Month: January 2013

Short link (well, easy to remember, at least): http://ehr.bz/emranswers

This week I got to sit down (virtually speaking) and talk shop with Linda of 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 ?
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 .

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. , is my personal standards authority. I deeply respect his perspective on interoperability.
• Vala Afshar, – I admire his leadership lessons and valuable insight.
• David Scher MD, is a former cardiac electrophysiologist, and has become a great friend. He is the Senior Medical Advisor .
• I love startups, hence .
• is for mental health advocacy.
• @ is my alter-ego.
• Lisa is a great person and a good friend, always optimistic.
• I always look for Tom Sullivan’s news and information .
• 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 Scoop.it. 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 paper.li 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 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!)

Short Link: http://ehr.bz/nistcbd

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…

• Is AWS Secure Enough for Patient-Identifiable Data?
• What Does AWS Bring to the EHR and Health IT Party?
• Chuck: Your Blog is Called EHR Workflow Management Systems…Well?

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

Attending Computing & Forum January 15-17 Hashtag is

— Charles Webster, MD ()

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…

How many ‘cloud-based’ EHRs meet all five NIST’s essential characteristics? @

— Charles Webster, MD ()

Cloud Computing: NIST Definition Measured network access 2 on-demand self-service w/resource pooling & rapid elasticity

— Charles Webster, MD ()

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.”

Federated Community Cloud Definition

— Charles Webster, MD ()

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 Organization: multiple orgs using federation to share computing resources (8 example capabilities)

— Charles Webster, MD ()

“[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.

Federated Community Cloud Scenarios: specialized remote medical care & catastrophic dynamic event response..

— Charles Webster, MD ()

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)

“Workflow” mention at > needs to be part of data science educational curricula

— Charles Webster, MD ()

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.

CD: Focusing now on healthcare organizations; not just networking, not just computing, but also authentication, records mgmt etc.

— Kenna Shaw ()

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.

Vint Cerf speaking at about “loose coupling” (earthquake analogy) in computing to achieve

— Charles Webster, MD ()

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

Question 2 Vint Cerf healthcare data & Google’s discontinued initiative: acuteness effects privacy, virtual clinical trials, etc.

— Charles Webster, MD ()

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

Question 2 Vint Cerf healthcare data & Google’s discontinued initiative: couldn’t get medical folks to contribute enough data…

— Charles Webster, MD ()

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!

Vint Cerf on healthcare: “disappointed” but need to create tools “at the edge” to make doctor’s life easier rather than harder!

— Charles Webster, MD ()

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 uploaded 5-minute YouTube video of Vint Cerf telling funny! story about Google’s Self-Driving Cars 1/16/13

— Charles Webster, MD ()

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 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.

EHRs Matter at the VA @ shows amazing stats: patient volume, quality, cost, evidence, accuracy, rates

— Charles Webster, MD ()

From a later slide I tweeted…

Proteomic snapshot/person/day for one year=4600 million terabytes (total 2010 Internet data = 250 million terabytes)

— Charles Webster, MD ()

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….

Funny @ “some codes downright insulting” “bizarre personal appearance” “walked into lamppost, subsequent encounter”

— Charles Webster, MD ()

…triggering the following three-party conversation:

@ @ only humor. Support .I was just pointing out that there are a LOT of them.I mean . . . chicken coops?

— Peter L. Levin ()

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.)

Slide 1/6. Info extraction from

— Charles Webster, MD ()

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.

Slide 2/6. data set R Akella

— Charles Webster, MD ()

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

Slide 3/6 Experimental setting R Akella

— Charles Webster, MD ()

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

Slide 4/6. Methodology Ram Akella

— Charles Webster, MD ()

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

Slide 5/6 Most Discriminant Terms Ram Akella

— Charles Webster, MD ()

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

Slide 6/6 Results: Prob of Recovery Akella

— Charles Webster, MD ()

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 is truly data management challenge & opportunity -Peter Levin, VA CTO

— MJ Schmitt ()

…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?

Yesterday’s about computing, today’s about (BigData/Cloud intersection) stay tuned 4 tweets, links & photos!

— Charles Webster, MD ()

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.

Pat Gallagher, Under Sec NIST opens with “We haven’t defined a definition for big data but whatever it is, it is here”

— Bernadette Hyland ()

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

The ‘V’s of volume, velocity, variety, veracity, value

— Charles Webster, MD ()

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

• Volume: “amount of data” (Healthcare example: A single medical sensor emits an extraordinary amount of data per unit time. Now multiply!)
• Velocity: “speed of data in and out” (Healthcare example: Reacting in the right way to the right patient event within seconds can save a life)
• Variety: “range of data types and sources” (Healthcare example: Structured data such as blood pressures and clinical codes versus unstructured free text dictated by clinicians)
• Veracity: “uncertain or imprecise data” (Healthcare example: Natural language descriptions of clinical phenomena is notoriously imprecise and even structured clinical data is almost always suspect)
• Value: “positive net value to be retained in an accessible fashion” (Healthcare example: See my closing remarks…)

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.

Nice synthesis of workflow: collect, reorder, join, identify, model, validate, apply, publish

— Charles Webster, MD ()

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

Biomedical Pipeline: data, transmit, store, process, analysis, results

— Charles Webster, MD ()

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!).

600 picocents to move a bit of data over the cloud ‘sGetSmall!

— Charles Webster, MD ()

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 ( of ), it takes an act of God or an order from government to retrieve.

ROB Return On Byte

— Charles Webster, MD ()

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

OH “Cost & price are not the same thing” Me: otherwise no profit, therefore unsustainable, healthcare shld learn this

— Charles Webster, MD ()

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!

Question: How make sure standards don’t hobble innovation? (reminds me of healthcare debate) “strong tension” slow standard APIs

— Charles Webster, MD ()

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.

Short Link: http://ehr.bz/rand

[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.

Surprise!

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 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 (http://ehr.bz/rand) and say sent you.

The reverse holds true too! If I mention you below (or you noticed when I added you to the ) 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.

Engage!

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