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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.

What’s the Role of a Hospital CIO in Business Model Transformation?

Posted on December 23, 2016 I Written By

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

I don’t think anyone would argue that the healthcare business model is changing. There are a number of dynamics at play that are requiring every healthcare organization to evaluate what their business will look like in the future. Some examples of these changes include:

  • Patients with High Deductible Plans
  • Accountable Care Organizations
  • Other Risk Based Care Models
  • Value Based Reimbursement
  • Telemedicine
  • Chatbots and AI Assistants
  • Health Sensors
  • Retail Clinics

I could go on and on, but I think that highlights some of the major ones. What’s interesting about these trends is that it requires a change in business model. However, pretty much every one of these changes in business models requires the use of technology to facilitate the change. Some of them are impossible to do without technology.

If technology is going to play an important role in healthcare’s business transformation, what role should the hospital CIO play in the organization?

What’s shocking to me is how many CIOs don’t want any part in the business transformation part of healthcare. At CHIME I heard one CIO say, “We don’t want anything to do with MACRA. We just want to supply them the systems and let them figure it out.” I’m not sure the “them” he was referring to, but I think this approach is a big mistake. We’re all in this together and have to act as a team to get it done in the most efficient and effective way possible.

I was impressed by another hospital CIO who said basically the opposite. She said, “Oh no, we’re going to be in charge of MACRA and MIPS. I don’t want them taking over MACRA and MIPS, because if they’re in charge of it they’ll select a bunch of items for which we’re not capable of doing.”

Once again, this points to the need for collaboration to occur. You need the clinical insight together with the technical and software based insight in order to make the best decisions possible.

More importantly is I think it’s a big mistake for the hospital CIO to not be part of the business transformation. If the hospital CIO doesn’t take part in business transformation, then IT essentially becomes a commodity. The worst thing you can be in an organization is a commodity. When you’re a commodity they squeeze the budget out of you and you’re seen as non-essential or non-critical to an organization. What CIO wants to be in that type of organization?

I do see most progressive healthcare IT leaders outsourcing much of the “commodity IT” to other third party providers so they can focus their efforts on becoming a more essential part of their organization’s business transformation. The problem is that this requires a different set of skills and interests than what was essentially an operational role managing servers, desktop, and the network.

What type of CIO are you? What type of CIO does your organization need or want?

We’re Great at Creating Policies and Procedures, but Awful At Removing Them

Posted on December 21, 2016 I Written By

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

Ever since I heard Tony Scott, the US CIO, talk about his goal of taking stuff off the federal books, I’ve been chewing on that concept. There’s little doubt that the federal government is really great at creating laws and regulations, but they’re really poor at getting rid of old laws and regulations. It’s hard to blame them. I don’t really know anyone that enjoys what amounts to “spring cleaning.” Needless to say, the US government could certainly be part of an episode (or even multiple seasons) of Hoarders the way they keep laws and regulations sitting around gathering dust.

While it’s easy to slam the government for their hoarding tendencies, I don’t think healthcare is immune to this problem either. Sometimes we’re required to “hoard” patient medical records by law. That’s not a bad thing since it’s good to comply with the law. However, it is a bad thing when we no longer are required to retain the data and the data in this old data has limited value.

In fact, much of that old outdated data could pose a risk to patients. We all know that many of our first IT systems were implemented quickly and therefore resulted in poorly collected data. Keeping around incorrect data can lead to disastrous consequences. It might be time for some spring cleaning (yes, it can be done in Winter too).

What’s more troublesome than this is many of the policies and procedures that exist in most hospital systems. Much like the government these policies and procedures get put in place, but we rarely go back and take them off the books. My least favorite thing to hear in a hospital when I ask why they do something a certain way is “We’ve always done it this way.”

If we don’t know why we’re doing something, that’s the perfect opportunity to ask the question and figure out the answer. Many times there is a good answer and a good reason for the policy and procedure. However, more often than most people realize, we’re just doing something because we’ve always done it that way and not because it’s the best way to do something.

I love Tony Scott’s effort to purge things from the books that are outdated, useless, or even harmful. Every hospital organization I’ve seen could benefit from this approach as well. Their organization would benefit, their employees would benefit, and ultimately patients would benefit as well.

When was the last time you got rid of a policy or procedure?

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.

ReadsforRads is Working to Democratize Radiology

Posted on December 14, 2016 I Written By

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

At the RSNA 2016 conference, Healthcare Scene learned about a new platform for radiologists that’s looking to democratize radiology. This new platform is called ReadsforRads. In our conversation with Dr. Phillip A. Templeton, Chief Medical Officer at ReadsforRads, we learned more about ReadsforRads and their mission to democratize radiology. I love the approach they’re taking to make radiology better for both radiology departments and imaging centers. Plus, doing so will ultimate benefit the patients the most.

To learn more about ReadsforRads and the way they benefit the health system, radiologists, and patients, check out our video interview with Dr. Templeton below:

No doubt ReadsforRads has some challenges as they work to scale their platform, but I was impressed by the progress they’ve already made. Their efforts on managing radiologists credentialing was quite interesting. I mentioned the ReadsforRads platform to my radiologist neighbor and his wife instantly said “Yes! Moonlight so we can buy a house.”

While the opportunity for a radiologist to make some extra cash moonlighting is interesting, I was extremely excited about ReadsforRads ability to get the right radiologist reading the radiology image. There are a lot of situations where the radiology image needs to be read by a true expert and that person might be on vacation or small institutions might not be able to afford that type of radiologist expertise in house. ReadsforRads can cover these gaps and make sure the read is done by the most qualified person. That can really benefit all of healthcare.

Using NLP with Machine Learning for Predictive Analytics in Healthcare

Posted on December 12, 2016 I Written By

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

There are a lot of elements involved in doing predictive analytics in healthcare effectively. In most cases I’ve seen, organizations working on predictive analytics do some but not all that’s needed to really make predictive analytics as effective as possible. This was highlighted to me when I recently talked with Frank Stearns, Executive Vice President from HBI Solutions at the Digital Health Conference in NYC.

Here’s a great overview of the HBI Solutions approach to patient risk scores:

healthcare-predictive-analytics-model

This process will look familiar to most people in the predictive analytics space. You take all the patient data you can find, put it into a machine learning engine and output a patient risk score. One of the biggest trends happening with this process is the real-time nature of this process. Plus, I also love the way the patient risk score includes the attributes that influenced a patients risk score. Both of these are incredibly important when trying to make this data actionable.

However, the thing that stood out for me in HBI Solutions’ approach is the inclusion of natural language processing (NLP) in their analysis of the unstructured patient data. I’d seen NLP being used in EHR software before, but I think the implementation of NLP is even more powerful in doing predictive analytics.

In the EHR world, you have to be absolutely precise. If you’re not precise with the way you code a visit, you won’t get paid. If you’re not precise with how the diagnosis is entered into the EHR, that can have long term consequences. This has posed a real challenge for NLP since NLP is not 100% accurate. It’s gotten astoundingly good, but still has its shortcomings that require a human review when utilizing it in an EHR.

The same isn’t true when applying NLP to unstructured data when doing predictive analytics. Predictive analytics by its very nature incorporates some modicum of variation and error. It’s understood that predictive analytics could be wrong, but is an indication of risk. Certainly a failing in NLP’s recognition of certain data could throw off a predictive analytic. That’s unfortunate, but the predictive analytics aren’t relied on the same way documentation in an EHR is relied upon. So, it’s not nearly as big of a deal.

Plus, the value that’s received from applying NLP to pull out the nuggets of information that exists in the unstructured narrative sections of healthcare data is well worth that small amount of risk of the NLP being incorrect. As Frank Stearns from HBI solutions pointed out to me, the unstructured data is often where the really valuable data about a patients’ risk score exist.

I’d be interested in having HBI Solutions do a study of the whole list of findings that are often available in the unstructured data that weren’t available otherwise. However, it’s not hard to imagine a doctor documenting patient observations in the unstructured EHR narrative that they didn’t want to include as a formal diagnosis. Not the least of these are behavioral health observations that the doctor saw, observed, and documented but didn’t want to fully diagnose. NLP can pull these out of the narrative and include them in their patient risk score.

Given this perspective, it’s hard to imagine we’ll ever be able to get away from using NLP or related technology to pull out the valuable insights in the unstructured data. Plus, it’s easy to see how predictive analytics that don’t use NLP are going to be deficient when trying to use machine learning to analyze patients. What’s amazing is that HBI Solutions has been applying machine learning to healthcare for 5 years. That’s a long time, but also explains why they’ve implemented such advanced solutions like NLP in their predictive analytics solutions.

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.

Evaluate options, define scope and formulate a strategy for EHR data migration by downloading Galen’s EHR Migration Whitepaper.

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.

Population Health 101: The One Where It All Starts

Posted on December 7, 2016 I Written By

The following is a guest blog post by Abhinav Shashank, CEO & Co-founder of Innovaccer.
population-health-101
Former US President Abraham Lincoln once said, “Give me six hours to chop down a tree and I’ll spend four hours sharpening the ax.”  After having a look at the efficiency of the US healthcare system, one cannot help but notice the irony. A country spending $10,345 per person on healthcare shouldn’t be on the last spot of OECD rankings for life expectancy at birth!

Increasing Troubles
report from Commonwealth Fund points out how massive the US health care budget is. Various US governments have left no stone unturned in becoming the highest spender on healthcare, but have equally managed to see most of its money going down the drain!

Here are some highlights from the report:

  1. The US is 3rd when it comes to public spending on health care. The figure is $4197 per capita, but it covers only 34% of its residents. On the other hand, the UK spends only $2,802 per capita and covers 100% of the population!
  2. With $1,074, US has the 2nd highest private spending on healthcare.
  3. In 2013, US allotted 17.1% of its GDP to healthcare, which was the highest of any OECD country.   In terms of money, this was almost 50% more than the country in the 2nd spot.
  4. In the year 2013, the number of practicing physicians in the US was 2.6 per 1000 persons, which is less than the OECD median (3.2).
  5. The infant mortality rate in the US was also higher than other OECD nations.
  6. 68 percent of the population above 65 in the US is suffering from two or more chronic conditions, which is again the highest among OECD nations.

The major cause of these problems is the lack of knowledge about the population trends. The strategies in place will vibrantly work with the law only if they are designed according to the needs of the people.

population-health-trends

What is Population Health Management?
Population health management (PHM) might have been mentioned in ACA (2010), but the meaning of it is lost on many. I feel, the definition of population health, given by Richard J. Gilfillan, President and CEO of Trinity Health, is the most suitable one.

Population health refers to addressing the health status of a defined population. A population can be defined in many different ways, including demographics, clinical diagnoses, geographic location, etc. Population health management is a clinical discipline that develops, implements and continually refines operational activities that improve the measures of health status for defined populations.

The true realization of Population Health Management  (PHM) is to design a care delivery model which provides quality coordinated care in an efficient manner. Efforts in the right direction are being made, but the tools required for it are much more advanced and most providers lack the resources to own them.

Countless Possibilities
If Population Health Management is in place, technology can be leveraged to find out proactive solutions to acute episodes. Based on past episodes and outcomes, a better decision could be made.

The concept of health coaches and care managers can actually be implemented. When a patient is being discharged, care managers can confirm the compliance with health care plans. They can mitigate the possibility of readmission by keeping up with the needs and appointments of patients. Patients could be reminded about their medications. The linked health coaches could be intimated to further reduce the possibility of readmission.

Let us consider Diabetes for instance. Many times Diabetes is hereditary and preventive measures like patient engagement would play an important role in mitigating risks. Remote Glucometers, could be useful in keeping a check on patient sugar levels at home. It could also send an alert to health coaches and at-risk population could be engaged in near real-time.

Population Health Management not only keeps track of population trends but also reduces the cost of quality care. The timely engagement of at-risk population reduces the possibility of extra expenditure in the future. It also reduces the readmission rates. The whole point of population health management is to be able to offer cost effective quality-care.

The best thing to do with the past is to learn from it. If providers implement in the way Population Health Management is meant to be, then the healthcare system would be far better and patient-centric.

Success Story
A Virginia based collaborative started a health information based project in mid-2010. Since then, 11 practices have been successful in earning recognition from NCQA (National Committee for Quality Assurance). The implemented technologies have had a profound impact on organization’s performance.

  1. For the medical home patients, the 30-day readmission rate is below 2%.
  2. The patient engagement scores are at 97th percentile.
  3. With the help of the patient outreach program almost 40,000 patients have been visited as a part of preventive measures.

All this has increased the revenue by $7 million.

Barriers in the journey of Population Health Management
Currently, population health management faces a lot of challenges. The internal management and leadership quality has to be top notch so that interests remain aligned. Afterall, Population Health Management is all about team effort.

The current reimbursement model is also a concern. It has been brought forward from the 50s and now it is obsolete. Fee-for-service is anything, but cost-effective.

Patient-centric care is the heart of Population Health Management. The transition to this brings us to the biggest challenge and opportunity. Data! There is a lot of unstructured Data. True HIE can be achieved only if data are made available in a proper format. A format which doesn’t require tiring efforts from providers to get patient information. Providers should be able to gain access to health data in seconds.

The Road Ahead
We believe, the basic requirement for Population Health Management is the patient data. Everything related to a patient, such as, the outcome reports, the conditions in which the patient was born, lives, works, age and others is golden. To accurately determine the cost, activity-based costing could come in handy.

Today, the EMRs aren’t capable enough to address population health. The most basic model of population health management demands engagement on a ‘per member basis’ which can track and inform the cost of care at any point. The EMRs haven’t been designed in such a way. They just focus on the fee-for-service model.

In recent years, there has been an increased focus on population health management. Advances in the software field have been prominent and they account for the lion’s share of the expenditure on population health. I think, this could be credited to Affordable Care Act of 2010, which mandated the use of population health management solutions.

Today, the Population Health Management market is worth $14 billion and according to a report by Tractica, in five years, this value will be $31.8 billion. This is a good sign because it shows that the focus is on value-based care. There is no doubt we have miles to go, but at least now we are on the right path!