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Is Skinny Data Harder Than Big Data?

On my post about Improving the Quality of EHR data for Healthcare Analytics, Glenn made a really great comment that I think is worth highlighting.

Power to change outcomes starts with liberating the data. Then transforming all that data into information and finally into knowledge. Ok – Sorry, that’s probably blindingly obvious. But skinny-data is a good metaphor because you don’t need to liberate ALL the data. And in fact the skinny metaphor covers what I refer to as the data becoming information part (filter out the noise). Selective liberation and combination into a skinny warehouse or skinny data platform is also manageable. And then build on top of that the analytics that release the knowledge to enable better outcomes. Now …if only all those behemoth mandated products would loosen up on their data controls…

His simple comment “filter out the noise” made me realize that skinny data might actually be much harder to do than big data. If you ask someone to just aggregate all the data, that is a generally pretty easy task. Once you start taking on the selection of data that really matters, it becomes much harder. This is likely why so many Enterprise Data Warehouses sit their basically idle. Knowing which data is useful, making sure it is collected in a useful way, and then putting that data to use is much harder than just aggregating all the data.

Dana Sellers commented on this in this Hospital EHR and Healthcare Analytics video interview I did (the whole video has some great insights). She said that data governance is going to be an important challenge going forward. Although she defined data governance as making sure that you’re collecting the data in a way that you know what that data really means and how it can be used in the future. That’s a powerful concept and one that most people haven’t dug into very much. They’re going to have to if they want to start using their data for good.

May 24, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

Medical Apps, $21 Billion EMR Market, and Sick of EMR


This is a pretty interesting idea and another way to talk about subjects we’ve talked about many times here. The idea of an app in this case is an app on top of EMR software. I call this making the Smart EMR. It will likely come from these apps. The article is right that many of the data warehouses are clunky and don’t serve the doctors. In fact, there are very few data warehouses focused on the doctors needs at all.


The last EMR incentive numbers I saw were at $10 billion. Does that mean the government has funded half of the market? These numbers are always a little fishy, but it’s interesting to consider how big the EMR market is.


I actually know a lot of doctors who love their EMR and wouldn’t practice medicine without one. What I think most doctors are tired of is all the government regulations. We shouldn’t confuse government regulations with EMR.

April 21, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

Healthcare Doesn’t Do Big Data Yet…It Does BI

It seems like healthcare big data is the topic du jour lately. Everyone seems interested in how they can tap into the big data in healthcare. I’m not sure what’s caused the flood of healthcare big data people. I expect that some of it comes from the rush of EHR implementations that have happened thanks in large part to the EHR incentive money. I imagine there’s a whole group of hospital CIO’s that are wondering how they can leverage all of that EHR data to benefit their institution and patients.

I think it’s great that healthcare has finally seemed to realize that there’s a lot of value found in healthcare data. The problem is that in every other industry, what we call healthcare big data isn’t very big data at all. In fact, most other industries would describe most of the healthcare data efforts as pretty simple business intelligence. Yes, there are pockets of exceptions, but most of the data initiatives I’ve seen in healthcare don’t even approach the true meaning of the words big data.

I’m not saying that there’s anything wrong with this. In fact, I loved when I met with Encore Health Resources and they embraced the idea of “skinny” healthcare data. Maybe it was a way for them to market their product a little different, but regardless of their intent they’re right that we’re still working on skinny data in healthcare. I’d much rather see a bunch of meaningful skinny data projects than a true healthcare big data project that had no results.

Plus, I think this highlights the extraordinary opportunity that’s available to healthcare when it comes to data. If all we’re doing with healthcare data is BI, then that means there is still a wide open ocean of opportunity available for true big data efforts.

I think the biggest challenges we face is around data standards and data liquidity. Related to data standards is the quality of the data, but a standard can often help improve the data quality. Plus, the standard can help to make the data more liquid as well.

Yes, I’m sure the healthcare privacy experts are ready to raise the red flag of privacy when I talk about healthcare data liquidity. However, data liquidity and privacy can both be accomplished. Just give it time and wait for the healthcare data revolution to happen.

April 15, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

Big Data Analytics vs Focused Patient Analytics

One of the most common buzzwords in healthcare right now is “big data.” Everyone is talking about how to leverage big data in healthcare. There is little doubt that there are a whole list of opportunities that are available to healthcare using big data analytics.

When it comes to big data analytics, most people see it as healthcare business intelligence. In other words, how do we take all the data from within the organization and leverage it to benefit the business. Or in the case of a health insurance company, how can we use all the healthcare data that’s available out there to benefit our business. This is really powerful stuff that can’t be ignored. A lot of money can be made/saved by a business that properly leverages the data it holds.

However, I think there’s another side of healthcare big data that doesn’t get nearly enough attention. Instead of calling it big data analytics, I like to call it focused patient analytics.

What is focused patient analytics? It’s where you take relatively small elements from big data that are focused on a specific patient. In aggregate the data that you get is relatively small, but when you consider all of the data is focused around one patient it can be a significant amount of valuable data. Plus, it requires all the healthcare big data silos be available to make this happen. Unfortunately, we’re not there yet, but we will get there.

Imagine how much smarter you could make the EHR if the EHR could tap into the various silos of healthcare data in order to create focused patient analytics. Unfortunately, we can’t even design these type of smart EHR software, because too much of the data is unavailable to EHR software. I love to think about the innovation that would be possible if there was a free flow of data to those that needed it in healthcare.

Certainly there are plenty of security risks and privacy concerns to consider. However, we can’t let that challenge be an excuse for us not to create focused patient analytics that will benefit patients.

December 18, 2012 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

EMR Interfaces Gone Wrong, Or The Tale Of The Albanian Patient

Today, for your consideration, we have the tale of the Albanian patient who wasn’t Albanian.  More broadly, I’m here to discuss the perils of adding an extra interface consideration to the workflow of busy EMR users, and the impact that has on data quality.

Scope, a blog published by the Stanford School of Medicine, shares the case of the Merced County, California physician who, exasperated with the requirement that he identify the ethnicity of each patient, chooses “Albanian” for all of them. Why? Simply because “Albanian” is the first item of the rather long list in the pulldown menu.

As a result of this interface issue, any attempt to mine this veteran doctor’s data for population health analysis is weakened, writes Anna Lembke, MD, asssistant professor of psychiatry and behavioral sciences at Stanford.  And this physician’s choices should give the “big data” users pause, she suggests:

Misinformation in electronic medical records, whether accidental or otherwise, has far-reaching consequences for patients and health care policy, because electronic medical records are being actively ‘data-mined’ by large health care conglomerates and the government as a basis for improving care. This is an important downside to consider as we move forward.

Dr. Lembke’s observations are important ones. If government entities and health organizations would like to mine the increasingly large pools of data EMRs are collecting, it’s important to look at whether the data collected actually reflects the care being given and the patients being seen.

I’m not suggesting that we audit clinicians’ efforts wholesale — they’d rightfully find it offensively intrusive — but I am suggesting that we audit the interfaces themselves from time to time.  Even a quarterly review of the interfaces and workflow an EMR demands, and results it produces, might help make sure that the data actually reflects reality.

October 16, 2012 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies.

Business Intelligence Gets a Much-Needed Boost in New Book

As I mentioned last week, I’m in the midst of reading Laura Madsen’s book, “Healthcare Business Intelligence: a Guide to Empowering Successful Data Reporting and Analytics.” I admit it’s kind of slow going, though not because of any lack of writing talent on Madsen’s part. On the contrary, the Lancet Software executive and founder of the Healthcare BI Summit has taken what at times can be a somewhat dry topic, and put a real-world clinical spin on it, injecting a bit of levity here and there to boot.

I’m reading at a snail’s pace because the events of National Health IT week have got me going this way and that – following the #NHITweek tweet stream, attending more webinars than I can count, attending networking events, etc. I’m looking forward to capping the week off with the #HITsm tweet chat on Friday, which will likely focus on the main themes brought forth by the ONC and HIMSS.

And so I’m glad, even though I haven’t finished her book, I had a chance to chat with Madsen before this week began about why the time was ripe for her book, and how the landscape of business intelligence and data analytics is evolving before our very eyes.

How have you seen the healthcare BI and data warehousing landscape change in the last 10 years? What has surprised you the most?
Healthcare BI has changed drastically. I would say that’s it’s gone from a back-office data geek operation to a front-of-the-house strategic effort in the last two years. The technology landscape in that time has changed significantly as well. We have solved so many of the issues that used to ‘hang us up.’ I can’t tell you how many conversations I used to have on ‘stovepiped’ data models or ‘architecting into a corner.’ The scalability of the products has improved our ability to deliver predictive analytics using desktop tools. It’s been a wild ride!

Why write this book now?
Well, simply, there was finally an audience. For years, BI in healthcare was something the big payers did, and if you wanted to do it you had to work with them. But as the impact of the Affordable Care Act (ACA) really started to sink in, most providers realized that data wasn’t a checkbox activity anymore; they would have to not only use EHRs, but the data that was coming out of them as well, to maintain their foothold.

On a related note, how long ago did you found the summit? Why was the time ripe then for its debut?
I founded the summit four years ago, in my first year at Lancet. In the beginning, I think most people thought I was a bit crazy. The truth was, I spent most of my career in healthcare BI and had to find the people that were doing it the hard way. I always wanted to have a good conference to go to that would allow me to network and fill the gaps that whitepapers and vendors can’t. It was purely by luck that I was right in guessing that other people felt that way too.

How have you seen the event’s demographics and content change in the last several years?
Interest has grown nearly exponentially just from last year to this year. This is the first year we are having a more ‘tactical’ track, with organizations that have created data warehouses so they can talk about the logistics of doing the work. It’s a lot to cover in a typical one-hour conference session, but it’s so important for us to learn from one another. We continue to draw attendees that are manager-level and above, and many other analysts from both payers and providers, so I believe we have hit the right mix of content.

In the beginning we still focused a lot on ‘reports’, whereas this year we have more content on analytics and nothing on traditional reports. It’s been an evolution that is matching the rapid changes in the industry. It’s fun to watch!

What will you be concentrating on in your keynote at this year’s summit?
The presentation is entitled,” Above the Fray: Delivering on the Promise of Healthcare BI.” The focus is really just that: There is such a frenetic pace in healthcare these days that it’s really difficult to focus. Based on the five tenets I wrote about in my book, I talk about the things healthcare organizations should focus on in their first year of a BI effort.

Aside from the BI summit, what are your must-attend healthcare events?
I like the smaller conferences that focus on healthcare analytics. Every two years The Center for Business Innovation (TCBI) does a conference on healthcare analytics. The conference has a lot of depth, but it’s small enough that it allows you to interact. Any conference like that is a winner for me, and the good news is that there are many of them now with the increased interest in healthcare BI.

In the book’s preface, you mention “more than 70 percent of BI programs fail on their first attempt.” That’s a huge number. What do you think is the main reason for these failures?
If I had to pick one thing, it’s that most organizations don’t treat BI like an ecosystem.  I introduced this idea in a Lancet blog a year or so ago.  Because BI is so inter-related, it is fraught with challenges. In the experiences that I’ve had, you can’t attribute ‘failure’ to just one thing. It’s also important to note that we often ‘fail’ without realizing it. BI will deliver something but the business doesn’t think it’s the right thing, or enough. Expectation management is an important piece of the work, and so is marketing the effort. Because failure is really high in first-attempt BI programs, I wrote about the tenets of BI – the things you have to do first to ensure success.

What do you think is the next step for data in healthcare? Where do you see it having the biggest impact on patient care in the next 5 to 10 years?
Without question, if we can crack the code of unstructured data, that will revolutionize healthcare BI. With the consistent use of unstructured data, we can glean so much more information from the data—important, clinically relevant information. This will really help in our journey towards analytics in healthcare. Also, any effort the industry can make towards consistency would be beneficial. We may see this evolve as a result of HIEs. The current landscape of healthcare data is so different that any sharing, or frankly, any attempt to create products will be slow to adopt because the work involved in getting the data in is so significant. We could change healthcare quickly with some consistency around products and codes; then innovation associated with healthcare data could be much farther reaching. Compared to today, when you really have to have someone that knows the data in your organization very well.

Broadly, I believe BI in healthcare will move much more towards the patient. In chapter 8 of my book, I talk about future trends. One thing I see is that the next generation has a much different perspective on privacy, confidentiality and the type and amount of information they have available to them. BI to the patient that is context-based and visually well-designed will forever change healthcare.

September 13, 2012 I Written By

As Social Marketing Director at Billian, Jennifer Dennard is responsible for the continuing development and implementation of the company’s social media strategies for its three key properties – Billian’s HealthDATA, Porter Research and HITR.com. She is a regular contributor to a number of healthcare blogs, and currently manages the Technology Association of Georgia Health Society’s social media channels. You can find her on Twitter @SmyrnaGirl.

The Immortal Life of Healthcare IT

As any one of my family or friends will tell you, I’m a voracious reader. I’ll read anything I can get my hands on – blogs, online news, books, magazines. I’ll even confess that after a week of keeping up with healthcare IT editorial, I typically enjoy a good, diverting issue of Entertainment Weekly on the weekend. Having an e-reader in the house has only increased my propensity to check out books from my local library, thanks to its new e-book lending program. Mobile technology has certainly aided and abetted my habit.

That being said, I find myself juggling two books right now – “The Immortal Life of Henrietta Lacks,” by Rebecca Skloot (great New York Times book review here); and “Healthcare Business Intelligence: a Guide to Empowering Successful Data Reporting and Analytics,” by Laura B. Madsen. One is for pleasure, while the other is to help me better understand the buzz behind BI. Both have much to say on the subject of healthcare. In the simplest of terms, they are two sides of the same coin. Skloot’s work of non-fiction tells the tale of what happens when patients and their families are kept in the dark, while Madsen’s guide denotes the possibilities that come with dissecting data in meaningful ways for patient benefit – freeing information, if you will, from silos for the benefit of better clinical outcomes.

I’m not too far into The Immortal Life, but one paragraph has jumped out at me in light of the current state of heightened patient engagement in healthcare:

“… like most patients in the 1950s, she deferred to anything her doctors said. This was a time when ‘benevolent deception’ was a common practice – doctors often withheld even the most fundamental information from their patients, sometimes not giving them any diagnosis at all. They believed it was best not to confuse or upset patients with frightening terms they might not understand, like cancer. Doctors knew best, and most patients didn’t question that.”

My how times are changing. (Granted, you’d hope that in 60+ years they would.) Patients are seeking information out before they even think to call their doctor. And they are no longer afraid to question diagnoses, or even obtain second opinions. Patients are becoming more interested in the value of their care – is the financial outlay worth the result? And some are beginning to wonder when their doctors will catch up.

By pure coincidence, HIMSS is asking the question “How will health IT make a difference a year from now at the next National Health IT Week?” as part of its first annual blog carnival, in an effort to highlight the week’s activities and reflect on the strides healthcare IT has made in the six preceding years the event has been held.

I would have to say that as the next year passes, we’ll see healthcare IT increase patient engagement – digital or otherwise. More doctors will implement EHRs, participate in HIEs, sign up for ACOs. Along the way, they’ll find themselves confronted with patients who are used to having instant access to up-to-the-minute information on everything, and who think access to their personal health information should be no different.

Couple this with the increasing consumerization of healthcare and IT – be it the new iPhone, the smaller iPad, fitness and weight-loss apps, cars that help you keep up with your quantified self, and other gadgets that let you “check your body as often as your email,” and you’ve got a population of patients ripe for aiding and abetting this transformation of healthcare we’ve been hearing so much about.

How interesting it is to think that Henrietta Lacks’ cells are still alive today to inadvertently be a part of this movement, when she herself was kept in the dark by the systemic problems of a society that never thought to question its care.

September 5, 2012 I Written By

As Social Marketing Director at Billian, Jennifer Dennard is responsible for the continuing development and implementation of the company’s social media strategies for its three key properties – Billian’s HealthDATA, Porter Research and HITR.com. She is a regular contributor to a number of healthcare blogs, and currently manages the Technology Association of Georgia Health Society’s social media channels. You can find her on Twitter @SmyrnaGirl.

Increasing Revenue Through Clinical Connectivity

As most of you know, I’ve been working hard to create more content related to revenue in healthcare. My interest in this has grown even more since I had the chance to attend the ANI 2012 conference in Las Vegas where I got the chance to talk to people like Rishi Saurabh from GE Healthcare. It’s amazing how many people (myself included) don’t think that revenue cycle management is sexy since there are so many opportunities in healthcare.

One example of missed healthcare revenue management opportunities has to do with connecting clinical content with the financial data. From my experience, it’s quite rare to see a healthcare institution that does a great job of connecting these two pieces of data. The clinical data is in a silo of its own and it’s only looked at by the clinical people. The financial data is in its own financial data silo and only ever looked at by the financial people.

These silos are a problem and present a really big opportunity for healthcare organizations to increase the revenue of their organization. Although, doing so in an organization is not always easy. It takes great leadership to bridge the two content silos. Plus, you need someone who’s effective at understanding both the clinical and financial point of view. So, it’s not hard to understand why this doesn’t happen more often.

I think the most basic example of what I’m talking about can be seen in the annual checkup. I was talking with a colleague the other day when I told him that I couldn’t remember the last time that I’d been to my doctor. In fact, I honestly don’t even know my doctor’s name (which might beg the question of whether he’s really MY doctor). Why hasn’t my doctor sent me a reminder about the need to do an annual physical exam? Why don’t I have a regular connection with my doctor that helps me to take better care of my health?

I think at least part of the answer to this is that the clinical is not tied to the financial. If the clinical were tied to the financial, then the doctor could provide a care plan for me and my specific health needs. Then, the financial could ensure that I’m following that care plan. Imagine the revenue implications of me visiting the doctor regularly as part of a well defined care plan.

I’m sure that many of you out there are likely skeptical about whether patient reminders will actually change behavior. Certainly in many cases, these reminders will be discarded or ignored. However, a certain percentage of those reminders will be followed. This will mean your patients get better care and your clinic increases their revenue. Plus, maybe we need to take a deeper look at the care plans that we offer patients. If large percentages are ignoring the suggestions, then maybe we need to rethink the plan or how we’re communicating that plan to the patient.

There are certainly plenty of other medical examples where a follow up doctor visit would make sense and improve the health of your patients. In fact, you could get really sophisticated with how you reach out to your patient population.

I believe the key to success of this type of program is to integrate the clinical data with the financial data. It creates tremendous power and amazing opportunities.

August 27, 2012 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

We Know What’s Right, but It’s Hard

In perusing various blogs, I came across Matthew Gibson, MD’s blog and this really compelling article titled, “It’s So Easy, and Yet…” Here’s one especially poignant section:

What I see day in and day out is complications of simple, easy to manage problems like diabetes, high blood pressure, asthma, etc. These are things that we KNOW how to treat. We know how to prevent complications. And yet, I just had a man last week who required half of his foot to be amputated as a complication of untreated diabetes. I had a woman this week who came in seeing snakes on people’s clothing, because her blood pressure was so high it was affecting her mind. Last month, I saw a man who had large amounts of yeast growing in his mouth and groin because his blood sugar (and thus urinary sugar) was so high.

This morning, I’m caring for a truly pleasant gentleman with COPD (bad chronic lung disease usually caused by smoking). He hasn’t smoked in the last 15 years, but he smoked quite heavily before that. Even though he’s been doing things all right as far as his lungs are concerned for the last 15 years, he has to live with the consequences of his actions prior to that. For the last several days, I’ve seen him decompensate and gasp for air, feeling like he’s drowning, because he can’t get the air to move through his lungs like he should. How did this kind old man get to this point?

At the core of his comments is the idea of how do we motivate ourselves to do something we know we should be doing. This is a really hard question to answer and something we probably will never solve completely. However, I think there’s plenty of room to improve even if we never become perfect at it.

Over on Smart Phone Healthcare we’ve spent a lot of time reviewing various mobile health applications. I’d say that the large majority of mHealth applications are about trying to help solve this problem. Plus, I think the mobile device connected with good data about ourselves is one method that will help us be healthier.

Related to this idea, is what I’ve called treating healthy patients. This is a concept that won’t leave me since I think it will be a fundamental part of the future of healthcare. I believe we’re on the brink of a series of devices and technology that will help us monitor our bodies in such a way that we can identify sickness within us before we feel sick. This information won’t make everyone change their behaviors, but it will help many.

We’re in the very early stages of monitoring our bodies and connecting all that data with action. However, it’s exciting to see that now many of these things are possible thanks to powerful computing and a new generation of devices.

August 24, 2012 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus.

Creating The Intelligence-Based EMR

Given today’s trends, I’m betting most of us would agree that EMRs need to evolve from transaction-based to intelligence based systems. They need to do better leveraging “big data,” make context-based care recommendations and support smart processes.  John likes to call them “Smart EMR,” but what would such an EMR look like?

In a recent issue of Hospitals & Health Networks, Dr. John Glaser, Ph.D.,  lays out a long– but useful– explanation as to why EMRs are stiffly focused on transactions such as documenting a visit or writing a prescription. (Very short summary: That’s just where they are coming from historically.)  Then he offers a take on the “intelligence-based EMR” and what it will take to get there.

Glaser, CEO of the Health Services Business for Siemens Healthcare, was formerly VP and CIO for Partners HealthCare, so he’s got both the vendor and the care provider view, which I think proves very useful for this discussion.

In his article, he argues that the next-gen EMR needs to offer the following:

  • foundational sets of templates, guidelines and order sets that reflect the best evidence or established best practice;
  • a process-management infrastructure that supports basic transaction checking such as drug-drug interactions, as well as asynchronous alerting like panic lab reporting and process monitoring and guidance;
  • team-based care support such as shared work lists, as well as tools for patient engagement and health information exchange;
  • novel decision aids like predictive models that can tell us if a particular patient is likely to be readmitted because he or she is fragile or has a substandard social situation at home that may negatively impact healing;
  • context-aware order sets and documentation templates that guide the physician and help infer what types of orders should be placed and what types of documentation should be done
  • intelligent displays of data, intelligent correction and identification of data, and extraction of structure by going through free text and pulling out quality measures or problems that were not previously in a patient’s problem list, for example.

The question is, are these functions science fiction (i.e. many years away from being standard) or just an evolutionary leap from today’s systems?  What are you seeing out there?

August 21, 2012 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies.