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Big Data is Like Teenage Sex

Yes, that is a catchy headline, but if you’ve read me for anytime you also know I love a good analogy. This analogy comes from Dan Ariely as shared by Iwona during #cikm2013.

For those who can’t load the image it says:
Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it…

As a big proponent of no sex before marriage, this is a little out there for me, but the analogy illustrated the point so well. In fact, I think this is why in healthcare we’re seeing a new line of smaller data project with meaningful outcomes.

What I wish we could change is the final part. How about we all stop hiding behind what we are and aren’t doing. We all deserve to be frank about our actual efforts. The fact is that many organizations aren’t doing anything with big data and quite frankly they shouldn’t be doing anything. Kind of like how many teenagers shouldn’t be having sex.

November 5, 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 Big Data and Meaningful Use Challenges Video

This Fall we decided to do a whole series of weekly video interviews with top healthcare IT thought leaders. Many of you may have come across our EHR video site and the Healthcare Scene YouTube channel where we host all of the videos. The next interview in that series is happening Thursday, October 3rd at 1:00 EST with Dr. Tom Giannulli, discussing the future of small physician practices. You can join with us live or watch the recorded video after the event. Plus, you can see all the future interviews we have scheduled here.

Last week’s video interview was with Mandi Bishop, Principal at Adaptive Project Solutions and also a writer at EMR and HIPAA. Mandi does an amazing job sharing her insights into healthcare big data and the challenges of meaningful use. We also dig in to EHR data sharing with insurance plans and ask Mandi if meaningful use is completely devoid of value or not.

For those who missed the live interview, you can watch the recorded interview with Mandi Bishop embedded below.

October 2, 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 Impacting Healthcare

The following is a guest post by Sarah E. Fletcher, BS, BSN, RN-BC, MedSys Group Consultant.
Sarah Fletcher
It is generally agreed that bigger is better.  When it comes to data, big data can be a challenge as well as a boon for healthcare.  As Meaningful Use drives electronic documentation and technologies grow to support it, big data is a reality that has to be managed to be meaningful.

Medical databases are becoming petabytes of data from any number of sources covering every aspect of a patient’s stay.  Hospitals can capture every medication, band-aid, or vital sign.  Image studies and reports are stored in imaging systems next to scanned documents and EKGs.

Each medication transaction includes drug, dose, and route details, which are sent to the dispensing cabinet.  The patient and medication can be scanned at the bedside and documentation added in real time.  Each step of the way is logged with a time stamp including provider entry, pharmacist verification, and nurse administration.  One dose of medication has dozens of individual datum.

All of this data is captured for each medication dose administered in a hospital, which can be tens of thousands of doses per month. Translate the extent of data captured to every patient transfer, surgery, or bandage, and the scope of the big data becomes clearer.

With the future of Health Information Exchanges (HIEs), hospitals will have access not just to their own patient data, but everyone else’s data as well.  Personal health records (PHRs), maintained by the patients themselves, may also lend themselves to big data and provide every mile run, blood pressure or weight measured at home, and each medication taken.

One of the primary challenges with big data is that the clinicians who use the data do not speak the same language as the programmers who design the system and analyze the data.  Determining how much data should be displayed in what format should be a partnership between the clinical and the technical teams to ensure the clinical relevance of the data is maximized to improve patient outcomes.  Big data is a relatively new event and data analysts able to manage these vast amounts of data are in short supply, especially those who can understand clinical data needs.

Especially challenging is the mapping of data across disparate systems.  Much of the data are pooled into backend tables with little to no structure.  There are many different nomenclatures and databases used for diagnoses, terminology, and medications.  Ensuring that discrete data points pulled from multiple sources match in a meaningful way when the patient data are linked together is a programmatic challenge.

Now that clinicians have the last thousand pulse measurements for a group of patients, what does one do with that?  Dashboards are useful for recent patient data, but how quickly it populates is critical for patient care. The rendering of this data requires stable wireless with significant bandwidth, processing power, and storage, all of which come with a cost, especially when privacy regulations must be met.

Likely the biggest challenge of all, and one often overlooked, is the human factor.  The average clinician does not know about technology; they know about patients.  The computer or barcode scanner is a tool to them just like an IV pump, glucometer, or chemistry analyzer.  If it does not work well for them consistently, in a timely and intuitive fashion, they will find ways around the system in order to care for their patients, not caring that it may compromise the data captured in the system.

Most people would point out that the last thousand measurements of anything is overkill for patient care, even if it were graphed to show a visual trend. There are some direct benefits of big data for the average clinician, such as being able to compare every recent vital sign, medication administration, and lab result on the fly.  That said, most of the benefit is indirect via health systems and health outcomes improvements.

The traditional paper method of auditing was to pull a certain number of random charts, often a small fraction of one percent of patient visits.  This gives an idea of whether certain data elements are being collected consistently, documentation completed, and quality goals met.  With big data and proper analytics, the ability exists to audit every single patient chart at any time.

The quality department may have reports and trending graphics to ensure their measures were met, not just for a percentage of a population, but each and every patient visit for as long as the data is stored.  This can be done by age, gender, level of care, and even by eye color, if that data is captured and the reports exist to pull it.

Researchers can use this data mining technique to develop new evidence to guide future care.  By reviewing the patients with the best outcomes in a particular group, correlations can be drawn, evaluated, and tested based on the data of a million patients.  Positive interventions discovered this way today can be turned into evidence-based practice tomorrow.

The sheer scope of big data is its own challenge, but the benefits have the potential to change healthcare in ways that have yet to be considered.  Big data comes from technology, but Meaningful Use is not about implementing technology.  It is about leveraging technology in a meaningful way to improve the care and outcomes of our patients.  This is why managing big data is so critical to the future of healthcare.

MedSys Group Consultant, Sarah E. Fletcher, BS, BSN, RN-BC has worked in technology for over fifteen years.  The last seven years have been within the nursing profession, beginning in critical care and transitioning quickly to Nursing Informatics.  She is a certified Nurse Informaticist and manages a regular Informatics Certification series for students seeking ANCC certification in Nursing Informatics.  Sarah currently works with MedSys Group Consulting supporting a multi-hospital system.

July 19, 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.

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. Contact her at @annezieger on Twitter.

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 Billian's HealthDATA and Porter Research. She is a regular contributor to a number of healthcare blogs and currently manages social marketing channels for the Health IT Leadership Summit and Technology Association of Georgia’s Health Society. You can find her on Twitter @JennDennard.

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 Billian's HealthDATA and Porter Research. She is a regular contributor to a number of healthcare blogs and currently manages social marketing channels for the Health IT Leadership Summit and Technology Association of Georgia’s Health Society. You can find her on Twitter @JennDennard.