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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.

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.

Best Doctor at the Lowest Cost

We have a real challenge in healthcare that won’t be easy to solve. In fact, we may not solve this problem. The challenge is knowing the quality of care that’s being provided by a doctor. This matters for so many things. Ideally we could base reimbursement on the quality of the care as opposed to the volume of care. If we had a good measurement for quality of care, none of us would go to doctors who didn’t provide a high quality of care.

Think about how it currently works. If a doctor’s costs or outcomes compare unfavorably with their colleagues, most of us will say, ‘That doctor’s patients are sicker than other doctors’ patients.” In many cases, that very well could be the case. I remember a similar discussion in the clinic I worked at where one doctor was always given the really complex patients, but then they all wondered why he was always running behind.

The problem is that we don’t have any really good ways to know if someones costs and outcomes are off because they have sicker patients or because they aren’t very good doctors. Plus, this doesn’t even really take into account the long term implications of the care that’s provided by a doctor. Maybe the up front cost was more, but the long term cost to the healthcare system and patient might end up being much less.

Like I said, we may never solve these problems because they are incredibly complex. I know that many people would look to big data to help solve this challenge. Big Data can do great things, but far too often it’s the cop out answer to really addressing the challenge. This is especially true because then it usually leads to us not having the data available for us to really solve the problem.

Even most doctors can’t judge the quality of care that another doctor provides. If it’s a doctor from their specialty that they work with on a regular basis, then they likely have some idea. However, except in really complex patients (which most aren’t), the interaction between doctors is pretty minimal. This isn’t a knock on doctors. It’s just the reality that if a doctor doesn’t have much interaction with another doctor, what basis do they have to know the quality of care another doctor provides?

All of the various doctor ranking systems miss this completely. Most users of those systems mistakenly assume that the ranking or ratings on those sites somehow reflect the quality of the doctor. As discussed above, there’s no way for these sites to assess the quality of the doctor. Instead, these websites rank and rate based solely upon customer service and not quality of care. Customer service can be an important factor in selecting a doctor, but quality of care measures would be infinitely more valuable.

How then do we measure the quality of care provided? I haven’t even mentioned the complexities around consistency of care. Some of my blog posts are better than others. The care provided by a doctor to one patient might be great, and the next patient only good. Plus, this also doesn’t take into account the quality of the patient. What if the patient withholds information which prevents the doctor from providing really quality care? Should we hold doctors responsible for the poor care they provide because of the patient’s choices?

This is a really slippery slope to start, but I’ve heard people talking about it. I’m sure it makes doctors cringe to even think about it. I don’t expect my doctor to be perfect, but I think it is good for doctors to be accountable. That’s just a really hard thing to do.

April 3, 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.

ACO Tire Change Analogy

I was at an ACO conference a while back and one of the speakers compared the idea of ACOs to a tire change. Although, he suggested in an ACO world, you’d get your tire changed and then a mile down the road the tire goes flat and the tire company will say they couldn’t predict that to happen.

It’s an interesting comparison to consider. I know many doctors are concerned with ACOs for situations like the one described. This is particularly true because they only have so much control over the health of a patient. Using the car analogy, they don’t know if the person is going to go off roading with their car (risky behavior), run over a nail (get in an accident), or slash the tire themselves (smoking or other unhealthy behavior). Yet, in an ACO world, the doctor is held accountable for all of these things.

I don’t pretend to be the foremost authority on ACOs. I’m still learning (and so is everyone at this point). However, there are some real challenges associated with reimbursing based on improving the health of a patient so they don’t return to the office.

Certainly technology can play a major role in making this happen. In fact, without technology this is a really hard thing to do. Mobile devices can help patients be more accountable for the choices they make. They can help a doctor influence healthy behavior in ways that weren’t possible before.

Big data can help a healthcare organization know which patient populations need the most attention to be able to increase the overall health of a population. Plus, this is only going to get more powerful as patients start tracking their health data more and more and healthcare can address those who have the most need before they even know they need it.

I like the direction that we’re headed in healthcare where we try and reimburse for the right things, but it’s going to be a really long, hard road. In fact, as I look into the future of ACOs I don’t really see a road at all. Instead, I see the ACO movement as trailblazing its way to an unknown future.

March 25, 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.

Wireless Healthcare IT Could Hold the Key to Preventable Readmissions

As I mentioned in my last blog post, CardioMEMS was the winner of this year’s Intel Innovation Award, presented at the Health IT Leadership Summit earlier this month. CardioMEMS has a number of development firsts to its credit, bolstering its recent claim to innovation fame:

  • First wireless communication system for the human body
  • First medical implant completely wafer fabricated
  • Only FDA-approved, permanently implanted wireless sensor

Essentially, the company has developed a first-of-its kind wireless (and battery-less) heart failure monitoring system. As Richard Powers, Vice President of Information Systems, explained to me on my field trip to CardioMEMS’ relatively new offices in Atlanta, the company has figured out a way to, in the least traumatic way possible, implant a cardiac sensor that monitors pressure and wirelessly transmits that data directly to a patient’s physician via a Web-based portal.

When I first came across the company nearly two years ago, the term “Big Data” hadn’t quite gained the buzzy reputation it has now, so I feel confident in saying that CardioMEMS’ analytics team were a bit ahead of the game – not surprising, given that the company was founded by Dr. Jay S. Yadav, its current CEO and still a consulting cardiologist.

In talking with Yadav, I realized he and his colleagues recognize not only the importance of back-end data, but also the value of simplicity.  As Powers pointed out, the sophisticated technology isn’t in the device itself, but comes after on the receiving end. Ideally, physicians will use data transmitted from the sensor to gauge cardiac pressure changes and adjust medication accordingly.

The timing of this technology couldn’t be better, in my opinion, since so much attention is being paid to preventing readmissions, increasing quality outcomes and improving patient satisfaction scores. Benefits of the sensor in clinical trials include fewer hospitalizations, lower cost of care and an increase in quality of life. And I do believe the CardioMEMS team has even figured out the reimbursement angle with CMS, which should make provider adoption of the devices that much more likely.

Pending FDA approval is the only thing holding up a full-court product marketing press, which may, when that approval comes, be aided by partnership with a select provider organization.

I couldn’t leave the CardioMEMS offices, of course, without asking about its plans to integrate into an EMR. According to Powers, integration of the physician portal into an EMR is in fact on the drawing board yet. They are also looking at ways to pull a patient’s EMR data into the CardioMEMS portal. The company is currently working with the Enterprise Innovation Institute at Georgia Tech to look into EMR interoperability.

I’m confident we’ll be seeing some really interesting developments from this company in the near future.

December 12, 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.