Free EMR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to EMR and HIPAA for FREE!!

Health Plans Need Big Data Smarts To Prove Their Value

Posted on November 2, 2016 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Recently, Aetna cut a deal which suggests a new role for health insurers in big data analytics and population health management. In partnership with Merck, the health insurer is launching a new program using predictive analytics to identify target populations and provide them with health and wellness services. AetnaCare will start by targeting patients with diabetes and hypertension in the mid-Atlantic U.S., but it seems likely to go national soon.

In its press release on the matter, Aetna says the goal of the program is to “proactively curate various health and wellness services… to support treatment adherence, ensure that critical social support needs are met, and reinforce healthy lifestyle behaviors.” That in and of itself isn’t a big deal. We all know that these are goals shared by providers, employers and health plans, and that most of the efforts health plans make on this front are pie in the sky, half-baked initiatives featuring cutesy graphics and little substance.

But then, Aetna’s chief medical officer gives away the real goal here — to power this effort by analyzing patient data being spun out by patients in varied care settings.  In the release, Dr. Harold Paz notes that patients are getting care in a wide variety of settings, including retail clinics, healthcare devices, pharmaceutical services, behavioral health, and social services, and that these services are seldom coordinated well, and implies that this is the real problem Aetna must solve.

If you listen to this with the ears of a health IT chick like myself, you hear Aetna (and Merck, actually) admitting that they must engage in predictive analytics across all of these encounters – and eventually, use these insights to help patients make good healthcare choices. In other words, they have to think like providers and even offer provider-like services fulfill their mission. And that means competing with or even beating providers at the big data game.

The truth is, health plans are in the same boat as providers, in that they’re at the center of a hailstorm of data and struggling with how to make use of it. Also, like providers they’re facing pressure from health purchasers to slow healthcare cost growth and boost patient wellness. But I’d argue that they’re even less prepared, technically and culturally, to improve health or coordinate care. So jumping in now is critically important.

In fact, I’d argue that health insurers are under greater pressure to improve population health than even sophisticated health systems or ACOs. Why? Because while health systems and ACOs can point to what they do – they make people better, for heaven’s sake — insurance companies are the eternal middleman who must continue to prove that they add value to the healthcare equation.

It remains to be seen whether programs like AetnaCare succeed at helping patients find the resources they need to improve and maintain their health. But even if this concept doesn’t work out, others will follow. Health plans need to leverage their unique data set to boost quality and reduce costs. Otherwise, as providers learn to work under value-based payments and accept risk, employers will have increasingly good reasons to contract directly — and leave the insurance industry out of the game entirely.

Envisioning the Future of Personalized Healthcare – Predictive Analytics – Breakaway Thinking

Posted on December 16, 2015 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 blog post by Jennifer Bergeron, Learning and Development Manager at The Breakaway Group (A Xerox Company). Check out all of the blog posts in the Breakaway Thinking series.
Jennifer Bergeron
As 2016 approaches, individuals and organizations are beginning to consider their New Year’s resolutions. In order to make a plan for change, we imagine ways we might reach our goals: “If I eat more vegetables, I’ll lower my cholesterol and have more energy. But if I eat more vegetables and skip the donuts, I will see the same improvements faster!” What if someone could tell us exactly what action or combination of actions would produce which results over a specific timeframe?

In healthcare, predictive analytics is doing just that – providing potential outcomes based on specific factors. The process involves more than gathering statistics that define group results, but research of patient outcomes that allows predictions for individuals. Both technology and statistics are used to sift through these results and turn them into meaningful insights. Considering big data and a patient’s own health information, diagnoses can be more accurate, patient outcomes improved, and readmission rates reduced.

Predictive analytics is being used to help improve patient safety, predict crises in the ICU, uncover hereditary diseases, and reveal correlations between diseases. Researchers at the University of California Davis are using electronic health record (EHR) data to create an algorithm to warn providers about sepsis. Genomic tests, an example of precision medicine, are now available for at-home DNA testing, which allows individuals to discover hereditary traits through genetic sequencing. Correlations can be found between illnesses using EHR data. Thirty thousand Type 2 diabetic patients were studied to predict the risk of dementia.

BMC Medical Informatics & Decision Making reported on the use of EHRs as a prediction tool for readmission or death among adult patients. The model was built using specific criteria: candidate risk factors had to be available in the EHR system at each hospital, were routinely collected and available within 24 hours, and were predictors of adverse outcomes.

But predictive analytics can only be as good as the data it uses. Accurate, relevant data is necessary in order to receive valuable information from the algorithms. But the information can be hard to find, considering that healthcare data is expected to grow from 500 to 25,000 petabytes between 2012 and 2020 (A petabyte is a million billion bytes). In an effort to solve this challenge, more than $1.9 billion of capital has been raised since 2011 to fund companies that can gather, process, and interpret the increasing amount of information.

There are four principles to follow in order to optimize how information is captured, stored, and managed in the EHR system:

  • Ensure that leadership delivers the message to the organization about the importance and future impacts of the EHR system
  • Quickly bring staff up to speed
  • Measure and track the results of the staff’s learning
  • Continue to support and invest in EHR adoption.

The EHR stands as the first point of collection of much of this data. Given the importance of accuracy and consistency, it is critical that EHR education and use is made a priority in healthcare.

Xerox is a sponsor of the Breakaway Thinking series of blog posts. The Breakaway Group is a leader in EHR and Health IT training.

We Share Health Data with Marketing Companies, Why Not with Healthcare Providers? Answer: $$

Posted on November 20, 2015 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.

For those who don’t realize it, your health data is being shared all over the place. Yes, we like to think that our health care data is being stored and protected and that laws like HIPAA keep them safe, but there are plenty of ways to legally share health care data today. In fact, many EHR vendors sell your health care data for a pretty penny.

Of course, many would argue that it’s shared in a way that complies with all the laws and that it’s done in a way that your health record isn’t individually identified. They’re only sharing your health data in a de-identified manner. Others would argue that you can’t deidentify the health data and that there are ways to reidentify the data. I’ll leave those arguments for another post. We’ll also leave the argument over whether all this sharing of health data (usually to marketing, pharma and insurance companies) is safe or not for a future post as well.

What’s undeniable is that health data for pretty much all of us is being bought and sold all over health care. If you don’t believe it’s so, take a minute to look at the work of Deborah Peel from Patient Privacy Rights and learn about her project theDataMap. She’ll be happy to inform you of all the ways data is currently being bought and sold. It’s a really big business.

Here’s where the irony comes in. We have no trouble sharing health data (Yes, even EHR vendors have no problem sharing data and lets be clear that not all EHR vendors share data with these outside companies but mare are sharing data) with marketing companies, payers and pharma companies that are willing to pay for access to that data. Yet, when we ask EHR vendors to share health data with other EHR vendors or with an HIE, they balk at the idea as if it’s impossible. They follow that up with a bunch of lame excuses about HIPAA privacy or the complexity of health care data.

Let’s call a spade a spade. We could pretty easily be interoperable in health care if we wanted to be interoperable. We know that’s true because when the money is there from these third party companies, EHR vendors can share data with them. The problem has been that the money has never been there before for EHR vendors to be motivated enough to make interoperability between EHR vendors possible. In fact, you could easily argue that the money was instructing EHR vendors not to be interoperable.

However, times are changing. Certainly the government pressure to be interoperable is out there, but that doesn’t really motivate the industry if there’s not some financial teeth behind it. Luckily the financial teeth are starting to appear in the form of value based reimbursement and the move away from fee for service. That and other trends are pushing healthcare providers to want interoperable health records as an important part of their business. That’s a far cry from where interoperability was seen as bad for their business.

I heard about this shift first hand recently when I was talking with Micky Tripathi, President & CEO of the Massachusetts eHealth Collaborative. Micky told me that his organization had recently run a few RFPs for healthcare organizations searching for an EHR. As part of the EHR selection process Micky recounted that interoperability of health records was not only included in the RFP, but was one of the deciding factors in the healthcare organizations’ EHR selections. The same thing would have never been said even 3-5 years ago.

No doubt interoperability of health records has a long way to go, but there are signs that times are changing. The economics are starting to make sense for organizations to embrace interoperablity. That’s a great thing since we know they can do it once the right economic motivations are present.

Eyes Wide Shut – Making the Most of Meaningful Use, for Healthcare Providers, Insurers, and Patients

Posted on July 21, 2015 I Written By

Mandi Bishop is a hardcore health data geek with a Master's in English and a passion for big data analytics, which she brings to her role as Dell Health’s Analytics Solutions Lead. She fell in love with her PCjr at 9 when she learned to program in BASIC. Individual accountability zealot, patient engagement advocate, innovation lover and ceaseless dreamer. Relentless in pursuit of answers to the question: "How do we GET there from here?" More byte-sized commentary on Twitter: @MandiBPro.

When I ask a room of 100 health plan leaders, “how many of you know what HL7 is,” and only a third raise their hands, I realize there had been a “Meaningful Use” for my recent travels through the healthcare provider system and its maze of regulatory and payer mandates. I navigated change management hell in order to inform my future endeavors. I came out on the other side of an attestation nightmare with the knowledge to educate others who are embarking on extensions of that journey. This “Eyes Wide Shut” series has come full-circle.

For those who have followed this series, a quick update on the fate of the IDN highlighted throughout earlier posts: they have not yet successfully attested to all Meaningful Use Stage 2 measures across all the inpatient facilities and ambulatory practices. However, the continuing changes to attestation criteria (specifically, the engagement measures that caused so much consternation) may allow them to squeak in under the wire in fiscal year 2016 before penalties kick in. Although I’m no longer directly involved in the IDN’s pursuit of multi-EMR integration excellence, I am a “beneficiary” of those encounter data normalization efforts, as I am back to working with payer clients who are leveraging this clinically-integrated network. And I’m still having to adjust for inconsistencies in identity management rules, coding practices, and clinical workflow differences across each of the offices (and departments within offices), as I integrate their information with the insurer’s data ecosystem.

I began this series on my (woefully neglected) personal blog, almost 2 years ago: Eyes Wide Shut: Seeing the Dark Side of Health IT Mandates and Meaningful Use. Coming from the health insurance world, I had no idea of the magnitude of healthcare provider process impacts resulting from regulatory and payer demands (nee, mandates). I was insensitive to the plight of the independent general practitioner, and the size of the budget required to implement a certified EMR, let alone populate it with any patient history or integrate it with existing scheduling or billing processes. I didn’t realize that my request for chart data to support HEDIS measures would involve more work than simply clicking an indicator in an EMR configuration screen to suddenly send me my heart’s desire of data elements. I would never have believed that certified software would not be tested for conformance to code-level specifications (only visual output tests).

To all my clinician and provider office-worker friends: I am sorry for all the ways in which this ignorance may have contributed to the new reality forced on practitioners of medicine to also be data-entry clerks/contract lawyers/IT experts. Personally, I want my doctor to be my doctor. So, I’m dedicating the next leg of my career journey aligning all healthcare system actors to what should always have been our higher purpose: contributing positively to the health and well-being of the individuals and populations we serve.

When I initially began writing this post, I thought I’d be using it to end the series.

Instead, I’m just embarking on a new chapter: the post-provider world of healthcare actor convergence.