Video and Written Q & A with First-Ranked #HIT100 @EMRAnswers Linda Stotsky

Short link (well, easy to remember, at least):

This week I got to sit down (virtually speaking) and talk shop with Linda of @EMRAnswers Twitter fame. She’s the number-one-ranked Twitterian on Michael Planchart’s #HIT100 list of heavy twitters. Since I know Linda mostly (actually solely!) through following each other and interacting on Twitter, and since many of our common followers are similar in this respect, I thought I’d interview Linda about her and Twitter!

Since there is so much good stuff, I’ve added the following mini-Table of Contents:

  1. YouTube One-Minute Interview
  2. Who is Linda of @EMRanswers?
  3. What do all your hashtags mean?
  4. How did you join Twitter?
  5. What’s your tweet workflow?
  6. Holy cow! What happened on November 16th?
  7. Who do you retweet?
  8. Who do you reply to?
  9. Who do you mention?
  10. Which hashtags to you use most frequently?
  11. What have been your most retweeted tweets?
  12. When do you tweet?
  13. Which platforms do you tweet from?
  14. What’s your favorite color?

But first! So you can get a sense of Linda’s effervescent personality, please watch on of my increasingly infamous One-Minute Interviews.

Here’s a screen capture of the Linda’s YouTube video.
(YouTube places its start arrow in the worst place!)

Below is the actual embeddable and runnable
YouTube interview (see what I mean!).

Before the Twitter-centric interview, let’s do give Linda a brief change to tell us about herself outside of Twitter.

Linda, who are you?

I know it’s hard to believe that I have a life outside of Twitter, but it revolves around my three kids and a large Chocolate Lab named “Coco”. Two of my children are grown, one followed me into HealthIT, and the youngest is a 16 year old rock star and budding scientist. My passions include music, the outdoors, contemporary art, pizza and anything chocolate. I’m a content writer, a really bad drummer, and I sing whenever my teenager allows it. I presently live in Nashville, Tennessee, although I am more than ready to move to the sunny beaches of South Florida. (hint-hint)

Thank you. Now, let’s talk shop!


What do all your hashtags mean?

On Twitter, if you want to join a conversation, you find the hashtag that best describes it. Naturally, my hashtags include the communities and the conversations I’m involved with on a daily basis. These hashtags allow me to operate in multiple registers at once, in a compressed Twitter-verse. I am fortunate to be part of the broad HealthIT community. I champion #patientengagement and interoperability. I walk as patient #117 in #thewalkinggallery- for patient safety and care coordination. #Innovation, and #technology hashtags merely reflect my personal interests.

I joined the #HITsm community in its infancy. Many of my friends, industry associates, and some of my most trusted advisors are “filed” under this hashtag. I am grateful and honored to be on the #HIT100 list. I was shocked to receive the #HIT1 award. My great predecessor handed it down “virtually” and I’ve enjoyed borrowing it. I join each one of the #HIT100 in the continued transformation of healthcare delivery. Hashtags are merely file tabs in a virtual world.

I see you’ve been around since July 18, 2009. How did that happen?

I joined Twitter after a downsizing. I was the co-designer of an online selection and educational platform for EMR, called EMR-Match at the time. I was the EMR usability expert, hence the name @EMRAnswers.


Let’s see: 32 tweets a day. You are more likely to include a link than not. Almost a fifth of your tweets are retweeted. I don’t have any benchmarks (do you?) but those seem very respectable.

Could you tell us about your workflow? From where do you get your ideas, links, tweets, etc? Do you “stage” tweets? (incoming, drafts, ready to go but not published, pending, published, etc.) I hear Google Alerts can be useful. Do you use them? If someone came to you for advice about how to balance topics, volume, engagement, etc. are there any principles or strategies you’d suggest?

I try to offer information, timely tidbits and newsworthy bytes of data. I balance topics with engagement in areas such as : Data security, Standards and Interoperability, HIPAA, EMR / EHR Usability, Mobile Health, HIE, and the Patient Experience. I learn a lot from my Twitter “community” every day. I begin early- about 5:30 AM, usually by searching online news publications. I balance news and topics with RT’s from respected friends because it’s important to show mutual respect. I tweet at lunch and in the evenings, though usually never after 9PM (Your analytics say otherwise). I seldom use Google Alerts, though I’ve set them up.

The principles I follow:

  • Provide quality content
  • Don’t get involved in politics or religion
  • Be a force for good
  • Listen to others
  • Share information
  • Cite your sources
  • Pass it forward


Wow! Look at that spike on November 16th! I looked it up. 129 tweets in one day. What happened?

HA!! I must’ve had a lot of caffeine that day!

I’m honored to be one your ten most retweeted over the last three months! Some of these other guys are familiar, others less so. Care to provide a run down or highlight several for our readers?

Of course everyone knows you, Charles. You are a very stable, well-respected source on EHR software solutions. I enjoy reading your content and re-posting content you’ve provided.

[Stable? Well-respected? OK then!]

Here’s the run down:

  • Keith Boone. @motorcycle_guy, is my personal standards authority. I deeply respect his perspective on interoperability.
  • Vala Afshar, @ValaAfshar– I admire his leadership lessons and valuable insight.
  • David Scher MD, @dlschermd is a former cardiac electrophysiologist, and has become a great friend. He is the Senior Medical Advisor @Happtique.
  • I love startups, hence @startuptechguy.
  • @NAMIMass is for mental health advocacy.
  • @hcsmmarketing is my alter-ego.
  • Lisa @PracticalWisdom is a great person and a good friend, always optimistic.
  • I always look for Tom Sullivan’s news and information @GovHIT.
  • @TEDMED is on the edge of innovation and creativity. It’s my dream gig.


It’s interesting that there is no overlap between folks you retweet and folks you reply to. Why is that? You know one set better? Different styles interact differently? Or another explanation?

Almost all of these people are involved in the same hashtags- most of them in #HITsm. I know these people well. They share awesome content. I support them, ask them questions, champion their causes, and my replies usually include a reference to their original tweet. I actually do RT them, not sure why its showing up this way except many times I “quote” them in a RT and may throw in a MT to the mix…


Hmm. Mentions straddles both Retweeted and Replied-to. I find the ebbs and flows of Twitter conversations fascinating. I’m always wondering who’s listening it. Sometimes I assume other folks on Twitter are like me and act like me and therefore interact in the same situations and for the same reasons as me. But I also know I’m likely misleading myself. In a previous life I was a graduate student in linguistics. If I went and looked at the raw data, the ‘corpus’ as they say, what would I see? I guess I’m just trying to prompt you for some sort of theory of Twitter psychology you may have. Have you?

I do agree that “like” attracts “like”. We attract and respond to people, situations, and events which conform to our expectations. I sincerely like what these users have to say. I respond to them on a frequent basis, some daily. But if you are trying to link it purely to the interaction of raw data, you would find that some is pure overlap between social media accounts. Sites with Twitter share buttons, like I “link” my scoops, because it’s quick, easy and reduces curation time. And industry publications like Business Wire, “auto-insert” their Twitter handle at the end of each article. It’s their copyright, so I respect it. I mention the people I follow because I find their content timely and relative to what’s going on in HealthIT, or HITsm..


Your hashtags make sense. Except, where’s #EHR? Actually, seriously, I find having to insert both #EHR and #EMR the same tweet tedious. Have you taken a principled stand here?

Hey, Charles, thanks for making me paranoid about not using the hashtag #EHR. 🙂 I will now be obsessed with making sure I treat it fairly. I agree that it is extremely tedious to insert both #EMR and #EHR. I am more of an ambulatory software gal. So I tend to go with EMR on most occasions. While I was working for a critical access hospital vendor, I used more EHR references. I’m not taking a principled stand; it’s purely a matter of choice.


The interesting thing is that I remember several of these tweets (particularly the October 28th)! In fact, I’ve read that since our brains are wired for social processing that we’re remarkably good at remembering tweets! Consistent with this, several times I’ve tried to find a tweet many thousands ago, but been frustrated (understand one can now download all the tweets from the beginning of a Twitter account). Can you still recall a now ancient tweet or exchange or two? Care to share?

I’ve tried to download my Tweets from a thousand years ago, but have not been successful with getting back to the beginning. My favorite exchanges are always those revolving around the continuum of patient care, healthcare interoperability, and patient engagement. It’s continually evolving. We have come so far, yet we have a ways to go in terms of a true continuum of care involving outliers like prison health and mental health. Being part of a movement that will change the face of healthcare in a few short years is extremely exciting. Patient engagement IS the new ROI. It’s fascinating to me how much we can contribute to the success and future sustainability of the healthcare eco-system by becoming involved in establishing standards. This is why I come back every day. I want to be part of the conversation, the community, AND the solution.

[CW: Great line! “Patient engagement IS the new ROI!”]


Ok, now let’s take a look at your schedule. Friday’s a big day, eh? Is that because that’s when you’re online? Or because that’s when you schedule tweets to post? How do feel about the sentiment, in some quarters, that scheduled tweets are, in some sense, cheating? I schedule tweets so that when I am out and about, and not able to easily pull the trigger myself, they’ll still appear when I think my followers expect them. As long as I know what’s going to post when and am ready to react to retweets and replies via smartphone (and to postpone a tweet, if necessary), that’s all to the good. Any opinion or guidance to share on this “issue”?

Who knew these times were so busy for me. Fridays are big for two reasons. #FollowFriday and the #HITsm chat at 11:00 AM CST. I create my #FF lists on Thursday night or early Friday morning. The #HITsm chat stream appears in my timeline, so that beefs up my Friday posts. I do schedule tweets each day, but other than that, do not schedule my normal tweets. I am “old school” Twitter. I find scheduled tweets are convenient, but I don’t schedule. I react to RT’s at night or first thing in the morning. I reply immediately whenever I can. I don’t think there is any “format” that works better than another.


Finally, platform! The shear variety of different ways you post tweets suggests a sophisticated, or at least diverse, set of workflows (already discussed). Which methods do you use to post which kinds of tweets, and why? Heard of any new and interesting applications or best practices in this area that sound promising?

I use quite a few applications, as you’ve pointed out. The “Tweet” button is great. I find it’s now available almost everywhere. (Thank goodness). Twitter’s new “cards technology” seems promising if expanded. It allows third-party developers to create richer, visually consistent content. The expansion of the ‘more’ button is something to look forward to. I’m anxious to see the new and expanded ways Twitter will “compete” with Facebook and Google. I hate to say this but I am just starting to use my iPad, vs. my iPhone. WOW. Progress.

Phew! That’s what I call really getting into the weeds! (tweeds?) I know I learned a lot, including some ideas and techniques I’m going to put to use immediately. You’ve done a great service to your Twitter followers, drawing back the curtain, sharing some of your secret sauce, so to speak (mixing several metaphors that, perhaps, should have remained unmixed).

Of course, we are — all of us, you, me, your and my followers, and tweeps and Twitterians we follow — much more than just our twitter accounts! We’re complicated, multi-dimensional people, outside our Twitter accounts!

So: What’s your favorite color?

Ahh…always a complicated question. Red is great. Black is back, and I’m starting to favor green— but isn’t that the color / lifestyle we should all be adopting?

Thank you again, Linda. I look forward to continue to follow you, and interact with you, on Twitter.

Thanks, Charles. Always a pleasure!

By the way (now speaking to you, the reader), I’ve enjoyed all my One-Minute Interviews and in-the-weeds co-writing assignments. But Linda was by far the most charming and emotive victim interviewee. (Doubly-embedded “by-the-way”: all of you interviewees to date were *all* charming and emotive! But Linda was more!)

Process Support and Knowledge Representation in Health Care: New Book!

Short Link: (from Process Support and Knowledge Representation)

As you can tell from this blog’s title (EHR Workflow Management Systems) and my Twitter account (@EHRworkflow), I think health IT and workflow technology is a great match. Many EHR (and related HIT) weaknesses are mirrored by business process management (BPM and related tech) strengths (for example, see Figure 3).

Therefore I was excited to see this tweet from @manfredreichert:

The following (my emphasis) describes the contents of this proceedings of a workshop attended by both medical informatics and business process management researchers.

Healthcare organizations are facing the challenge of delivering high quality services to their patients at affordable costs. These challenges become more prominent with the growth in the aging population with chronic diseases and the rise of healthcare costs. High degree of specialization of medical disciplines, huge amounts of medical knowledge and patient data to be consulted in order to provide evidence-based recommendations, and the need for personalized healthcare are prevalent trends in this information-intensive domain. The emerging situation necessitates computer-based support of healthcare process & knowledge management as well as clinical decision-making.

The ProHealth’12 / KR4HC’12 workshop brought together researchers from two communities who have been addressing these challenges from two different perspectives. The knowledge-representation for healthcare community, which is part of the larger medical informatics community, has been focusing on knowledge representation and reasoning to support knowledge management and clinical decision-making. In turn, the process-oriented information systems in healthcare community, which is part of the larger business process management (BPM) community, has been studying ways to adopt BPM technology in order to provide effective solutions for healthcare process management. Adopting BPM technology in the healthcare sector is starting to address some of the unique characteristics of healthcare processes, including their high degree of flexibility, the integration with EMRs and shared semantics of healthcare domain concepts, and the need for tight cooperation and communication among medical care teams.

Using the published Table of Contents I tracked down preprints for most of the papers. Keep in mind they were likely edited and polished after the workshop. Nonetheless, they show important connections between healthcare information and healthcare processes. And they show the motivation of respective academic communities to understand and exploit these connections.

I hope these papers, and their authors, inspire US EHR, health IT and business process management professionals, organizations, and vendors to benefit from similar concerted effort.

Table of Contents Plus Links to Papers

Where I know of a Twitter account for an author (or relevant associated vendor or organization) I added it.

Let me know if you track down any of the missing papers or know the Twitter accounts of additional authors.

Fixing Our Health IT Mess: Are Business Models or Technology Models to Blame?

Short link:

You may not agree with me that Health IT is a mess. Check out my sentiment analysis of Twitter’s reaction to the New York Times coverage of the recent RAND report. You’ll at least agree that many people do agree with me.

That said, reasons offered for the mess are all over the map. You can read my summary. I’ll focus on one defense of Health IT: It’s the business model, not the technology. Sometimes it’s put differently, as in: It’s the incentive model, not the technology. But business models are all about incentives: to create a business, to sustain a business, to do business with a business.

So, to those who say it’s the business model, not the technology, I say the opposite: It is, in fact, the technology model, not the business model.


I’m a fan of business and models. I understand the importance of financial incentives to mold behavior. I have degrees in accountancy and industrial engineering, the spiritual homes of cost, revenue, and profit engineering and performance-based incentive systems.

But, no matter how much you persuade, pay, or punish frozen workflows, they won’t change. You have to unfreeze the workflows, change them, and then refreeze them. Most current EHR and health IT systems have relatively frozen workflows. They don’t have the necessary innards: workflow engines, process definitions, graphical editors, or similar means to achieve similar ends. Process-aware systems include workflow management systems, business process management and adaptive case management. Executable and malleable workflow is what these systems do. It’s the opposite state of affairs in the EHR and health IT world.

The problems of Meaningful Use are entirely predictable through the lens of the infamous Iron Triangle anti-pattern of software development. Attempting to bring too many features to market too soon usually results in unstable, less usable, and hard to maintain software.

Wait, you say. Why can’t we add resources? You can. Up to a point. At the beginning of a software project, adding the right programmer or two can be helpful. The problem is, as the number of personnel grows, you run into Fred Brooks’ most enduring law: “Adding manpower to a late software project makes it later.”

There is no way out of the Iron Triangle. You can only make it bigger. It should be renamed the Carbon Nanotube Triangle (strongest, lightest material known). You can change the triangle’s shape by shifting emphases among features, schedule, and resources. And you can change its size through technological innovation. So far we’ve been trying to do the former, mostly via stakeholders asking, begging, demanding that we slow down. Some innovators nibble at the problem, creating workarounds and crafting end-runs: EHR-lite, EHR-extenders, mEHR etc.

The only way to increase the size of the Iron Triangle (to deliver more and better features sooner) is to change what economists call the “factors of production”. In this case the factors are the software technologies we use to attempt to meet the requirements of Meaningful Use.

Most EHRs are based on structured documents represented in relational databases. What do users of Meaningful Use certified EHRs complain about? Workflow! It’s the wrong workflow. It’s laborious workflow. The workflow doesn’t fit their specialty or special needs. The workflow can’t be changed. The workflow slows them down.

Well? If the problem is workflow and we aren’t using workflow technology, maybe we should use workflow technology? This seems so obvious that one must ask: Why hasn’t it already happened? I cover that in Top Ten Reasons EHR-BPM Tech Is Not (Yet) Widely Deployed in Healthcare.

To expand the Iron Triangle we need to move from structured document management systems to structured workflow management systems. Workflow management systems have been widely used in other industries since the mid-nineties. With improvements and complementary technology (business activity monitoring, process mining, simulation, graphical editors, adaptive and adaptable workflows) workflow management became business process management and adaptive case management.

I do agree that even malleable systems won’t change and improve unless they are caused to do so by outside forces. Business and incentive models play a role here. But frozen systems won’t change even in the face of those outside forces. Our current workflows can’t change because they aren’t modeled, reasoned about, executed, tracked and improved. EHR workflows are frozen. We need to unfreeze these workflows to, if not escape from Iron Triangle, at least expand it to accommodate our goals and needs.

To those who say it’s the business model, not the technology, I say the opposite: It’s the technology model, not the business model.

It’s worth quoting from a recent interview (Could Dutch Computer Scientist Wil van der Aalst Save U.S. Healthcare 600 Billion Dollars?) with a business process management researcher.


Are there process-aware aspects of the world we take for granted in our daily lives? (products we buy made possible by process-aware factory automation, smart consumer-facing web services, stuff that happens we don’t think about until there is a glitch). Headlines are full of “mobile”, “cloud”, “social”, and “bigdata”. Does “process-awareness” not get the awareness it deserves?


“Processes are of course everywhere. When you rent a car, book a flight, buy a book, file a tax declaration, or transfer money there are process-aware information systems making this possible. Processes are as essential as data, but less tangible. Moreover, process support is much more difficult than managing data. Indeed it should get much more attention. The problem is of course that processes have always been there, while “big data”, “mobile computing” and “clouds” are perceived as something new.”

It’s a great interview. The rest of it is healthcare specific.

Process-aware information systems are widely prevalent outside healthcare. Ironically, the very cloud, mobile, social, and data technologies that health IT looks toward importing into healthcare rely on process-aware technologies. For example, when innovators look to cloud and mobile for EHR alternatives, workarounds, and wrappers, they also get the process-aware technology that makes cloud and mobile workable. Secure, flexible, scalable, context- and process-aware cloud-based backends will be key to secure, flexible, scalable, context- and process-aware front-end mobile apps used by patients and healthcare providers. There’s more about this in my Attending AWS re: Invent, Amazon Web Services’ First Global Customer and Partner Conference: What’s The Healthcare Angle? BPM vendors are further along than HIT vendors in use of cloud, mobile, and social technology. So: cloud, mobile, and social will be important “vectors” for transport of BPM’s process-aware ideas and technologies into healthcare.

Social and data tech will play supporting roles too, but that is another entire blog post. Maybe two! And language tech and workflow tech also have fascinating connections to each other.

What’s to be done?


  1. Business process management (BPM) and adaptive case management (ACM) vendors are eager to partner with healthcare organizations and vendors. Many already do substantial healthcare business, though usually not at the point-of-care (yet).
  2. EHR users are restive, increasingly critical of the workflow-challenged systems they feel forced or bribed to use. Their professional organizations ask whether too much has been attempted too soon and with inadequate technology.
  3. Some EHR and HIT vendors have more customizable workflows than others. They may not think of themselves as EHR workflow management systems or even EHR business process management systems, but in effect they are becoming so.


  1. Educate EHR users and HIT buyers so they can recognize systems with the more customizable workflows.
  2. Find and market the EHR and HIT vendors with the right stuff: workflow engines, process definitions, graphical editors, plus other valuable BPM-like and -compatible products and services.
  3. Leverage existing business process management and adaptive case management vendor products and services.

Yes, this threatens status quo. Good. Cloud, mobile, social, and data already do. Why not add process-aware technology to the mix?

I said it before: twice. But I’ll say it again:

It’s the technology model, not the business model.

P.S. I’ll be at the upcoming HIMSS13 conference in New Orleans. I’d love to network with like-minded health IT folks about bringing more process-aware information systems to healthcare. You can contact me through this blog or tweet me at @EHRworkflow.

P.S.S. If you are interested in further related reading about the benefits of workflow technology in healthcare….

Siemens Healthcare Uses TIBCO Business Process Management and Complex-Event Processing to Save Lives

Short Link:

Siemens Healthcare has been a pioneer in bringing business process management technology to healthcare, starting with the Soarian hospital EHR workflow management system over a decade ago. I’ve written about Soarian and Siemens here in my EHR Workflow Management Systems blog. I included material about Siemens Soarian in my three-hour tutorial on EHR Workflow Management Systems at the old TEPR conference. And I occasionally tweet about them from my @EHRworkflow account.

More and more BPM vendors are getting into the deeper end of the healthcare swimming pool, closer and closer to clinical patient care. It’s a big project to educate healthcare and health IT about process-aware information systems such as Soarian. It’s a paradigm shift (#3 in my Ten Reasons EHR-BPM Tech Is Not (Yet) Widely Deployed in Healthcare) from mere structured documents to robust structured workflows. So I was delighted to see this crisp, clear and concise description of the benefits of business process management and complex-event processing in a healthcare and hospital context.

Some people like to skim text and others like to watch video. For the readers like me, I transcribed the interview (30 seconds versus two minutes?). It’s also a way to make sure the search engines find this important material. After the interview I added some links to related blog posts about topics mentioned in the interview.

“My name is Tommy Richardson [@Techdadda on Twitter]. I’m the Chief Technology Officer and VP for Technology for Siemens Healthcare

To us what is most exciting about TIBCO is that most companies out there today are hardcoding when building their systems. They’ve got hardcoded rules, hardcoded workflows, hardcoded integrations.

What’s exciting to us about TIBCO is using TIBCO’s BPM and enterprise service bus to build much more flexible and systems that can be much more easily extended for our clients. So you can think about some hospitals have certain workflows when you go to emergency. Others may want to change that and say when [the patient] first comes in skip the insurance collection and send him back to the room if he’s seriously hurt.

So we’re using the tools TIBCO has given us to build this tremendous flexibility into the system, where we can have different workflows for the hospital organizations and the hospitals organizations can even change and update the workflows we deliver with TIBCO’s products today.

TIBCO’s technologies are extremely important to us.

The data that’s changing in our systems is flowing over the TIBCO enterprise service bus. Using the complex event processing, the workflow, and the rules allow us to get a two-second advantage.

So we can look at the data that is changing immediately, whether it’s from taking someone’s blood pressure and knowing there’s a problem to monitoring their cardiac test to discharge to admission. All the different events that happen in healthcare you can use the TIBCO tools to, in a second or two, make real life-determining decisions.

That’s the really key thing about healthcare that’s certainly different from other industries. Other industries are using the tools to make dollars. We’re using it to save peoples’ lives.

That brings chills up my spine just talking about it.

That’s the special stuff.”

Special indeed!

Great interview! Reminds me of my infamous HatCam One-Minute Interviews (well, length- and content-wise, though not production quality).

If you’d like to learn more about EHR workflow technology and EHR complex event processing, I hope you’ll check out some of the following links:

Could Dutch Computer Scientist Wil van der Aalst Save U.S. Healthcare 600 Billion Dollars?

Short Link:

Prof. Wil van der Aalst is not a health economist studying cost, a surgeon promoting safety, or a pediatrician investigating quality. In fact, Prof. van der Aalst is not a healthcare researcher. He is a Dutch mathematician and computer scientist. His ideas and invented techniques are generally valuable to any industry that needs to better understand, manage and improve its processes. That said, about a fifth of his many academic papers deal with healthcare processes and workflow. His ideas about workflow models, patterns, and analytics have been tested generally, across many industries, and specifically in healthcare.

Could Prof. van der Aalst save U.S. Healthcare 600 Billion Dollars? Watch his One-Minute Interview. Read his answers to my questions. Decide for yourself. (Several of my questions have lengthy preambles. I do that sometimes. Feel free to skim directly to his indented responses.)


Prof. Wil van der Aalst’s “One-Minute Interview”
(without the “arrowhead”)

The above image? It’s so you can see Prof. van der Aalst without YouTube’s irritating “arrowhead.” Below is the actual embedded and runnable One-Minute Interview on YouTube .

Prof. Wil van der Aalst’s “One-Minute Interview”
(with “arrowhead”)

Here’s a mini-table of contents for Prof. van der Aalst’s interview:

1. What is your name? Where do you work? What is your role?

“I’m Wil van der Aalst and work as a full professor of Information Systems at Eindhoven University of Technology. I also have professorship appointments at Queensland University of Technology in Brisbane (where I’m now) and the National Research University Higher School of Economics (HSE) in Moscow. Besides running a research group in Eindhoven, I’m also chairing various committees, e.g., the steering committee of the Business Process Management Conferences and the IEEE Task Force on Process Mining.”

2. What are process-aware information systems (PAISs)? How do workflow management systems, business process management suites, and recent debate about case management fit into your description?

“Any information system that is supporting processes beyond the limits of individual tasks is a Process-Aware Information Systems (PAIS). Process support in its purest form can be found in Business Process Management (BPM) and Workflow Management (WFM) systems. These systems are driven by explicit process models. Changing the model directly results in changes of the process that is supported: no coding is needed. However, there are also many process-aware information systems where process models are less visible. Consider for example systems where processes are hard-coded or the ERP (Enterprise Resource Planning) systems that can be found in most of the larger organizations. Many people do not realize that larger ERP (Enterprise Resource Planning) systems (e.g., SAP and Oracle), CRM (Customer Relationship Management) systems, case-handling systems, rule-based systems, call center software, and high-end middleware (e.g. WebSphere) are process-aware, although they do not necessarily control processes through some generic workflow engine.

The debate on case management is a bit silly. Note that we were already working on the foundations of case handling long before the term got popular. Also note systems such as FLOWer and ECHO that have been around for a long time. The key problem is to truly support flexibility and to move beyond simplistic flowchart modeling notations such as BPMN and the like.”

3. On my blog I’ve a list of ten reasons BPM has not gained more advocates and use in the US healthcare industry.

Top Ten Reasons EHR-BPM Tech Is Not (Yet) Widely Deployed in Healthcare

Do they seem reasonable to you? Any other reasons? Any one or two that really seem to be key to you? How?

“The list seems to be very reasonable. The 10 items explain well why there is resistance against adopting BPM technologies. Items 4 (Lack of competition) and 7 (Self-interest) seem very important. I also think that there are additional reasons for the slow adoption:

Governments have been too passive in forcing care organizations to work in a more structured and standardized manner. A nice example is the so-called Diagnosis Treatment Combination (DBC in Dutch) introduced in 2005 in the Netherlands. Every hospital is required to use DTC’s in order to determine the total cost of a medical treatment. Hospitals are required to report information in standard form to get reimbursed. The data is collected at the national level and has helped us a lot in our process mining research. It shows that a government decision can result in rapid changes of the underlying processes and IT systems as long as they have the guts to enforce it.

Another factor is the ignorance of end-user organizations of BPM technology (in this case hospitals). They do not know what is possible and therefore do not ask the right questions. As a result, technology providers get lazy and focus on superficial things like user interfaces (see also point 6 on your list). Health care managers and IT specialists need to be educated when it comes to business process management and process mining.

In Europe we are facing another complication. Many of the healthcare related regulations are country specific. As a result, there is no real competition and innovative software products developed in one country cannot simply be used in another country. Here the European Union should be more active rather than spending their energy on talking about Greece’s financial problems.”

4. You keynoted the 2004 MedInfo conference in San Francisco. You said you’d looked at every instance of the use of the word “workflow” in the proceedings but, it did not seem to be used in the same way that you and your colleagues use the term. What did you mean? Has there been any convergence in meaning? I don’t suppose you’ve looked at any MedInfo proceedings in the same manner recently, but you may have had other indications.

“There has been a long workflow management tradition in the business area. Already in the 70-ties there were workflow systems in place. Unfortunately, the same ideas tend to be reinvented in different domains. A nice example are the so-called medical guidelines languages (Asbru, GLIF, GUIDE, and PROforma) reinventing basic workflow patterns.

The terms Business Process Management (BPM) and Workflow Management (WFM) also have the problem that people do cherry picking: they focus a particular aspect and abstract from all other aspects. For example, people focus on execution engines and ignore management aspects or people only draw PowerPoint diagrams while closing their eyes for the actual Spaghetti-like processes and complex information systems.

The drawback of people misusing terminology is that some technologies get a bad reputation because of unrealistic promises and organizations do not even try to use them anymore.”

5. Your bio notes you’ve published “more than 150 journal papers, 17 books (as author or editor), 290 refereed conference/workshop publications, and 50 book chapters”. You seem interested in any innovative use of BPM or process mining, no matter the industry. However, many of your papers happen to be about healthcare processes. Approximately how many papers related to healthcare processes have you published? Is healthcare a special case?

“The techniques and tools we develop tend to be very generic. We are not interested in tailoring them towards a particular application domain. For example, we have applied our process mining tool ProM in over 100 organizations covering very different industries. I guess that about 20 of my papers are focusing on healthcare applications of our technologies. We have a particular interest in healthcare because processes are much more chaotic than in many other industries, and potential savings are enormous. For example, we did quite some research into workflow flexibility. It is interesting to see that many researchers working on this topic are inspired by applications in healthcare. This illustrates that healthcare is a very challenging, and therefore interesting, application domain for BPM.”

6. What is process mining? How is it relevant to healthcare?

“Process mining can be used to discover and analyze emerging processes that are supported by systems that are not even “aware” of the processes they are used in. It is definitely one of the “hot topics” in BPM research and I love to work on it because it is driven by real data rather than simplistic diagrams.

Imagine this: in 2060 your laptop can store the whole digital universe as we know it today. All books, movies, music, articles, the whole internet, etc. known today will fit onto your hard disk in 2060. This can be shown by simply extrapolating Moore’s law. This means that more and more data will be available and we should use it to analyze processes. The “Big Data” wave will also impact the healthcare industry. Unfortunately, most people working with data are not very interested in processes because they lack the proper tools and focus on specific activities rather than end-to-end processes.

See the websites and to learn more about applications of process mining in healthcare.”

[CW: Also my EHR Business Process Management: From Process Mining to Process Improvement to Process Usability]

7. Many professionals in health IT have had some personal health experience, or observed that of a relative or friend, motivating them to use their knowledge to improve healthcare information management. Do, or can, you relate to this?

“This also holds for me. We have four children and our oldest son Willem has Down’s syndrome. In his first year he had heart surgery because of a serious Atrioventricular septal defect (AVSD). Over the last 11 years he has seen many hospitals from in the inside. Overall, I’m impressed by the work done in hospitals. However, I also see that with our aging population and advances in medicine, it is vital to do things more efficiently. Therefore, I’m eager to contribute. In banks and insurance companies our techniques can be used to make things even more efficient and effective. However, improvements in managing healthcare processes are much more urgent.”

8. Are there process-aware aspects of the world we take for granted in our daily lives? (products we buy made possible by process-aware factory automation, smart consumer-facing web services, stuff that happens we don’t think about until there is a glitch). Headlines are full of “mobile”, “cloud”, “social”, and “bigdata”. Does “process-awareness” not get the awareness it deserves?

“Processes are of course everywhere. When you rent a car, book a flight, buy a book, file a tax declaration, or transfer money there are process-aware information systems making this possible. Processes are as essential as data, but less tangible. Moreover, process support is much more difficult than managing data. Indeed it should get much more attention. The problem is of course that processes have always been there, while “big data”, “mobile computing” and “clouds” are perceived as something new.”

[CW: The next question? One of those lengthy preambles I warned you about.]

9. To put the following numbers in perspective, World Gross Domestic Product is about $81 trillion and US and European economies are about $15 trillion each. Over the next ten years the US health industry sector is projected reach $4 trillion. It spends twice as much per capita as other similar industrialized countries. The growth rate of US spending on healthcare is also considerably higher than other similar countries.

Health Care Spending in the United States and Selected OECD Countries

Why does U.S. health care cost so much?

The US is estimated to waste more than $765 billion/year on healthcare spending, one third of the total $2.5 trillion dollar healthcare industry, due to:

  • Unnecessary services
  • Frequency
  • Defensive medicine
  • Unnecessary use of high-cost services
  • Administrative waste
  • Duplicative costs of administering different plans
  • Unproductive documentation
  • Inefficiently delivered services
  • Medical errors
  • Uncoordinated care
  • Inefficient operations
  • Too-high prices
  • Prices higher than competitive levels
  • Excessive variation in service prices
  • Fraud
  • Medicare/Medicaid fraud
  • Insufficient investment to detect fraud
  • Missed prevention opportunities
  • Poor delivery of clinical prevention services

I hate to put you on the spot (actually, I relish the opportunity to do so, in this case), but, approximately how big is the opportunity for workflow management systems technology, business process management suites, and healthcare process mining in US healthcare?

  1. 0 – 7 Billion
  2. 7 – 70 Billion
  3. 70 – 700 Billion
  4. Greater than 700 Billion

Justify your answer!

“As far as I can recall 8000 dollars are spent per person per year in the US (17% of the GDP). Assume that we could save 2000 dollars through process improvement and better IT support. This would amount to 300 million x 2000 = 600 billion. Of course this is just a guess. However, both the absolute numbers and the relative increase in spending due to our aging society show that there is a need for action! Therefore, I appreciate your efforts to bring these issues to the attention of medical professionals and decision makers.”

I take it your answer is #3!

Thank you Prof. van der Aalst. It will be fascinating to watch workflow management systems, business process management, and process mining technology diffuse into and throughout US health IT and healthcare.

Cloud Computing and Big Data: Looking for Healthcare Workflow

Short Link:

Last week I attended the Cloud Computing and Big Data Forum & Workshop at the National Institute for Standards and Technology (NIST) in Gaithersburg, Maryland. While conference was not about healthcare or workflow per se, cloud computing and big data are obviously relevant to healthcare. I tweeted generally about cloud computing and big data while noting material of specific relevance to healthcare and workflow. I collected some of the tweets and wrapped them in further comments to write this blog post.

By the way, you may also be interested in my complementary blog post: 2012 Amazon Web Services (Health) User Conference Trip Report. Specifically relevant subsections include…

…back to the NIST Cloud Computing and Big Data Forum & Workshop!

I (like the workshop) will start with cloud, move to big data, and then big data in the cloud.

Cloud Computing

NIST is in the business of coming up with helpful definitions. For example, prior to the conference I wondered out loud…

NIST defines cloud computing to have five essential characteristics. They’re worth reviewing, so as to build upon when we cover federated cloud computing in the next few tweets.

  1. On-demand self-service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.
  2. Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).
  3. Resource pooling. The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth.
  4. Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.
  5. Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.”

Federate Community Cloud definition:

A cloud in which resources are provisioned for use by a community or by multiple communities of consumers from multiple organizations using methods that address shared needs or concerns.

Building on the idea of federated community clouds are virtual organizations. I’ve heard and read the phrase “virtual organization” for decades. What appears different here is that NIST is using it in a more formally defined manner.

“[Virtual organizations] are defined as multi-organization constructs that use federation to share access to computing resources.”

… and include one or more of the following capabilities:

  • privacy and security
  • compliance adherence
  • trust infrastructure
  • membership
  • internal organization roles
  • common governance
  • policies and procedures
  • private communication

So, a cloud is made up of measured network access to on-demand self-service with resource pooling and rapid elasticity. A federated cloud serves multiple communities coming together as virtual organization. If these definitions stick, then we’ll be speaking of federated clouds to create virtual healthcare organizations.

Two-fifths of listed federated community cloud scenarios listed on this slide involve healthcare:

  • Catastrophic dynamic event response
  • Specialized remote medical care

These particular scenarios make complete sense, of course, given the definition of cloud as being remote, on-demand, and scalable. But I am certain we’ll see more mundane examples too, as everyday healthcare organizations (hospitals, medical offices, laboratories, etc.) move to the cloud.

Workflow sighting! (Fifth bullet down)

Chris Davis, from Verizon, spoke in the Progress on Standards for Interoperability between Clouds session about healthcare. I wasn’t close enough to get good pictures of slides. But here’s a tweet to give the flavor. I sure my health IT readers would have found what he said familiar, about healthcare’s unique needs, etc.

The special treat of the NIST workshop was to be sitting right up front while Vint Cert (of Inventing-The-Internet fame) gave the keynote.

There was an interesting question from the audience about Google Health, which was discontinued last year.

VC said their basic problem was getting data from the physicians, by which I assume he means from their EHRs and patients.

VC went on to say that we need to make physicians lives easier with tech, not harder. Since surely no one would, or could, disagree with this sentiment, it’s a remarkable statement to have to make!

But then it got even better than that! Vint Cerf came right down into the audience, Oprah-style, and took questions. He stood a couple feet from me. I caught on smartphone video this funny, interesting, and informative story about Google’s self-driving cars. Aside from the fun and interesting part, he had a point. We’re entering the age of machines talking to machines. Not only will cars negotiate with each other at stop signed four way intersections, but they will have access to everything each other has ever seen at that intersection. “That” never moves. It’s a tree. “That” is moving thing wasn’t there before. Don’t run over it.

I could go on about relevance of self-driving cars to public health (epidemiology of traffic accidents) or aging-in-place (keeping seniors as independent as possible as long as possible), but VC’s story was just plain fun!

Big Data

The second day began mixing big data into the cloud, including some interesting healthcare and biomedical case studies.

Peter Levin @pllevin Chief Technology Officer and Senior Advisor to the Secretary, Department of Veterans Affairs, talked about the VA’s EHR data and some mind-blowing statistics. Click on the photo of the slide to zoom into a readable rendition.

From a later slide I tweeted…

An interesting aspect of live-tweeting a conference is that people who aren’t present can chime it. And then, after the conference is over, a speaker can chime right back, such as below. For example, Peter Levin showed a slide with funny ICD-10 codes and I tweeted….

…triggering the following three-party conversation:

Next is a fascinating series of slides, about text mining EHRs to predict probability of patient recovery, presented by Ram Akella of UC-Santa Cruz in the Big Data Analytics , Processing and Interaction: Measurement Science, Benchmarking and Evaluation Challenges session. I dug around on the Web to try to find more details, but this appears to be a currently ongoing project. Prof. Akella was making larger points about relevance and big data, so he flew through these slides. I present them below without much comment (“Looks like…”, “I surmise…”). The point being: EHR data is indeed part of big data. (Though if I find out more info at a later date I’ll update this post.)

If you click through several times on the image you can get to a more readable version of the slide. It shows free text from the major content subsections of an EHR: medical history, physical examination, treatment, etc.

Here it looks like the data that has been extracted from an patient’s electronic record.

This slide describes the kind of available data available for each patient.

This looks like an influence diagram. The stuff on the left influences the stuff on the right.

I surmise that the “most discriminant terms” are the most valuable terms, information-wise, to predict patient recovery.

I surmise (again, he flew through these slides) that this shows probability of recovery when a particular term (“unstable blood pressure”) is present in the patient’s chart.

To return to a tweet about Peter Levin’s presentation….

…Healthcare truly is a data management challenge and opportunity

Cloud Computing, Big Data and Wrap-Up

What about big data’s relation to cloud computing?

One of the folks from NIST joked that while we know what “cloud computing” is (see earlier section regarding NIST’s role in helping to define it) we don’t know what “big data” is. But we do know it is here and important. So, we all need to help define big data.

So, what are the properties of “big data”?

One way (the only way?) to describe something is in terms of its properties. For example, see my Clinical Groupware: A Definition. One of the NIST reps listed five Big Data properties (see below), but did not define them. I’d heard of Volume, Velocity, and Variety, but not Veracity and Value. I cobbled together the following outline from various venues (VVeeee!).

Apparently the 3D — Volume, Velocity, Variety — distinction goes all the way back to 2001. Wikipedia references that and includes a recent update:

In a 2001 research report[20] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this “3Vs” model for describing big data.[21] In 2012, Gartner updated its definition as follows: “Big data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.” (my emphasis) Wikipedia on Big Data

Relative to adding veracity to big data’s trivium there’s a succinct blog post on Data Science Central. And here are a couple of thumbnails to encourage you to read it. I like the first infographic: lots of dots (volume), moving dots (velocity), different colored dots (variety), and probability distributions around dots (veracity)…


…and a clever Venn diagram of Volume, Velocity, and Variety:


Big Data value? I’ll close with that, connecting it back to healthcare IT.

I’d like to return to the big three 3D of big data, as they are most famously and frequently invoked.

I found this great Big Data Consumer Guide from the Open Data Center Alliance. They only mention healthcare in passing. However their definitions are so accessible, I’ll include them here.

  • Volume. As the name Big Data suggests, its volume can take up terabytes and petabytes of storage space. It has arisen as a result of an increasing enterprise demand to use and analyze more types of structured and unstructured data that do not fit into existing operational and analytic business systems. Data is growing at an exponential rate, so much that 90 percent of the data in the world today has been created in the last two years alone.
  • Velocity. Increasingly, enterprises need answers not next week or next month, but right now. Nightly batch loading is poorly suited for e-commerce, multimedia content delivery, ad targeting, and other real-time applications. This puts pressure on accelerating data loading at the same time that data volumes are skyrocketing. Data streaming, complex event processing, and related technologies, once mostly prevalent in financial services and government, are now emerging as enterprise data architecture requirements in multiple industries. Likewise, as more enterprises engage in social media and the Web, responding in real-time or in near real-time becomes significantly more necessary.
  • Variety. Variety relates to the complexity of data types and data sources. Also, much of today’s data is unstructured or semi-unstructured. This means that it doesn’t fit into neat rows and columns of the traditional relational database management systems (DBMS).”

We usually want to do things to data, such as gather, clean, transform, report, etc. We used to do this on mainframes or desktop computers. Now we upload to the cloud. Different clouds may specialize in different data processing services. How do we move data around? You can’t, practically, download petabytes of data from one cloud in order to upload it to another. In other words, how to you move data through the “inter-cloud” (a phrase I frequently heard).

The next couple of slides addressed this issue, which is, essentially, about data workflow.

That’s proposed abstraction of data workflow. Here is another, in the biomedical domain.

How much does it cost to move data through the cloud? How about 600 picocents a bit? (A completely out of context factoid, I admit!).

And cost of data is only half the equation. Data has to have a positive net value to be retained in an accessible fashion. Otherwise it’s “archived”, in which case, quipped a presenter (@awadallah of @cloudera), it takes an act of God or an order from government to retrieve.

And, keep in mind, cost and price are not the same thing.

One person’s cost (the buyer) is another person’s price (the seller). Folks call for price transparency. Unfortunately, most healthcare organizations don’t know their true costs, so they don’t know how to correctly price. (Hey! I was a pre-med accountancy major!) In order for external price transparency to be helpful it has to reflect internal cost transparency.

Ideally, in a free market, microeconomics operates to drive prices down to costs. This is because if there is any excess profit, sellers will enter the market to compete on price. Once price equals cost there is no more lure. However, if you don’t know your costs, you don’t know what price will allow you to continue in business in a sustainable manner. Price transparency without reflecting true costs is unhelpful to the healthcare system as a whole.

I hope that the combination of cloud computing (with its metered service costs) and big data (about costs!) will help make a dent in healthcare’s price/cost transparency quandary. But that is really another entire blog post. And it is time to wrap up this one!

I’ll close this blog post with the question from the audience that I tweeted above. How do we make sure that standards (after all we are here at the National Institute of Standards and Technology) don’t prevent future innovation? Sound familiar? (If you are from Health IT, it must!). No, I didn’t hear any better answer to this question than I’ve heard in debates about healthcare standards. Oh well. These people are really smart. Makes me feel better.

That said, the NIST Cloud Computing and Big Data Forum & Workshop was truly a remarkable convocation of smart people (I heard about a thousand physically attended, not counting those watching the web cast). I learned a lot about cloud computing, big data, and big data in the cloud, relevant to where healthcare informatics is and is going. I hope you enjoyed my account of the experience, and that the social media angle contributed by my, and others’, tweets, gave it an extra dimension and degree of immediacy.

Can Healthcare Really Have Both Consistent and Flexible Information Systems?

Short Link:

From my recent chapter published in How Knowledge Workers Get Things Done:

Physicians complain about having to hew to what their EHR vendor thinks their workflows should be. It ought to be up to users (or at least someone who knows their workflows) to decide how consistent or flexible they’d like their software to be. Workflow technology (Business Process Management, BPM) opens up the possibility of physicians owning their own workflows. They can make everyone’s workflow the same. They can also allow different physicians to have different preferred workflow.


There’s a stereotype of workflow systems turning users into cogs, with humans doing what machines cannot, instead of machines doing what humans prefer not. That said, defining and executing workflows is one way to influence user behavior, to get folks to do their work the way that medical practice or that hospital intends it be done. Instead of making it impossible for users to depart from intended workflows, the best approach is to make it as easy as possible for users to do their work intended ways, ideally ways that have been vetted and discussed and agreed upon by relevant personnel.

Consistency and flexibility are in natural contradictory tension. At one extreme is a physician, working with lots of physician assistants and nurses and staff, who’d like to make sure everyone does it his or her way. In organizational management-speak, customizable workflows enable greater span-of-control than otherwise possible. At the other extreme, multiple physicians can work together and each have their own workflow. Tension between consistency and flexibility is reduced by using rules and process definitions that non-programming users can understand. They, not their vendor, find the best compromise.

Twitter Reacts to New York Times’ “In Second Look, Few Savings From Digital Health Records”: An Informal Sentiment Analysis

Short Link:

[Nota bene! This post would load too slowly if I included all 200 embedded tweets here. You’ll find them at Rand Tweets.]

If you’re a business these days and you’re not monitoring what people on social media are saying about you and your product or service, you’re making a mistake. The same is true for health IT. I’ve been working in this area for decades. I’ve been on Twitter for four years (longer than more than 99 percent of users). I’ve never heard or seen such jaded, cynical, or even gleeful comments as I saw on Twitter in reaction to the recent New York Time’s article about the RAND report questioning whether EHRs have or can reduce costs. That said, there were insightful and constructive comments too.



Social media is a wonderful way to find and interact with people interested in the same things, and holding the same opinions, as you. This does have the effect of creating echo chambers. For example, most tweeps I follow or who follow me are much more similar to each other and to me than any collection of randomly chosen tweeps. I’m always surprised, when I conduct a systematic search on Twitter, how, well, “different” people are from me and my view of the world. Plus, I find folks who I wouldn’t think might be interested in what I’m interested, but do, in fact, have an opinion. (Eric Topol: OK. Edward Tufte: OK. Bette Midler? Yes. She has an opinion about EHRs.)

I used the following methods for this decidedly informal and un-automated sentiment analysis:

  • I used a variety of search terms, both from the title and the article text.
  • I looked at about 2000 tweets.
  • Exclusion rules reduced that to the about 200 you see below.
  • Excluded simple tweets (ie. no sentiment expressed) of article title (sometimes paraphrased, though not if paraphrase implied a positive or negative sentiment) and links.
  • Excluded simple retweets (though, presumably, RT sentiment usually reflects sentiment of original tweet).
  • Excluded tweets with offensive content
  • Excluded tweets mentioning specific companies
  • Resulting tweets are representative, not exhaustive. After the tenth “Not surprised” (most frequent sentiment) I stopped collecting.
  • That said, the most frequently tweeted sentiments (such as “Not surprised”) do appear more than once.
  • Search and collection occurred about 48 hours after publication. (I sampled about 24 hours later and the volume diminished substantially by then).
  • Some exceptions to my exclusion rules crept through. (I merged half a dozen searches, resulting in a few duplicates. I deleted those obvious to me at the time, but did not invest much more effort than that.)
  • I did not use microblogging sentiment analysis software. That’s why it an “informal sentiment analysis.”

Off the top of my head (told you this was informal) here are frequent or notable reactions:

  • I’m not surprised. No one should be surprised. Why is anyone surprised?
  • EHRs (specifically) are not at fault. It’s the [scape goat here: vendors, payers, gov] that’s the problem.
  • Technology is not at fault. It’s the [scape goat here: culture, luddites] that’s the problem.
  • What are the implications for my country’s similar effort?
  • EHRs aren’t usable, need better design (with which I personally agree)
  • See! I’m not the problem (from physicians)
  • Too preliminary to judge. Wait until after meaningful use stage two or three.
  • Interoperability/lack of standards/lack of patient engagement/etc. is the real problem.

I’m sure you can come up with another couple of categories.

Anyway, like I said up front, this is an informal sentiment analysis of reactions to the NYT’s report on the RAND study. I thought about generating some sort of bar graph, based on sentiment category or polarity (negative five to positive five?), but I’d just be formalizing my own implicit biases. Instead, browse the tweets and bring to bear your own pattern-matching and -explaining cognitive algorithms.

If you are in EHRs or health IT, I think it’s important for you to read these tweets, even if some of them make you wince or seem so so wrong or just plain weird. There is also some great insight and good suggestion. To the degree that Twitter is a real-time mirror of what society thinks, these tweets are what they (not the health IT tweeps you follow) think about you (generically, in this context).

In fact, I encourage you to start conversations with these tweeps. I have done so and benefited from it. If you’re already on Twitter: favorite, retweet, reply. If you’re not on Twitter, what a great excuse to join! Just head on over to with an email address, choose a Twitter name (you can change it later. I’ve done so twice), and agree, disagree, agree to disagree, and so forth. In fact, I created a Twitter list called Tweeted on RAND EHR Study and added the people whose tweets are below. Browse and follow whoever seems interesting. I’m sure they’d also be interested in what you have say about what they had to say. Think of it as outreach from health IT to the rest of society. Feel free to mention this blog post ( and say @EHRworkflow sent you.

The reverse holds true too! If I mention you below (or you noticed when I added you to the Tweeted on RAND EHR Study) feel free to follow those I follow or follow me. Of course you are free to follow anyone at anytime; you don’t need my permission or invitation. However, a high percentage of my fellow tweeps are (ahem) somewhat, peripherally, implicated involved in the healthcare information ecosystem described by the RAND researchers. It’s a target-rich environment, so to speak. You (a non-health IT member of society) and we (health IT folks) will benefit!

To end on an even more positive note, there are lots of new technologies set to “disrupt” lots of industries, including healthcare. These tech trends include mobile, cloud, workflow (my hobby horse), language (with connections to workflow), social, and data technologies. In fact, many of the tweeps I follow, or follow me, on Twitter are intent on doing this disrupting for the greater good. I look forward to your tweets and agreeing with you, disagreeing with you, and retweeting you.


[Nota bene! This post would load too slowly if I included all 200 embedded tweets here. You’ll find them at Rand Tweets.]

Meaningful Use, Workflow Burdens, and the “Broken Iron Triangle” Software Development Anti-Pattern

Process-aware information systemsworkflow management systems, business process management, and adaptive case management — are relevant to meaningful use. The relevance deserves a longer blog post than this. But I can’t resist reposting a Google Plus comment I made to Brian Ahier’s G+ post What It Will Take To Achieve The As-Yet-Unfulfilled Promises Of Health Information Technology about a Health Affairs article of the same name.



I usually tweet links to comments, but cannot, for the life of me, figure out how to tweet a link to a Google Plus comment. Apparently, unlike tweets, they cannot be embedded elsewhere (yet), such as in a blog. Here’s an example of an embedded tweet. In fact, thank you to Vince for tweeting it in the first place, otherwise I would not have known about the Google Plus post and its several dozen comments.

So I took a look and was impressed by Dr. Stanley’s and Dr. Vaughn’s comments. I Googled around and found the quote (below) about effects of “poor planning” and “meaningful use” on “software tweaks” and “workflow burden”. Since this blog is, substantially, about eliminating workflow burdens via tweaking software (editing workflow definitions executed by workflow engines), well, I could no longer resist Google Plus.

But I’ll plagiarize myself, repost the comment here, and tweet it. (Sigh: Sometimes it seems like even my workarounds have workarounds.)

The context is this. A couple of folks from the RAND Corporation followed up a 2005 RAND study that predicted “widespread adoption of health information technology could eventually save the United States more than $81 billion annually by improving the delivery and efficiency of health care.”

From Brian’s excellent summary:

“‘The failure of health information technology to quickly deliver on its promise is not caused by its lack of potential, but rather because of the shortcomings in the design of the IT systems that are currently in place,’ said Dr. Art Kellermann, the study’s senior author and the Paul O’Neill Alcoa Chair in Policy Analysis at RAND, a nonprofit research organization.

‘We believe the productivity gains of health information technology are being delayed by the slow pace of adoption and the failure of many providers to make the process changes needed to realize the potential,’ Kellermann said.”


  • Predicted savings of $81 billion
  • “shortcomings in the design of the IT systems that are currently in place”
  • “failure of many providers to make the process changes needed”

Elsewhere I have argued that current structured-document-based EHRs aren’t up to the task of creating the natural, consistent, supportive, relevant, and flexible workflows we need to transform healthcare (on the scale addressed by RAND’s 2005 report). I could include a few more links here, but, actually, you can chose a blog post at random from the right side of this blog, under “EHR Workflow” (well, maybe not The Twelve Days of EMR Beta Testing, gotta recategorize that!). Instead, we need structured-workflow-based EHRs, built on modern workflow management systems, business process management suites, and adaptive case management foundations. (Natural language processing too! There are fascinating connections between workflow and language.)

Anyway, here’s my Google Plus comment (my first ever, by the way):

“What should be a robust market for reducing administrative costs, the authors write, has been skewed a bit by poor planning in the rush to meet federal meaningful use deadlines, resulting in the need for repeated investment in software tweaks and workflow burdens for some doctors and nurses.”

A long list of features + a series of hard deadlines = recipe for unstable, difficult to use and maintain software. It’s the Iron Triangle anti-pattern of software development. (and lots of hits elsewhere)

This part of the RAND report, albeit reported second hand, rings true to me. The only part I disagree with: “a bit.”

Platitudes? Agreed with by everyone in health IT, even on the outskirts? Not this part. (Are we even talking about the same report?)

Some of the other comments about the 2013 RAND report indicate it just restates the obvious. As you can tell from the last line in my comment, I disagree. At least the paragraph “What should be a robust market…” (admittedly second-hand and somewhat out of context) touches on a real problem. There is no “robust market for reducing administrative costs” and “poor planning” and “meaningful use” are part of the problem.

I don’t think this is a widely held view in health IT (though I think it is gaining mindshare). So the recent RAND report is tacking into a multibillion-dollar headwind to make this argument. For that, I congratulate Kellermann and O’Neill for their effort.

I might have to actually walk a couple blocks, to the Library of Congress, and read it!

Addendum (1/10/13)

Check out tomorrow’s New York Times article on this subject.

Addendum (1/22/13)

My blog post: Twitter Reacts to New York Times’ “In Second Look, Few Savings From Digital Health Records”: An Informal Sentiment Analysis

Natural Language Processing and Healthcare Workflow: Interview with M*Modal’s Chief Scientist Juergen Fritsch, Ph.D.

Here’s a fantastic video and written interview with Juergen Fritsch, Ph.D. of M*Modal. M*Modal is a leading provider of clinical transcription services, clinical documentation workflow solutions, advanced cloud-based speech understanding technology and advanced unstructured data analytics. Juergen is Chief Scientist at M*Modal. (Sounds fun!)

Juergen’s insightful responses, to my in-the-weeds questions about language technology and healthcare workflow, hit so many nails on so many heads that I run out of metaphors. And his heartfelt thoughts on starting a company make me re-appreciate how lucky I am to live and work in the good-ol’ USA!


Here’s Juergen without that pesky YouTube play button
(see below) that turns everyone into an “Arrow Head”!

Juergen Fritsch on LinkedIn

Only one question (#8) is about workflow per se (though it’s implicit in others), but since that is what this blog is about, I telegraphed my punch in the title: Natural Language Processing and Healthcare Workflow…. EHRs and health IT need both workflow and language technology if they are to have a shot at making healthcare substantially more effective, efficient, and satisfying.

One-Minute Interview:
Juergen Fritsch on Founding of M*Modal and US Innovation
[Nota bene! I captured this video at two-frames-a-second
video using Skype. It’s quality, or lack, is my sole responsibility!]

  1. Who is Juergen Fritsch?
  2. Structured vs. Unstructured Data
  3. Closed loop documentation with automated feedback
  4. M*Modal”? “Multi-modal”?
  5. Juergen’s Ph.D. Thesis
  6. Does firing linguists improve speech recognition?
  7. Starting a company in the US
  8. The workflow tech/language tech connection
  9. Moving to pragmatics & discourse processing
  10. Medical equivalent of the HAL computer?

(By the way, while I used Skype for the One-Minute Interview segment of this post, at #HIMSS13 I’ll be out and about with my Infamous HatCam. Tweet me at @EHRworkflow if you’d like a shot at almost real-time stardom. I’ll record, edit, get your OK, upload to YouTube, and tweet your booth number and #HIMSS13 hashtag, all literally on the spot! Here’s an example from the #HIMSS12 exhibit floor.)

QUESTIONS – Answers from Juergen Fritsch, Chief Scientist, M*Modal

1. Who are you? Where do you work? What is your role?

I’m Juergen Fritsch and I serve as Chief Scientist at M*Modal. I’m responsible for all innovation activities around M*Modal’s speech understanding and clinical documentation workflow solutions.

2. In your recent AHIMA presentation, your closing slide included the following bullets:

Unstructured documentation not sufficient
Structured data entry via EHRs not sufficient

What do you mean by this?

Re: second point: The government is pushing for structured data entry. That’s because the EHR paradigm is not sufficient as it forces physicians to abbreviate and be minimalistic in their approach to clinical documentation. Physicians don’t have time anymore to tell the full patient story, leaving quality on the table and creating substandard clinical documentation.

Re: first point: Unstructured documentation is not sufficient because it’s a blob of text and although very valuable for the physician to read, it doesn’t allow a computer to read and then drive action. These are hidden in the unstructured blob of text and not actionable as a result.

3. With respect to the same slide, what do you mean by “closed loop documentation with automated feedback”?

Closed loop means bidirectional. As a physician when I do documentation I do not want to just provide input, I need to be able to get feedback, hear back from the system, as in “yes this is sufficient,” and give all the details I need. In most cases there’s a lack of specificity in the documentation. Here’s an example:

If I’m documenting a patient with a hand fracture I will need to comply with ICD-10 and be able to provide detail as to which fingers, which arm, is it healing or not. It’s a lot of detail from a billing perspective that physicians may not even think to provide on their own. Closed loop documentation helps with that by constantly observing the information being provided and prompting the physician to fill in missing information or address a lack of specificity in the system so at the end you get the best possible documentation with the least amount of effort.

4. When I look at “M*Modal”, I think “multimodal”. Right or wrong? How did you (or whoever) come up with the name? What does the “multi” refer to?

Great question. Yes, M*Modal refers to multimodal, and means that only one way of doing things is not sufficient. In other words, different physicians have different approaches, preferences and needs. For example, an ER physician may not have a hands-free environment, and may want to use a microphone when creating documentation, while a primary care physician can be in front of a computer and enter things right then and there with the patient. At M*Modal, we want to be multimodal and not force physicians into one way of doing things, but accommodate them, their needs and different ways of completing documentation.

5. The title of your Ph.D. thesis was “Hierarchical Connectionist Acoustic Modeling for Domain-Adaptive Large Vocabulary Speech Recognition.” In basic, non-mathematical terms, could you explain your research? Is it still relevant? How has speech recognition evolved since?

That thesis was about using artificial neuro networks to do speech recognition. At the time there was not much research done along those lines and there was a prevailing way of doing things using statistical method. I tried to apply artificial neuro networks and was quite successful. Interestingly there was just recently a renaissance of that idea and people picked it up again with a slight twist. It has evolved and I can provide more details via our video interview if you are interested. What’s exciting to me is, it is being picked up again, not in almost five to seven years, but now again people are doing it.

6. I studied computational linguistics back when one took courses in linguistics and GOFAI (Good Old Fashioned Artificial Intelligence). Statistical and machine learning approaches superseded that kind of NLP with considerable success. Will the pendulum continue to swing? In which direction? In other words, is “Every time I fire a linguist, the performance of our speech recognition system goes up.” still true?

Unfortunately, yes, this is still true – mostly because we have so much data available to us. Stat methods have been so successful in replacing the old school linguistic approaches because of good, plentiful data. The enormous amount of data available makes those methods so difficult to replace.

7. You performed original research, founded a company, and continue to evolve those ideas and that product. This must be personally satisfying. Could you share some thoughts about science, innovation, jobs, and economic progress?

I found it extremely gratifying coming to the U.S. as a student, not having been born or raised here, and being able to work on challenging, cutting-edge research problems. Then getting the opportunity to form a company and how relatively easy it was to get started, and how much people gave a very small company with only about 10 people and not much revenue a chance, was also very gratifying. I would never have been able to do this in my home country – there would have been too many obstacles and people would not have been ready to bet on a startup as much as they do here. The American culture of giving the underdog a chance to try out new ideas as long as they are perceived to be valuable is very rewarding. I would encourage students of various disciplines to try the same things.

8. I write and tweet a lot about workflow management systems and business process management systems in healthcare. These include, at the very least, workflow engines and process definitions. To me, there does seem to be some similarities, or at least complementary fit, between language technology and workflow technology. For example, on the M*Modal website is a short page where “workflow” is mentioned nine times, as well as “workflow orchestration” and a “workflow management module.”

From your unique perspective, what is the connection between language tech and workflow tech?

This is an absolute dead-on question, I’m so happy you asked it. The important connection is if we would just do speech-to-text transcription we wouldn’t affect anything. We’d just be creating a piece of text, without being able to drive actions. Ultimately we want to drive that action in the workflow – for example, have a physician create that order for a new medication. We want to make sure follow up happens and facilitate the workflow that enables that process from beginning to end. Also, healthcare is all about collaboration among providers. There is a lot of patient handoff and effective coordination of care doesn’t happen nearly as much as it should, and it only happens if proper workflow processes are in place. If we’re not trying to get involved in that process and drive more effective workflow processes, we’re not being successful in affecting change.

9. As you know, computational linguistics, the science behind the NLP engineering, is about more than sound (phonetics and phonology), sentence structure (syntax), or even meaning (semantics). It’s also conversation (discourse) and achieving goals (pragmatics). Where do you see medical language technology going in this regard?

Again, you hit it dead-on – in the past, people have ignored the pragmatics aspect. At M*Modal we have been focused on pragmatics since the very beginning. Where it’s all going is being able to understand the content of speech, using semantics and syntax to understand what people are really talking about. You are absolutely right that without pragmatics we’d never be able to accomplish what we’re trying to with NLP technology.

10. How many years until we have the medical equivalent of the HAL computer depicted in the Movie 2001?

Hopefully never! ☺ We are getting there in a different way but I don’t think that the computer will ever replace humans. The computer will provide information, guide and educate the user, but not replace the human decision-making process.