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Searching for Disruptive Healthcare Innovation in 2017

Posted on January 17, 2017 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin is a true believer in #HealthIT, social media and empowered patients. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He currently leads the marketing efforts for @PatientPrompt, a Stericycle product. Colin’s Twitter handle is: @Colin_Hung

Disruptive Innovation has been the brass ring for technology companies ever since Clayton Christensen popularized the term in his seminal book The Innovator’s Dilemma in 1997. According to Christensen, disruptive innovation is:

“A process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors.”

Disruption is more likely to occur, therefore, when you have a well established market with slow-moving large incumbents who are focused on incremental improvements rather than truly innovative offerings. Using this definition, healthcare has been ripe for innovation for a number of years. But where is the AirBNB/Uber/Google of healthcare?

On a recent #hcldr tweetchat we asked what disruptive healthcare technologies might emerge in 2017. By far the most popular response was Artificial Intelligence (AI) and Machine Learning.

Personally, I’m really excited about the potential of AI applied to diagnostics and decision support. There is just no way a single person can stay up to speed on all the latest clinical research while simultaneously remembering every symptom/diagnosis from the past. I believe that one day we will all be using AI assistance to guide our care – as common as we use a GPS today to help navigate unknown roads.

Some #hcldr participants, however, were skeptical of AI.

While I don’t think @IBMWatson is on the same trajectory as Theranos, there is merit to being wary of “over-hype” when it comes to new technologies. When a shining star like Theranos falls, it can set an entire industry back and stifle innovation in an area that may warrant investment. Can you imagine seeking funding for a technology that uses small amounts of blood to detect diseases right now? Too much hype can prematurely kill innovation.

Other potentially disruptive technologies that were raised during the chat included: #telehealth, #wearables, patient generated health data (#PDHD), combining #HealthIT with consumer services and #patientengagement.

The funniest and perhaps most thoughtful tweet came from @YinkaVidal, who warned us that innovations have a window of usefulness. What was once ground-breaking can be rendered junk by the next generation.

What do you believe will be the disruptive healthcare technology to emerge in 2017?

Rumor Control: These are the Facts

Posted on January 16, 2017 I Written By

For the past twenty years, I have been working with healthcare organizations to implement technologies and improve business processes. During that time, I have had the opportunity to lead major transformation initiatives including implementation of EHR and ERP systems as well as design and build of shared service centers. I have worked with many of the largest healthcare providers in the United States as well as many academic and children's hospitals. In this blog, I will be discussing my experiences and ideas and encourage everyone to share your own as well in the comments.

Why is it that one of the largest challenges on any project is miscommunication and out of control rumors? While many projects need and would benefit from more communication, even with the best of communication plans, project teams can spend more time dispelling false information than proactively communicating.

I believe in strong communication plans for EHR and ERP projects that include a wide range of communication including town halls, newsletters, emails, signage, internet sites, and other methods of sharing correct information. But on every project, no matter how much we communicate, certain hospital staff will find other sources of information.

I can see the rumor coming when an email or conversation starts with “I heard that…” or “Is it true that…”. These are telltale signs that I am about to hear a rumor. Rumors can range from minor details to far-reaching implications such as a perceived change in project scope or even the live date. While most rumors are just annoyances, responding to them and controlling them can be a significant strain on the project team’s time.

I believe that hospitals have a unique challenge in that proactive communication is more challenging than in many traditional businesses because it is common for a large portion of the staff, including nurses and physicians, to rarely check email. As a result, they are often in a position where “hallway conversation” is how they first hear information and are more likely to give it credibility.

While I admit that I have personally never been able to fully eliminate the rumor challenge, I’d like to share several ideas about what I have seen as an effective way to keep the rumor mill under control:

1) Establish a clear Source of Truth – From the very beginning of the project, communicate to every possible audience how decision and communications will be distributed and who they should contact with questions and information. If it doesn’t come from one of the accepted Sources of Truth, its not true. When I lead a project, I prefer to be the Source of Truth – if it doesn’t come from me verbally or in writing, it isn’t true.

2) Encourage questions and respond to all of them timely – When I am running a project, my motto is “Ask me anything, anytime”. At times, I will get dozens or even hundreds of questions a day through meetings, phone calls, texts, and emails. I respond to every question, providing the truth if I have it, or getting them to the person who can provide the truth. Rumors often start because staff members are not getting answers or don’t feel their questions are welcomed. How do I respond to so many requests? I do it immediately so they can’t accumulate – which also helps inspire confidence and a feeling that they can ask rather than assume.

3) Town Halls – I strongly believe that a change management and communication strategy must include town halls. During town halls, project teams should provide an overview of what is occurring that is relevant to the staff, do occasional software demonstrations, and most importantly – field questions. Creating those proactive communication channels is a powerful way to avoid people creating their own truths.

4) Provide the complete truth – Sometimes the answer to a question is not known because it has not been determined, or has not been considered. Sometimes it is not what the person wants to hear. Regardless, provide the truth – and the complete truth. There is nothing wrong with saying that you don’t know – but can find out. Or that a decision has not been made, but now that they have raised the concern we will make it and get back to them. Responding immediately doesn’t always mean providing an answer immediately, as long as the follow-up is done once the answer is available.

5) Communicate Everywhere – A communication plan must be extensive and include many different points of contact. Intranet sites can look impressive and have lots of great information on them – but usually only a small percentage of the staff will check them. Consideration must be given as to how to communicate with contracted employees, physicians, and traveling nurses. This is particularly challenging during an EHR roll-out when all of these parties must be enrolled in training classes and kept up-to-date on the go-live. Find and use every possible communication challenge. There are always questions about how much communication is too much – but they apply to the volume of communication you push through a particular communication channel – not the number of different communication channels you use.

Finally, accept that no matter what you do, rumors will form and will need to be dispelled. Its part of project management and change management that always had existed, and always will. Properly controlled, the rumors can be a minor distraction at worst – entertainment at best.

Please share any ideas you have found to be successful in keeping rumors under control.

If you’d like to receive future posts by Brian in your inbox, you can subscribe to future Healthcare Optimization Scene posts here. Be sure to also read the archive of previous Healthcare Optimization Scene posts.

Healthcare’s Not Good At Mining Health Data

Posted on January 13, 2017 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.

I was really blown away by this quote from an interview with Rebecca Quammen.

The buzz around data analytics promotes the need for data scientists and data analysts as among the most sought-after roles, and that is problematic in and of itself. It’s creating a huge demand, but it’s also a demand that many healthcare organizations don’t know how to deal with right now. I see the buzz around data analytics increasing the pressure to “do something” with data, but many organizations across the nation, both large and small and in every setting of care, simply don’t have the foundational knowledge to manage the data to their benefit, and to know the database structure and how to get it the data out and what the data tells them when they get it. We are not an industry historically good at mining good, rich data out of products and doing something meaningful with it. We do traditional reporting and we may do a little bit of historical reporting, but we’re not good at looking at data to predict and promote and to work toward the future, or to see trends and do analysis across the organization.

Rebecca nailed this one on the head. I’ve seen a bunch of organizations go running towards healthcare informatics with no idea of what they wanted to accomplish or any sort of methodology for how they’re going to analyze the data to find useful insights. It kind of reminds me of the herd mentality that happens at conferences. If any sort of crowd starts to build at a conference, then the crowd quickly grows exponentially as people think that something interesting must be going on. The same seems to happen as healthcare organizations have run towards data analytics.

While I think there’s so much potential in health data analytics, I think that most organizations are afraid to fail. The culture in healthcare is “do no harm.” There are some very good reasons for this and some real fears when it comes to medical liability. There’s a lot more at stake when using data in healthcare than say Netflix trying to predict which shows you might be interested in watching. If Netflix gets it wrong, you just keep scrolling after some minor frustration which you quickly forget. In healthcare, if we get it wrong, people can die or be harmed in some major way.

I understand why this healthcare culture exists, but I also think that inactivity is killing as many or more people than would be damaged by our data mistakes. It’s a challenging balance. However, it’s a balance that we must figure out. We need to enable more innovation and thoughtful experimentation into how we can better use health data. Yes, I’m talking beyond the traditional reporting and historical reporting which doesn’t move the needle on care. I’m talking using data to really impact care. That’s a brave place to be, but I applaud all of those brave people who are exploring this new world.

“Learning Health System” Pilot Cuts Care Costs While Improving Quality

Posted on January 11, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

As some of you will know, the ONC’s Shared Nationwide Interoperability Roadmap’s goal is to create a “nationwide learning health system.”  In this system, individuals, providers and organizations will freely share health information, but more importantly, will share that information in “closed loops” which allow for continuous learning and care improvement.

When I read about this model – which is backed by the Institute of Medicine — I thought it sounded interesting, but didn’t think it terribly practical. Recently, though, I stumbled upon an experiment which attempts to bring this approach to life. And it’s more than just unusual — it seems to be successful.

What I’m talking about is a pilot study, done by a team from Nationwide Children’s Hospital and The Ohio State University, which involved implementing a “local” learning health system. During the pilot, team members used EHR data to create personalized treatments for patients based on data from others with similar conditions and risk factors.

To date, building a learning health system has been very difficult indeed, largely because integrating EHRs between multiple hospital systems is very difficult. For that reason, researchers with the two organizations decided to implement a “local” learning health system, according to a press statement from Nationwide Children’s.

To build the local learning health system, the team from Nationwide Children’s and Ohio State optimized the EHR to support their efforts. They also relied on a “robust” care coordination system which sat at the core of the EHR. The pilot subjects were a group of 131 children treated through the hospital’s cerebral palsy program.

Children treated in the 12-month program, named “Learn From Every Patient,” experienced a 43% reduction in total inpatient days, a 27% reduction in inpatient admissions, a 30% reduction in emergency department visits and a 29% reduction in urgent care visits.

The two institutions spent $225,000 to implement the pilot during the first year. However, the return on this investment was dramatic.  Researchers concluded that the program cut healthcare costs by $1.36 million. This represented a savings of about $6 for each dollar invested.

An added benefit from the program was that the clinicians working in the CP clinic found that this approach to care simplified documentation, which saved time and made it possible for them to see more patients during each session, the team found.

Not surprisingly, the research team thinks this approach has a lot of potential. “This method has the potential to be an effective complementary or alternative strategy to the top-down approach of learning health systems,” the release said. In other words, maybe bottom-up, incremental efforts are worth a try.

Given these results, it’d be nice to think that we’ll have full interoperability someday, and that we’ll be able to scale up the learning health system approach to the whole US. In the mean time, it’s good to see at least a single health system make some headway with it.

Some Projections For 2017 Hospital IT Spending

Posted on January 4, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A couple of months ago, HIMSS released some statistics from its survey on US hospitals’ plans for IT investment over the next 12 months. The results contain a couple of data points that I found particularly interesting:

  • While I had expected the most common type of planned spending to be focused on population health or related solutions, HIMSS found that pharmacy was the most active category. In fact, 51% of hospitals were planning to invest in one pharmacy technology, largely to improve tracking of medication dispensing in additional patient care environments. Researchers also found that 6% of hospitals were planning to add carousels or packagers in their pharmacies.
  • Eight percent hospitals said that they plan to invest in EMR components, which I hadn’t anticipated (though it makes sense in retrospect). HIMSS reported that 14% of hospitals at Stage 1-4 of its Electronic Medical Record Adoption Model are investing in pharmacy tech for closed loop med administration, and 17% in auto ID tech. Four percent of Stage 6 hospitals plan to support or expand information exchange capabilities. Meanwhile, 60% of Stage 7 hospitals are investing in hardware infrastructure “for the post-EMR world.”

Other data from the HIMSS report included news of new analytics and telecom plans:

  • Researchers say that recent mergers and acquisitions are triggering new investments around telephony. They found that 12% of hospitals with inpatient revenues between $25 million and $125 million – and 6% of hospitals with more than $500 million in inpatient revenues — are investing in VOIP and telemedicine. FWIW, I’m not sure how mergers and acquisitions would trigger telemedicine rollouts, as they’re already well underway at many hospitals — maybe these deals foster new thinking and innovation?
  • As readers know, hospitals are increasingly spending on analytics solutions to improve care and make use of big data. However (and this surprised me) only 8% of hospitals reported plans to buy at least one analytics technology. My guess is that this number is small because a) hospitals may not have collected their big data assets in easily-analyzed form yet and b) that they’re still hoping to make better use of their legacy analytics tools.

Looking at these stats as a whole, I get the sense that the hospitals surveyed are expecting to play catch-up and shore up their infrastructure next year, rather than sink big dollars into future-looking solutions.

Without a doubt, hospital leaders are likely to invest in game-changing technologies soon such as cutting-edge patient engagement and population health platforms to prepare for the shift to value-based health. It’s inevitable.

But in the meantime it probably makes sense for them to focus on internal cost drivers like pharmacy departments, whose average annual inpatient drug spending shot up by more than 23% between 2013 and 2015. Without stanching that kind of bleeding, hospitals are unlikely to get as much value as they’d like from big-idea investments in the future.

A Look At Geisinger’s Big Data Efforts

Posted on December 28, 2016 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

This week I got a look at a story appearing in a recent issue of Harvard Business Review which offers a description of Geisinger Health System’s recent big data initiatives. The ambitious project is designed not only to track and analyze patient outcomes, but also to visualize healthcare data across cohorts of patients and networks of providers and even correlate genomic sequences with clinical care. Particularly given that Geisinger has stayed on the cutting edge of HIT for many years, I think it’s worth a look.

As the article’s authors note, Geisinger rolled out a full-featured EMR in 1996, well ahead of most of its peers. Like many other health systems, Geisinger has struggled to aggregate and make use of data. That’s particularly the case because as with other systems, Geisinger’s legacy analytics systems still in place can’t accommodate the growing flood of new data types emerging today.

Last year, Geisinger decided to create a new infrastructure which could bring this data together. It implemented Unified Data Architecture allowing it to integrate big data into its existing data analytics and management.  According to the article, Geisinger’s UDA rollout is the largest practical application of point-of-care big data in the industry. Of particular note, Geisinger is crunching not only enterprise healthcare data (including HIE inputs, clinical departmental systems and patient satisfaction surveys) and consumer health tools (like smartphone apps) but even grocery store and loyalty program info.

Though all of its data hasn’t yet been moved to the UDA, Geisinger has already seen some big data successes, including:

* “Close the Loop” program:  Using natural language processing, the UDA analyzes clinical and diagnostic imaging reports, including free text. Sometimes it detects problems that may not be relevant to the initial issue (such as injuries from a car crash) which can themselves cause serious harm. The program has already saved patient lives.

* Early sepsis detection/treatment: Geisinger uses the UDA to bring all sepsis-patient information in one place as they travel through the hospital. The system alerts providers to real-time physiologic data in patients with life-threatening septic shock, as well as tracking when antibiotics are prescribed and administered. Ninety percent of providers who use this tool consistently adhere to sepsis treatment protocols, as opposed to 40% of those who don’t.

* Surgery costs/outcomes: The Geisinger UDA tracks and integrates surgical supply-chain data, plus clinical data by surgery type and provider, which offers a comprehensive view of performance by provider and surgery type.  In addition to offering performance insight, this approach has also helped generate insights about supply use patterns which allow the health system to negotiate better vendor deals.

To me, one of the most interesting things about this story is that while Geisinger is at a relatively early stage of its big data efforts, it has already managed to generate meaningful benefits from its efforts. My guess is that its early successes are more due to smart planning – which includes worthwhile goals from day one of the rollout — than the technology per se. Regardless, let’s hope other hospital big data projects fare so well. (Meanwhile, for a look at another interesting hospital big data project, check out this story.)

ACO-Affiliated Hospitals May Be Ahead On Strategic Health IT Use

Posted on December 26, 2016 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Over the past several years I’ve been struck by how seldom ACOs seem to achieve the objectives they’re built to meet – particularly cost savings and quality improvement goals – even when the organizations involved are pretty sophisticated.

For example, the results generated the Medicare Shared Savings Program and  Pioneer ACO Model have been inconsistent at best, with just 31% of participants getting a savings bonus for 2015, despite the fact that the “Pioneers” were chosen for their savvy and willingness to take on risk.

Some observers suggested this would change as hospitals and ACOs found better health IT solutions, but I’ve always been somewhat skeptical about this. I’m not a fan of the results we got when capitation was the rage, and to me current models have always looked like tarted-up capitation, the fundamental flaws of which can’t be fixed by technology.

All that being said, a new journal article suggests that I may be wrong about the hopelessness of trying to engineer a workable value-based solution with health IT. The study, which was published in the American Journal of Managed Care, has concluded that if nothing else, ACO incentives are pushing hospitals to make more strategic HIT investments than they may have before.

To conduct the study, which compared health IT adoption in hospitals participating in ACOs with hospitals that weren’t ACO-affiliated, the authors gathered data from 2013 and 2014 surveys by the American Hospital Association. They focused on hospitals’ adherence to Stage 1 and Stage 2 Meaningful Use criteria, patient engagement-oriented health IT use and HIE participation.

When they compared 393 ACO hospitals and 810 non-ACO hospitals, the researchers found that a larger percentage of ACO hospitals were capable of meeting MU Stage 1 and Stage 2. They also noted that nearly 40% of ACO hospitals had patient engagement tech in place, as compared with 15.2% of non-ACO hospitals. Meanwhile, 49% of ACO hospitals were involved with HIEs, compared with 30.1% of non-ACO hospitals.

Bottom line, the authors concluded that ACO-based incentives are proving to be more effective than Meaningful Use at getting hospitals adopt new and arguably more effective technologies. Fancy that! (Finding and implementing those solutions is still a huge challenge for ACOs, but that’s a story for another day.)

Of course, the authors seem to take it as a given that patient engagement tech and HIEs are strategic for more or less any hospital, an assumption they don’t do much to justify. Also, they don’t address how hospitals in and out of ACOs are pursuing population health or big data strategies, which seems like a big omission. This weakens their argument somewhat in my view. But the data is worth a look nonetheless.

I’m quite happy to see some evidence that ACO models can push hospitals to make good health IT investment decisions. After all, it’d be a bummer if hospitals had spent all of that time and money building them out for nothing.

Paris Hospitals Use Big Data To Predict Admissions

Posted on December 19, 2016 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Here’s a fascinating story in from Paris (or par-ee, if you’re a Francophile), courtesy of Forbes. The article details how a group of top hospitals there are running a trial of big data and machine learning tech designed to predict admission rates. The hospitals’ predictive model, which is being tested at four of the hospitals which make up the Assistance Publiq-Hopitaux de Paris (AP-HP), is designed to predict admission rates as much as 15 days in advance.

The four hospitals participating in the project have pulled together a massive trove of data from both internal and external sources, including 10 years’ worth of hospital admission records. The goal is to forecast admissions by the day and even by the hour for the four facilities participating in the test.

According to Forbes contributor Bernard Marr, the project involves using time series analysis techniques which can detect patterns in the data useful for predicting admission rates at different times.  The hospitals are also using machine learning to determine which algorithms are likely to make good predictions from old hospital data.

The system the hospitals are using is built on the open source Trusted Analytics Platform. According to Marr, the partners felt that the platform offered a particularly strong capacity for ingesting and crunching large amounts of data. They also built on TAP because it was geared towards open, collaborative development environments.

The pilot system is accessible via a browser-based interface, designed to be simple enough that data science novices like doctors, nurses and hospital administration staff could use the tool to forecast visit and admission rates. Armed with this knowledge, hospital leaders can then pull in extra staffers when increased levels of traffic are expected.

Being able to work in a distributed environment will be key if AP-HP decides to roll the pilot out to all of its 44 hospitals, so developers built with that in mind. To be prepared for the future, which might call for adding a great deal of storage and processing power, they designed distributed, cloud-based system.

“There are many analytical solutions for these type of problems, [but] none of them have been implemented in a distributed fashion,” said Kyle Ambert, an Intel data scientist and TAP contributor who spoke with Marr. “Because we’re interested in scalability, we wanted to make sure we could implement these well-understood algorithms in such a way that they work over distributed systems.”

To make this happen, however, Ambert and the development team have had to build their own tools, an effort which resulted in the first contribution to an open-source framework of code designed to carry out analysis over scalable, distributed framework, one which is already being deployed in other healthcare environments, Marr reports.

My feeling is that there’s no reason American hospitals can’t experiment with this approach. In fact, maybe they already are. Readers, are you aware of any US facilities which are doing something similar? (Or are most still focused on “skinny” data?)

Easing The Transition To Big Data

Posted on December 16, 2016 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Tapping the capabilities of big data has become increasingly important for healthcare organizations in recent years. But as HIT expert Adheet Gogate notes, the transition is not an easy one, forcing these organizations to migrate from legacy data management systems to new systems designed specifically for use with new types of data.

Gogate, who serves as vice president of consulting at Citius Tech, rightly points out that even when hospitals and health systems spend big bucks on new technology, they may not see any concrete benefits. But if they move through the big data rollout process correctly, their efforts are more likely to bear fruit, he suggests. And he offers four steps organizations can take to ease this transition. They include:

  • Have the right mindset:  Historically, many healthcare leaders came up through the business in environments where retrieving patient data was difficult and prone to delays, so their expectations may be low. But if they hope to lead successful big data efforts, they need to embrace the new data-rich environment, understand big data’s potential and ask insightful questions. This will help to create a data-oriented culture in their organization, Gogate writes.
  • Learn from other industries: Bear in mind that other industries have already grappled with big data models, and that many have seen significant successes already. Healthcare leaders should learn from these industries, which include civil aviation, retail and logistics, and consider adopting their approaches. In some cases, they might want to consider bringing an executive from one of these industries on board at a leadership level, Gogate suggests.
  • Employ the skills of data scientists: To tame the floods of data coming into their organization, healthcare leaders should actively recruit data scientists, whose job it is to translate the requirements of the methods, approaches and processes for developing analytics which will answer their business questions.  Once they hire such scientists, leaders should be sure that they have the active support of frontline staffers and operations leaders to make sure the analyses they provide are useful to the team, Gogate recommends.
  • Think like a startup: It helps when leaders adopt an entrepreneurial mindset toward big data rollouts. These efforts should be led by senior leaders comfortable with this space, who let key players act as their own enterprise first and invest in building critical mass in data science. Then, assign a group of core team members and frontline managers to areas where analytics capabilities are most needed. Rotate these teams across the organization to wherever business problems reside, and let them generate valuable improvement insights. Over time, these insights will help the whole organization improve its big data capabilities, Gogash says.

Of course, taking an agile, entrepreneurial approach to big data will only work if it has widespread support, from the C-suite on down. Also, healthcare organizations will face some concrete barriers in building out big data capabilities, such as recruiting the right data scientists and identifying and paying for the right next-gen technology. Other issues include falling reimbursements and the need to personalize care, according to healthcare CIO David Chou.

But assuming these other challenges are met, embracing big data with a willing-to-learn attitude is more likely to work than treating it as just another development project. And the more you learn, the more successful you’ll be in the future.

EMR Replacement & Migration Perspective: Tim Schoener, VP/CIO, UPMC Susquehanna

Posted on December 8, 2016 I Written By

header-chime
In the midst of a merger with a major Pennsylvania healthcare organization, Tim Schoener is wholly focused on EHR transition. He outlines Susquennaha’s plan for each aspect of transition, offering innovative and unique approaches to each. In addition, Schoener provides cogent insights regarding the intricacies involved with a multi-database system, the expenses associated with archival solutions, and the challenges associated with migrating records. This interview touches on many of the considerations necessary for a successful EHR transition as Schoener discusses minimizing surprises during a transition; why migrating a year’s worth of results is optimal; and how their document management system fulfills archival needs.

CHIME Fall CIO Forum provides valuable education programming, tailored specifically to meet the needs of CIOs and other healthcare IT executives. Justin Campbell, of Galen Healthcare Solutions, had the opportunity to attend this year’s forum and interview CIOs from all over the country. Looking for additional EMR replacement perspectives & lessons learned? View a recent panel where HCO leaders discussed their experiences with EHR transition, data migration & archival.

KEY INSIGHTS

Absolutely, we have problem lists that can’t be reconciled; there’s a problem list in the Soarian world and a problem list in the NextGen world, and they’re not the same thing right now, not at all.

We’re being told, if you think you’re going to migrate and move all this data to some sort of other archiving solution, get ready for a sticker shock.

Our intent is to take it to each physician specialty to establish a good comfort level, so when the transition occurs, I don’t have physicians’ saying to me ‘no one ever asked me…’ or not be able to provide excellent patient care. It’s going to be critical to the success of our EMR transition to keep our physicians engaged and involved.

Let’s face it, no staff member has the desire to support the legacy application when all of their coworkers are learning the new application. That’s a career limiting move.

It used to be something that struggling organizations were forced to pursue, but now very successful organizations are starting to affiliate and merge with other organizations because it’s just the state of healthcare.

CHIME is a great way to challenge yourself as a CIO and in your leadership. It pushes me in my leadership skills and helps to focus me back to what’s critical in the industry.
tim-schoener
Campbell: Tell me a little about yourself and your organization’s initiatives

Schoener: I’m Tim Schoener, the VP/CIO of, originally Susquehanna Health, which, as of October 1st, is now a part of the University of Pittsburgh Medical Center (UPMC) and re-named to UPMC Susquehanna. We’re located in central Pennsylvania, four hours away from Pittsburgh.

A major IT initiative for us is that we’re swapping out our EMR over the next couple of years. We are currently a Cerner Soarian customer. In fact, we were the initial Soarian beta site for Financials and second for Clinicals. We determined we eventually need to migrate to something else – that’s an Epic or Cerner decision for us at this point. UPMC’s enterprise model is Cerner and Epic, Cerner on the acute care side and Epic on the ambulatory side. As of this writing, we’ve made the decision to migrate to the UPMC blended model. Over the past nine months we’ve been focused on an EMR governance process, trying to get our team aligned on the journey that we’re about to take and by late next year we will likely be starting an implementation.

We currently leverage NextGen on the Ambulatory side, with approximately 300 providers that use that software product. We’re a four hospital system: two of which are critical access, one which is predominately outpatient, and the other a predominately inpatient facility. We were about a $600MM organization prior to our UPMC acquisition.

Campbell: Related to your current implementation, tell me a little bit about your data governance strategy and dictionary mapping that may occur between NextGen and Soarian.

Schoener: We definitely have a lot of interfaces, a lot of integration between the two core systems. From an integration perspective, we have context sharing, so physicians can contextually launch and interoperate from NextGen to Soarian, and vice-versa. We do pass some data back and forth—allergies and meds can be shared through a reconciliation process—but we certainly aren’t integrated. It’s the state of healthcare.

Campbell: That’s why you anticipate moving to a single platform, single database?

Schoener: Absolutely, we have problem lists that are not reconciled. There’s a problem list in the Soarian world and a problem list in the NextGen world, and they’re not the same thing right now, not at all. Meds and allergies are pretty much all we get in terms of outpatient to inpatient clinical data sharing today.

Campbell: Do you leverage an archival solution for any legacy data?

Schoener: We use EMC and have large data storage with them. I wouldn’t call it archival, but we have an electronic document management system – Soarian’s eHIM.

There’s a huge amount of data out there and I know you have some questions related to our thinking with respect to migration. I have some thoughts around that related to levering our document management system versus archiving into a separate system. I’m pretty certain we would be thinking ‘why not use eHIM as our archival process, and just put other data in that repository as necessary?’ For results data, for instance, what we’re thinking of migrating, or what our providers are requesting, is a years’ worth of results. ‘Give me a year’s worth of results, and then make sure everything else is available in eHIM.’

Campbell: As such, your default is to migrate a year’s worth of data?

Schoener: Yes. We would presume that the provider is probably not going to refer back to lab results or radiology results beyond a year, other than for health maintenance kind of things such as mammograms, pap smears, PSAs; those types of things.

Campbell: What expectations have you set with physicians when they go live on the new EMR?

Schoener: From an ambulatory perspective, we’re thinking that it would be nice to have the most recent note from the EMR available. All of the other notes for that patient would be consolidated into one note via a single pdf attachment. The note that’s the separate most recent note, we envision that being in a folder for that particular date. That note would reside in the appropriate folder location just like it would in the current EMR. Our goal is to bring the clinical data forward to the new EMR, taking all the other notes and placing them in a “previous notes” folder.

Campbell: Can you elaborate on your consideration of PAMI (Problems, Allergies, Medications, Immunizations) as part of the data migration?

Schoener: Sure. The disaster scenario would be the physician sits down with patient for first time with new EMR, and there are no meds, no allergies, and no problems! They’ll spend 25 minutes just gathering information, that would not work.

We’re thinking of deploying a group of nurses to assist with the data conversion and migration process. Our intent is to have them to retrieve CCDAs to populate those things I mentioned by consuming them right into the medical record, based on the physicians’ input. We expect there to be a reconciliation process to clean-up potential duplicates. Or, to be candid, we’ve talked about automating the CCDA process, consuming discrete clinical items from it by writing scripts and importing into the new EMR. I think we’re leaning towards having some staff involved in the process though.

Now if you share the same database between your acute and ambulatory EMR, and the patient was in ambulatory setting but now they’ve been admitted, it’s the same database: the meds are there, the problems are there, the allergies are there; it’s beautiful, right? If they weren’t, then the admission nurse is going to have to follow the same CCDA consume process that the ambulatory nurse followed. Or you start from scratch. On the acute side, we start from scratch a lot. Patients come in and we basically just start asking questions in the ER or in an acute care setting. We start asking for their meds, allergies, or problems – whatever they may have available.

Campbell: We’ve discussed notes, results and PAMI. Are there other clinical data elements that you’ve examined? How will you handle those?

Schoener: From an acute care perspective, our physicians are very interested in seeing the last H & P (History & Physical Examination) and the last operative note, so we’re going to consider two different ideas. One would be that all of that data would still reside in document management, which has the ability to be sorted. It’s currently very chart centric. For instance, you can easily pull the patient’s last acute care stay. There is the ability, however, to sort by H & P, operative note, or discharge summary—something along those lines for the separate buckets of information. Therefore, a physician could view the most recent H & P or view all sorted chronologically. In addition, they’ll be able to seamlessly launch directly from the new EMR to the old EMR, bypassing authentication, which is important to mitigate context switching.

One of the areas we’re struggling with is the growth chart. A physician would love the ability to see a child’s information from start to finish, not just from the time of the EMR transition. So that means some sort of birth height/weight data that we would want to retrieve and import into the new system so a growth chart could be generated. The other option is to somehow generate some sort of PDF of a growth chart up until the place where we transitioned to the new EMR. The latter however, would result in multiple growth charts, and a physician’s not going to be happy with that. So we’re trying to figure that one out.

Another area of concern is blood pressure data. We’re struggling with what to do with a patient we’re monitoring for blood pressure. We’d like to see more than one blood pressure reading and have some history on that.

Campbell: Thank you for elaborating on those items. What about data that is not migrated. How will that be addressed and persisted going forward?

Schoener: For the most part, everything else would be available in the document management system. We can generate that data from document our document management system and make it available to be queried by OIG or whoever else requires that data from a quality perspective. We are aware that an archival solution is very expensive. We’re being told, ‘if you think you’re going to migrate and move all this data to some sort of other archiving solution, get ready for a sticker shock.’ If that’s what the advisors and consultants are saying, then our thought is that probably isn’t going to be the direction we’re going to go. We’re likely going to stick with some type of document management system for archival.

Campbell: Very good. How are you gathering feedback from different specialties and departments? Do you have a governance process in place?

Schoener: So as you may have gathered, we’re getting ready. I don’t want surprises. I want physicians to be prepared and to set expectations for what’s going to be available. What I just described to you, we’ve vetted that out with our primary care docs. Now we’re going to take that to our cardiologists and ask them what they think. Then on to our urologists to allow them to weigh in. Our intent is to take it to each physician specialty to establish a good comfort level, so when the transition occurs, I don’t have physicians’ saying to me ‘no one ever asked me…’ or not be able to provide excellent patient care. It’s going to be critical to the success of our EMR transition to keep our physicians engaged and involved.

There will definitely be a learning curve with the new EMR, but we want to be clear and set expectations with respect to data migration and conversion, so that when the physician does use the new EMR they’re not saying ‘that darn Cerner or Epic.’  It’s more ‘that’s a part of the data migration process and we weren’t able to accomplish that.’

Campbell: What about legacy applications support. Will all of your staff be dedicated to the new project?

Schoener: I mean, let’s face it, no staff member has the desire to support the legacy application when all of their coworkers are learning the new application. That’s a career limiting move. We still haven’t decided what to do.

Campbell: I agree that no staff member wants to be left behind. I’ve talked to organizations where they use folks for both and it just doesn’t end well. You can’t expect them to do both, learning the new system while supporting the old one.

Schoener: I guess it depends on the capacity and the expectation of that particular project they’re working on. Maybe there is a person who has less involvement with the new EMR and they have availability where they can support both, although it’s unlikely. Sometimes you end up having someone who wants to retire within the time period. In that case, they can almost work their way to retirement and then not ever support the new EMR, although that situation is also unlikely.

It’s a great question, and one we’re going to have to have folks help us determine.

Campbell: Shifting gears a little bit, what are your thoughts on health data retention requirements? Too loose? Too stringent?  As you know, it varies state-to-state, from 7-10 years, but I feel like there’s a huge responsibility that is placed on organizations to be the custodians of that data. Do you agree?

Schoener: I think that’s just healthcare. A lot of it is legal considerations and our need to protect ourselves. That’s why do we do a lot of the things we do. We’re protecting ourselves from lawsuits and litigation. I think it’s expected; it’s just the nature of the business. Just think of what we had in a paper world. We used to have rooms and rooms full of charts and now that’s all gone. With our current process, any paper that comes in is scanned in within the first 24 hours. So it’s not something I worry about. My focus now is making sure our providers can perform excellent patient care on the new EMR.

Campbell: Could you provide some advice, insight or wisdom for healthcare organizations pursuing EMR/EHR replacement & transition?

Schoener: Get ready for some fun! Affiliations and acquisitions are greatly impacting these decisions. It used to be something that struggling organizations were forced to pursue, but now very successful organizations are starting to affiliate and merge with other organizations because it’s just the state of healthcare. One bit of wisdom for anyone is: if you’re not interested in that type of transition and change occurring, healthcare’s not for you. That’s the nature of the business we’re in.

I would say from an EHR transition process, I found that having an advisor is extremely beneficial to help me think outside of my day-to-day operations. They’re able to look outside of your organization and ask the right questions. If you pick the right advisor, they’ll protect you and protect your organization. I think it’s been very healthy for us to have someone from the outside give us counsel and advice because it’s a tough process. It’s extremely expensive, and extremely polarizing.

Campbell: Outside of the networking, what did you come to CHIME focused on this year?

Schoener: CHIME is a great way to challenge yourself as a CIO and in your leadership, it pushes me in my leadership skills and helps to focus me back to what’s critical in the industry. It helps me to think more strategic and broad, not to get too engaged in one particular topic. I think it’s just great for professional development. CHIMEs the best out there with respect to what I do.

This interview has been edited and condensed.

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About Tim Schoener
Tim Schoener is the Vice President/Chief Information Officer for UPMC Susquehanna, a new partner of UPMC since October 1, 2016, which is a four-hospital integrated health system in northcentral Pennsylvania including Divine Providence Hospital, Muncy Valley Hospital, Soldiers + Sailors Memorial Hospital and Williamsport Regional Medical Center. UPMC Susquehanna has been Most Wired for 14 of the last 16 years and also HIMSS Level 6. Tim has worked at Susquehanna for over 24 years, 19 of those years in Information Technology.  He also has responsibilities for health records, management engineering and biomedical engineering. He is a CHCIO, HIMSS Fellow and CPHIMS certified. Tim received his undergraduate degree from The Pennsylvania State University with a BSIE in Industrial Engineering and his MBA from Liberty University. 

About Justin Campbell
Justin is Vice President, Strategy, at Galen Healthcare Solutions. He is responsible for market intelligence, segmentation, business and market development and competitive strategy. Justin has been consulting in Health IT for over 10 years, guiding clients in the implementation, integration and optimization of clinical systems. He has been on the front lines of system replacement and data migration and is passionate about advancing interoperability in healthcare and harnessing analytical insights to realize improvements in patient care. Justin can be found on Twitter at @TJustinCampbell and LinkedIn.

About Galen Healthcare Solutions
Galen Healthcare Solutions is an award-winning, #1 in KLAS healthcare IT technical & professional services and solutions company providing high-skilled, cross-platform expertise and proud sponsor of the Tackling EHR & EMR Transition Series. For over a decade, Galen has partnered with more than 300 specialty practices, hospitals, health information exchanges, health systems and integrated delivery networks to provide high-quality, expert level IT consulting services including strategy, optimization, data migration, project management, and interoperability. Galen also delivers a suite of fully integrated products that enhance, automate, and simplify the access and use of clinical patient data within those systems to improve cost-efficiency and quality outcomes. For more information, visit www.galenhealthcare.com. Connect with us on Twitter, Facebook and LinkedIn.