Healthcare Big Data | EMR and EHR

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.

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.

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.

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.

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.

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.

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Is Skinny Data Harder Than Big Data?

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

Best Doctor at the Lowest Cost

ACO Tire Change Analogy

Big Data Analytics vs Focused Patient Analytics

Wireless Healthcare IT Could Hold the Key to Preventable Readmissions

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