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Correlations and Research Results: Do They Match Up? (Part 2 of 2)

Posted on May 27, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The previous part of this article described the benefits of big data analysis, along with some of the formal, inherent risks of using it. We’ll go even more into the problems of real-life use now.

More hidden bias

Jeffrey Skopek pointed out that correlations can perpetuate bias as much as they undermine it. Everything in data analysis is affected by bias, ranging from what we choose to examine and what data we collect to who participates, what tests we run, and how we interpret results.

The potential for seemingly objective data analysis to create (or at least perpetuate) discrimination on the basis of race and other criteria was highlighted recently by a Bloomberg article on Amazon Price deliveries. Nobody thinks that any Amazon.com manager anywhere said, “Let’s not deliver Amazon Prime packages to black neighborhoods.” But that was the natural outcome of depending on data about purchases, incomes, or whatever other data was crunched by the company to produce decisions about deliveries. (Amazon.com quickly promised to eliminate the disparity.)

At the conference, Sarah Malanga went over the comparable disparities and harms that big data can cause in health care. Think of all the ways modern researchers interact with potential subjects over mobile devices, and how much data is collected from such devices for data analytics. Such data is used to recruit subjects, to design studies, to check compliance with treatment, and for epidemiology and the new Precision Medicine movement.

In all the same ways that the old, the young, the poor, the rural, ethnic minorities, and women can be left out of commerce, they can be left out of health data as well–with even worse impacts on their lives. Malanga reeled out some statistics:

  • 20% of Americans don’t go on the Internet at all.

  • 57% of African-Americans don’t have Internet connections at home.

  • 70% of Americans over 65 don’t have a smart phone.

Those are just examples of ways that collecting data may miss important populations. Often, those populations are sicker than the people we reach with big data, so they need more help while receiving less.

The use of electronic health records, too, is still limited to certain populations in certain regions. Thus, some patients may take a lot of medications but not have “medication histories” available to research. Ameet Sarpatwari said that the exclusion of some populations from research make post-approval research even more important; there we can find correlations that were missed during trials.

A crucial source of well-balanced health data is the All Payer Claims Databases that 18 states have set up to collect data across the board. But a glitch in employment law, highlighted by Carmel Shachar, releases self-funding employers from sending their health data to the databases. This will most likely take a fix from Congress. Unless they do so, researchers and public health will lack the comprehensive data they need to improve health outcomes, and the 12 states that have started their own APCD projects may abandon them.

Other rectifications cited by Malanga include an NIH requirement for studies funded by it to include women and minorities–a requirement Malanga would like other funders to adopt–and the FCC’s Lifeline program, which helps more low-income people get phone and Internet connections.

A recent article at the popular TechCrunch technology site suggests that the inscrutability of big data analytics is intrinsic to artificial intelligence. We must understand where computers outstrip our intuitive ability to understand correlations.

Correlations and Research Results: Do They Match Up? (Part 1 of 2)

Posted on May 26, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Eight years ago, a widely discussed issue of WIRED Magazine proclaimed cockily that current methods of scientific inquiry, dating back to Galileo, were becoming obsolete in the age of big data. Running controlled experiments on limited samples just have too many limitations and take too long. Instead, we will take any data we have conveniently at hand–purchasing habits for consumers, cell phone records for everybody, Internet-of-Things data generated in the natural world–and run statistical methods over them to find correlations.

Correlations were spotlighted at the annual conference of the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. Although the speakers expressed a healthy respect for big data techniques, they pinpointed their limitations and affirmed the need for human intelligence in choosing what to research, as well as how to use the results.

Petrie-Flom annual 2016 conference

Petrie-Flom annual 2016 conference

A word from our administration

A new White House report also warns that “it is a mistake to assume [that big data techniques] are objective simply because they are data-driven.” The report highlights the risks of inherent discrimination in the use of big data, including:

  • Incomplete and incorrect data (particularly common in credit rating scores)

  • “Unintentional perpetuation and promotion of historical biases,”

  • Poorly designed algorithmic matches

  • “Personaliziaton and recommendation services that narrow instead of expand user options”

  • Assuming that correlation means causation

The report recommends “bias mitigation” (page 10) and “algorithmic systems accountability” (page 23) to overcome some of these distortions, and refers to a larger FTC report that lays out the legal terrain.

Like the WIRED articles mentioned earlier, this gives us some background for discussions of big data in health care.

Putting the promise of analytical research under the microscope

Conference speaker Tal Zarsky offered both fulsome praise and specific cautions regarding correlations. As the WIRED Magazine issue suggested, modern big data analysis can find new correlations between genetics, disease, cures, and side effects. The analysis can find them much cheaper and faster than randomized clinical trials. This can lead to more cures, and has the other salutory effect of opening a way for small, minimally funded start-up companies to enter health care. Jeffrey Senger even suggested that, if analytics such as those used by IBM Watson are good enough, doing diagnoses without them may constitute malpractice.

W. Nicholson Price, II focused on the danger of the FDA placing too many strict limits on the use of big data for developing drugs and other treatments. Instead of making data analysts back up everything with expensive, time-consuming clinical trials, he suggested that the FDA could set up models for the proper use of analytics and check that tools and practices meet requirements.

One of exciting impacts of correlations is that they bypass our assumptions and can uncover associations we never would have expected. The poster child for this effect is the notorious beer-and-diapers connection found by one retailer. This story has many nuances that tend to get lost in the retelling, but perhaps the most important point to note is that a retailer can depend on a correlation without having to ascertain the cause. In health, we feel much more comfortable knowing the cause of the correlation. Price called this aspect of big data search “black box” medicine.” Saying that something works, without knowing why, raises a whole list of ethical concerns.

A correlation stomach pain and disease can’t tell us whether the stomach pain led to the disease, the disease caused the stomach pain, or both are symptoms of a third underlying condition. Causation can make a big difference in health care. It can warn us to avoid a treatment that works 90% of the time (we’d like to know who the other 10% of patients are before they get a treatment that fails). It can help uncover side effects and other long-term effects–and perhaps valuable off-label uses as well.

Zarsky laid out several reasons why a correlation might be wrong.

  • It may reflect errors in the collected data. Good statisticians control for error through techniques such as discarding outliers, but if the original data contains enough apples, the barrel will go rotten.

  • Even if the correlation is accurate for the collected data, it may not be accurate in the larger population. The correlation could be a fluke, or the statistical sample could be unrepresentative of the larger world.

Zarsky suggests using correlations as a starting point for research, but backing them up by further randomized trials or by mathematical proofs that the correlation is correct.

Isaac Kohane described, from the clinical side, some of the pros and cons of using big data. For instance, data collection helps us see that choosing a gender for intersex patients right after birth produces a huge amount of misery, because the doctor guesses wrong half the time. However, he also cited times when data collection can be confusing for the reasons listed by Zarsky and others.

Senger pointed out that after drugs and medical devices are released into the field, data collected on patients can teach developers more about risks and benefits. But this also runs into the classic risks of big data. For instance, if a patient dies, did the drug or device contribute to death? Or did he just succumb to other causes?

We already have enough to make us puzzle over whether we can use big data at all–but there’s still more, as the next part of this article will describe.

Patients and Their Medical Data

Posted on April 4, 2016 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

Sometimes they say a picture is worth a thousand words. That’s what I thought when I saw this image from Nature.com:
Patient Health Data Sharing and Big Healthcare Data

It’s great to see Nature.com talking about healthcare data. The authors are two people you likely know: Leonard Kish and Eric Topol.

This graphic shows the ideal. It’s interesting to think about what the reality would actually look like. Sadly, it would be much more complex, disconnected, and would lack the fluid sharing that this graphic shows.

It’s good to know what the idea for data sharing and understanding data would look like. Shows the potential of what’s possible and that’s exciting.

How Much Patient Data Do We Truly Need?

Posted on November 23, 2015 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 @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

As the demands placed on healthcare data increase, the drive to manage it effectively has of course grown as well. This has led to the collection of mammoth quantities of data — one trade group estimates that U.S. hospitals will manage 665 terabytes of data during 2015 alone — but not necessarily better information.

The assumption that we need to capture most, if not all, of a patient’s care history digitally is clearly driving this data accumulation process. As care moves into the digital realm, the volume of data generated by the healthcare industry is climbing 48% percent per year, according to one estimate. I can only assume that the rate of increase will grow as providers incorporate data feeds from mHealth apps, remote monitoring devices and wearables, the integration of which is not far in the future.

The thing is, most of the healthcare big data discussions I’ve followed assume that providers must manage, winnow and leverage all of this data. Few, if any, influencers seem to be considering the possibility that we need to set limits on what we manage, much less developing criteria for screening out needless data points.

As we all know, all data is not made equal.  One conversation I had with a physician in the back in the early 1990s makes the point perfectly. At the time, I asked him whether he felt it would be helpful to put a patient’s entire medical history online someday, a distant but still imaginable possibility at the time. “I don’t know what we should keep,” he said. “But I know I don’t need to know what a patient’s temperature was 20 years ago.”

On the other hand, providers may not have access to all of the data they need either. According to research by EMC, while healthcare organizations typically import 3 years of legacy data into a new EMR, many other pertinent records are not available. Given the persistence of paper, poor integration of clinical systems and other challenges, only 25% of relevant data may be readily available, the vendor reports.

Because this problem (arguably) gets too little attention, providers grappling with it are being forced to to set their own standards. Should hospitals and clinics expand that three years of legacy data integration to five years? 10 years? The patient’s entire lifetime? And how should institutions make such a decision? To my knowledge, there’s still no clear-cut way to make such decisions.

But developing best practices for data integration is critical. Given the costs of managing needless patient data — which may include sub-optimal outcomes due to data fog — it’s critical to develop some guidelines for setting limits on clinical data accumulation. While failing to collect relevant patient data has consequences, turning big data into astronomically big data does as well.

By all means, let’s keep our eye on how to leverage new patient-centric data sources like wearable health  trackers. It seems clear that such data has a role in stepping up patient care, at least once we understand what part of it is wheat and which part chaff.

That being said, continuing to amass data at exponential rates is unsustainable and ultimately, harmful. Sometimes, setting limits is the only way that you can be sure that what remains is valuable.

Are Researchers Ready to Use Patient Health Records?

Posted on October 20, 2014 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

There’s a groundswell of opinion throughout health care that to improve outcomes, we need to share clinical data from patients’ health records with researchers who are working on cures or just better population health measures. One recommendation in the much-studied JASON report–an object of scrutiny at the Office of the National Coordinator and throughout the field of health IT–called on the ONC to convene a conference of biomedical researchers.

At this conference, presumably, the health care industry will find out what researchers could accomplish once they had access to patient data and how EHRs would have to change to meet researchers’ needs. I decided to contact some researchers in medicine and ask them these very questions–along with the equally critical question of how research itself would have to evolve to make use of the new flood of data.
Read more..

Big Data is Like Teenage Sex

Posted on November 5, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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.

Healthcare Big Data and Meaningful Use Challenges Video

Posted on October 2, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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.

Big Data Impacting Healthcare

Posted on July 19, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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.

Is Skinny Data Harder Than Big Data?

Posted on May 24, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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.

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

Posted on April 15, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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.