussion of task, workflow, and pragmatic interoperability, see my five-part series

4. ACOs will know exactly how much each service line (chronic Dx, procedure, etc) costs. Comprehensive ACO workflow IT platforms will seamlessly drive sophisticated event-driven activity-based cost management systems. Other industries know exactly what their smartphones and vehicles cost. Healthcare needs to do so too.

5. Virtual ACO enterprises will systematically optimize ROI on collections of targeted workflows. If each of predictions 1-4 become true (workflow tech infrastructure, embedded stochastic simulation, pragmatic workflow interoperability, and virtual ACOs), ACO will become truly intelligent learning healthcare systems.

Relative to intelligent learning systems, I should mention another of my degrees, an MS in Artificial Intelligence. Artificial intelligence, machine learning, workflow technology, business process management, and data pipeline management systems are increasingly leveraging each other’s strengths, and in some cases, even merging. While process-aware workflow technologies will increasingly form virtual ACO IT infrastructure, these workflows will be highly “tunable.” The additional data made possibly by workflow technology about what happened when will increasingly feed into stochastic models, which, in turn, will be essential for systematic improvement of workflows both driven by, and generating the data. This is the “intelligent learning” to which I am specifically referring.

I also have a whole bunch of questions! For example, what, exactly, does “stability” mean? (Probability that premiums will be sufficient to claims cash flows?) What is the current state-of-the-art for actuarial simulation? Are “state models” (as in, Markov models of disease progression) routinely used in ACO actuarial calculations? And so on.

For now, I’ll just close with some thoughts on the intersection among actuarial science, accountable care, and my favorite topics: healthcare workflow and workflow technology!

What is the connection between workflow, workflow technology, actuarial science, and accountable care?

I’ve taken entire courses in workflow. I’ve looked at hundreds of definition of workflow. The following is what I eventually “settled” upon.

“Workflow⁰ is a series¹ of steps², consuming resources³, achieving goals⁴.”

⁰ process
¹ thru graph connecting process states (not necessarily deterministic)
² steps/tasks/activities/experiences/events/etc
³ costs
⁴ benefits

Workflow technology is any technology that represents workflow as a model, explicitly (declaratively) or implicitly (neural network weights), and operates on the model/representation to automatically execute workflows or automatically support human execution of workflows. Academic workflow researchers call these “process-aware information systems.” The best know PAIS are BPM systems. However process-aware workflow tech is rapidly appearing in IT systems, such as Customer Relationship Management systems (CRM) and data and language “pipeline” platforms not typically referred to as BPM systems.

If one modifies my definition of workflow, though within my subscripted limits, to…

Process is a series of events, consuming expected resources, achieving expected benefits

…you arrive at a stochastic process closely resembling actuarial science’s generalized individual model (page 35 in Fundamental Concepts of Actuarial Science).

During my student days, we spent a lot of time estimating parameters and distributions, and then predicting behaviors of these stochastic processes. Sometimes we did so analytically with complicated equations (Markov Models). Sometimes we fell back on computer simulation (Monte Carlo).

A quick review of actuarial science literature indicate many of these same techniques are used today. I found questions about them on actuarial science exams and interesting papers by actuarial science researchers. I’ve appended links to some examples at the end of this post.

By the way, I believe my five predictions are incredibly relevant to a very topical topic: MACRA’s “virtual groups.” So I’ll close with this quote (stretches in italic due to me).

Virtual Groups

“…the MACRA Proposed Rule will likely put pressure on solo practices and small group practices, while favoring large groups.

Fortunately, the MACRA legislation offers a possible “salvation” for solo practitioners in that the rule allows for the formation of “virtual” groups. This would presumably enable smaller practices to band together (virtually) and to function as a larger group, spreading risk and potentially taking advantage of APMs and other benefits the MACRA legislation offers larger practice groups.

Unfortunately, CMS has decided that virtual groups, while mandated by law, were too complicated to set up. Consequently, it is proposing to delay the implementation of virtual groups until 2018.

What is especially ironic about this is that CMS states that virtual groups will be delayed due to the difficulty of establishing an efficient and effective “technical infrastructure” by the beginning of the 2017 performance period. Yet (of course) providers and software vendors are granted no such relief, even though they too will have “technical infrastructure” needs that will have to be enabled in their EHRs in that same restrictive timeframe.

The net result is that without the relief of virtual groups, the majority of small and solo practitioners may be even more unlikely to meet the MIPS standards during 2017, and are more unlikely to avoid penalties being assessed in 2019.”

What’s my point? Well, my predictions are ten years out, and therefore not likely to benefit small medical practices next year. However, I do think the concepts I invoke — virtual ACOs, pragmatic workflow interoperability, true costs, intelligent learning systems — are highly relevant in the long run. Therefore, even when fighting short-term fires, we need to keep our eye on long term goals, and paths to those goals.

Relevant to the #ActuarialChallenge, virtual intelligent learning ACOs will require actuarial science knowledge and experience to be successful. But, I also firmly believe, actuaries must leverage workflow technology to achieve the kind automatic, transparent, flexible, and systematically improvable workflow necessary to merge actuarial science secret sauce directly into ACO IT infrastructure.

@wareFLO On Periscope!


P.S. Here are some links and resources I consulted while writing this blog post.

Fundamental Concepts of Actuarial Science (fantastic introduction to AS if you hate math)

Introduction to Actuarial Science (edX course: videos, but also PDFs, ends with Monte Carlo simulation of life insurance)

Health Insurance: Basic Actuarial Models (Amazon, heavier going, but Fundamentals monograph and Intro edX course are good prereqs)

Society of Actuaries 2014 Exam MLC Models for Life Contingencies (includes questions about multiple state models)

Actuarial Calculations Using a Markov Model

Critical Review of Stochastic Simulation Literature and Applications for Health Actuaries

Stochastic Process (Wikipedia)

Constructing Probabilistic Process Models based on Hidden Markov Models for Resource Allocation (process mining to estimate state model transition probabilities)

Mathematical modelling of social phenomena

An actuarial multi-state modeling of long term care insurance

Estimation of disease-specific costs in a dataset of health insurance claims and its validation using simulation data

How Predictive Modeling Is Helping Employers Gain Control of Health Care Costs

Stochastic Modeling in Health Insurance

The ACO Conundrum: Safety-Net Hospitals in the Era of Accountable Care(Lean Six Sigma to ID and eliminate inefficient processes)

CMS ices reinsurers out of an ACO program

“The group practices were unable to track and manage the fluctuations in risk, and many went out of business. In recent years, organizations have tried to develop better cost tracking systems”

Multiple State Models

Actuarial Uses of Health Service Locators

Estimation of disease-specific costs in a dataset of health insurance claims and its validation using simulation data


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