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UCHealth Adds Claims Data To Population Health Dataset

Posted on April 24, 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 Colorado-based health system is implementing a new big data strategy which incorporates not only data from clinics, hospitals and pharmacies, but also a broad base of payer claim data.

UCHealth, which is based in Aurora, includes a network of seven hospitals and more than 100 clinics, caring collectively for more than 1.2 million unique patients in 2016. Its facilities include the University of Colorado Hospital, the principal teaching hospital for the University of Colorado School of Medicine.

Leaders at UCHealth are working to improve their population health efforts by integrating data from seven state insurers, including Anthem Blue Cross and Blue Shield, Cigna, Colorado Access, Colorado Choice Health Plans, Colorado Medicaid, Rocky Mountain Health Plans and United Healthcare.

The health system already has an Epic EMR in place across the system which, as readers might expect, offers a comprehensive view of all patient treatment taking place at the system’s clinics and hospitals.

That being said, the Epic database suffers from the same limitations as any other locally-based EMR. As UCHealth notes, its existing EMR data doesn’t track whether a patient changes insurers, ages into Medicare, changes doctors or moves out of the region.

To close the gaps in its EMR data, UCHealth is using technology from software vendor Stratus, which offers a healthcare data intelligence application. According to the vendor, UCHealth will use Stratus technology to support its accountable care organizations as well as its provider clinical integration strategy.

While health system execs expect to benefit from integrating payer claims data, the effort doesn’t satisfy every item on their wish list. One major challenge they’re facing is that while Epic data is available to all the instant it’s added, the payer data is not. In fact, it can take as much as 90 days before the payer data is available to UCHealth.

That being said, UCHealth’s leaders expect to be able to do a great deal with the new dataset. For example, by using Stratus, physicians may be able to figure out why a patient is visiting emergency departments more than might be expected.

Rather than guessing, the physicians will be able to request the diagnoses associated with those visits. If the doctor concludes that their conditions can be treated in one of the system’s primary care clinics, he or she can reach out to these patients and explain how clinic-based care can keep them in better health.

And of course, the health system will conduct other increasingly standard population health efforts, including spotting health trends across their community and better understanding each patient’s medical needs.

Over the next several months, 36 of UCHealth’s primary care clinics will begin using the Stratus tool. While the system hasn’t announced a formal pilot test of how Stratus works out in a production setting, rolling this technology out to just 36 doctors is clearly a modest start. But if it works, look for other health systems to scoop up claims data too!

EMRs Can Improve Outcomes For Weekend Hospital Surgeries

Posted on April 7, 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.

Unfortunately, it’s well documented that people often have worse outcomes when they’re treated in hospitals over the weekend. For example, one recent study from the Association of Academic Physiatrists found that older adults admitted with head trauma over the weekend have a 14 percent increased risk of dying versus those admitted on a weekday.

But if a hospital makes good use of its EMR, these grim stats can be improved, according to a new study published in JAMA Surgery. In the study, researchers found that use of EMRs can significantly improve outcomes for hospital patients who have surgeries over the weekend.

To conduct the study, which was done by Loyola Medicine, a group of researchers identified some EMR characteristics which they felt could overcome the “weekend effect.” The factors they chose included using electronic systems designed to schedule surgeries seamlessly as well as move patients in and out of hospital rooms efficiently.

Their theories were based on existing research suggesting that patients at hospitals with electronic operating room scheduling were 33 percent less likely to experience a weekend effect than hospitals using paper-based scheduling. In addition, studies concluded patients at hospitals with electronic bed-management systems were 35 percent less likely to experience the weekend effect.

To learn more about the weekend effect, researchers analyzed the records provided by the AHRQ’s Healthcare Cost and Utilization Project State Inpatient Database.  Researchers looked at treatment and outcomes for 2,979 patients admitted to Florida hospitals for appendectomies, acute hernia repairs and gallbladder removals.

The research team found that 32 percent (946) of patients experienced the weekend effect, which they defined as having longer hospital stays than expected. Meanwhile, it concluded that patients at hospitals with high-speed EMR connectivity, EMR in the operating room, electronic operating room scheduling, CPOE systems and electronic bed management did better. (The analysis was conducted with the help of Loyola’s predictive analytics program.)

Their research follows on a 2015 Loyola study, published in Annals of Surgery, which named five factors that reduced the impact of the weekend effect. These included full adoption of electronic medical records, home health programs, pain management programs, increased registered nurse-to-bed ratios and inpatient physical rehabilitation.

Generally speaking, the study results offer good news, as they fulfill some the key hopes hospitals had when installing their EMR in the first place. But I was left wondering whether the study conflated cause and effect. Specifically, I found myself wondering whether hospitals with these various systems boosted their outcomes with technology, or whether hospitals that invested in these technologies could afford to provide better overall care.

It’s also worth noting that several of the improvement factors cited in the 2015 study did not involve technology at all. Even if a hospital has excellent IT systems in place, putting home health, pain management and physical rehabilitation in place – not to mention higher nurse-to-patient ratios – calls for different thinking and a different source of funding.

Still, it’s always good to know that health IT can generate beneficial results, especially high-ticket items like EMRs. Even incremental progress is still progress.

Cleveland Clinic Works To Eliminate Tech Redundancies

Posted on March 1, 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.

The Cleveland Clinic has relied on its EMR for quite some time. In fact, it adopted Epic in the 1990s, long before most healthcare organizations were ready to make a bet on EMRs. Today, decades later, the Epic EMR is the “central data hub” for the medical center and is central to both its clinical and operational efforts, according to William Morris, MD, the Clinic’s associate chief information officer.

But Morris, who spoke about the Clinic’s health IT with Health Data Management, also knows its limitations. In an interview with the magazine’s Greg Slabodkin, he notes that while the EMR may be necessary, it isn’t sufficient. The Epic EMR is “just a digital repository,” he told Slabodkin. “Ultimately, it’s what you do with the technology in your ecosystem.”

These days, IT leaders at the Clinic are working to streamline the layers of additional technology which have accreted on top of the EMR over the years. “As an early adopter of Epic, we have accumulated quite a bit of what I’ll call technical debt,” said Doug Smith, interim chief information officer. “What I mean by that is multiple enhancements, bolt-ons, or revisions to the core application. We have to unburden ourselves of that.”

It’s not that Clinic leaders are unhappy with their EMR. In fact, they’re finding ways to tap its power to improve care. For example, to better leverage its EMR data, the Cleveland Clinic has developed data-driven “risk scores” designed to let doctors know if patients need intervention. The models, developed by the Clinic’s Quantitative Health Sciences group, offer outcome risk calculators for several conditions, including cancer, cardiovascular disease and diabetes.

(By the way, if predictive analytics interest you, you might want to check out our coverage of such efforts at New York’s Mount Sinai Hospital, which is developing a platform to predict which patients might develop congestive heart failure and care for patients already diagnosed with the condition more effectively. I’ve also taken a look at a related product being developed by Google’s DeepMind, an app named Streams which will ping clinicians if a patient needs extra attention.)

Ultimately, though, the organization hopes to simplify its larger health IT infrastructure substantially, to the point where 85% of the HIT functionality comes from the core Epic system. This includes keeping a wary eye on Epic upgrades, and implementing new features selectively. “When you take an upgrade in Epic, they are always turning on more features and functions,” Smith notes. “Most are optional.”

Not only will such improvements streamline IT operations, they will make clinicians more efficient, Smith says. “They are adopting standard workflows that also exist in many other organizations—and, we’re more efficient in supporting it because we don’t take as long to validate or support an upgrade.”

As an aside, I’m interested to read that Epic is tossing more features at Cleveland Clinic than it cares to adopt. I wonder if those are what engineers think customers want, or what they’re demanding today?

The Distributed Hospital On The Horizon

Posted on February 24, 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.

If you’re reading this blog, you already know that distributed, connected devices and networks are the future of healthcare.  Connected monitoring devices are growing more mature by the day, network architectures are becoming amazingly fluid, and with the growth of the IoT, we’re adding huge numbers of smart devices to an already-diverse array of endpoints.  While we may not know what all of this will look when it’s fully mature, we’ve already made amazing progress in connecting care.

But how will these trends play out? One nice look at where all this is headed comes from Jeroen Tas, chief innovation and strategy officer at Philips. In a recent article, Tas describes a world in which even major brick-and-mortar players like hospitals go almost completely virtual.  Certainly, there are other takes out there on this subject, but I really like how Tas explains things.

He starts with the assertion that the hospital of the future “is not a physical location with waiting rooms, beds and labs.” Instead, a hospital will become an abstract network overlay connecting nodes. It’s worth noting that this isn’t just a concept. For an example, Tas points to the Mercy Virtual Care Center, a $54 million “hospital without beds” dedicated to telehealth and connected care.  The Center, which has over 300 employees, cares for patients at home and in beds across 38 hospitals in seven states.

While the virtual hospital may not rely on a single, central campus, physical care locations will still matter – they’ll just be distributed differently. According to Tas, the connected health network will work best if care is provided as needed through retail-type outlets near where people live, specialist hubs, inpatient facilities and outpatient clinics. Yes, of course, we already have all of these things in place, but in the new connected world, they’ll all be on a single network.

Ultimately, even if brick-and-mortar hospitals never disappear, virtual care should make it possible to cut down dramatically on hospital admissions, he suggests.  For example, Tas notes that Philips partner Banner Health has slashed hospital admissions almost 50% by using telehealth and advanced analytics for patients with multiple chronic conditions. (We’ve also reported on a related pilot by Partners HealthCare Brigham and Women’s Hospital, the “Home Hospital,” which sends patients home with remote monitoring devices as an alternative to admissions.)

Of course, the broad connected care outline Tas offers can only take us so far. It’s all well and good to have a vision, but there are still some major problems we’ll have to solve before connected care becomes practical as a backbone for healthcare delivery.

After all, to cite one major challenge, community-wide connected health won’t be very practical until interoperable data sharing becomes easier – and we really don’t know when that will happen. Also, until big data analytics tools are widely accessible (rather than the province of the biggest, best-funded institutions) it will be hard for providers to manage the data generated by millions of virtual care endpoints.

Still, if Tas’s piece is any indication, consensus is building on what next-gen care networks can and should be, and there’s certainly plenty of ways to lay the groundwork for the future. Even small-scale, preliminary connected health efforts seem to be fostering meaningful changes in how care is delivered. And there’s little doubt that over time, connected health will turn many brick-and-mortar care models on their heads, becoming a large – or even dominant – part of care delivery.

Getting there may be tricky, but if providers keep working at connected care, it should offer an immense payoff.

Indiana Health System Takes On Infection Control With Predictive Analytics

Posted on February 22, 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.

At Indiana University Health, a 15-hospital non-profit health system, they’ve taken aim at reducing the rate of central-line associated bloodstream infections – better known to infection control specialists as CLABSIs.

According to the CDC, CLABSIs are preventable, but at present still result in thousands of deaths each year and add billions of dollars in costs to U.S. healthcare system spending. According to CDC data, patient mortality rates related to CLABSI range from 12% to 25%, and the infections cost $3,700 to $36,000 per episode.

Hospitals have been grappling with this problem for a long time, but now technology may offer preventive options. To cut its rate of CLABSIs, IU Health has decided to use predictive analytics in addition to traditional prevention strategies, according to an article in the AHA’s Hospitals & Health Systems magazine.

Reducing the level of hospital-acquired infections suffered by your patients always makes sense, but IU Health arguably has additional incentives to do it. The decision to attack CLABSIs comes as IU Health takes on a strategic initiative likely to demand a close watch on such metrics. At the beginning of January, Indiana University Health kicked off its participation in the CMS Next Generational Accountable Care Organization Model, putting its ACO in the national spotlight as a potential model for improving fee-for-service Medicare.

According to H&HN, IU Health has launched its predictive analytics pilot for CLABSI prevention at its University Hospital location, which includes a 600-bed Level I trauma center and 300-bed tertiary care center which also serves as one of the 10 largest transplant centers in the U.S.

Executives there told the magazine that the predictive analytics effort was an outgrowth of its long-term EMR development effort, which has pushed them to streamline data flow across platforms and locations over the past several years.

The hospital’s existing tech prior to the predictive analytics effort did include an e-surveillance program for hospital-acquired infections, but even using the full powers of the EMR and e-surveillance solution together, the hospitals could only monitor for CLABSI which had already been diagnosed.

This retrospective approach succeeded in cutting IU Health’s CLABSI rate from 1.7 CLABSIs over central-line days in 2015 to 1.2 last year. But IU Health hopes to improve the hospital’s results even further by getting ahead of the game.

Last year, the system implemented a data visualization platform designed to give providers a quick-and-easy look at data in real time. The platform lets managers keep track of many important variables easily, including whether hospital units have skipped any line maintenance activities or failed to follow-through on CLABSI bundles. It’s also saving time for nurse managers, who used to have to track data manually, and letting them check on patient trend line data at a glance.

The H&HN article doesn’t say whether the hospital has managed to cut its CLABSI rate any further, but it’s hard to imagine how predictive analytics could deliver zero results. Let’s wish IU Health further luck in cutting CLABSI rates down further.

Is Your Current Analytics Infrastructure Keeping You From Success in Healthcare Analytics?

Posted on February 17, 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.

The following is a paid blog post sponsored by Intel.

Healthcare analytics is all the talk in healthcare right now.  It’s really no surprise since many have invested millions and even billions of dollars in digitizing their health data.  Now they want to extract value from that data.  No doubt, the promise of healthcare analytics is powerful.  I like to break this promise out into two categories: Patient Analysis and Patient Influence.

Patient Analysis

On the one side of healthcare analytics is analyzing your patient population to pull reports on patients who need extra attention.  In some cases, these patients are the most at risk portions of your population with easy to identify disease states.  In other cases, they’re the most expensive portion of your population.  Both of these are extremely powerful analytics as your healthcare organization works to improve patient care and lower costs.

An even higher level of patient analysis is using healthcare analytics to identify patients who don’t seem to be at risk, but whose health is in danger.  These predictive analytics are much more difficult to create because by their very nature they’re imperfect.  However, this is where the next generation of patient analysis is going very quickly.

Patient Influence

On the other side of healthcare analytics is using patient data to influence patients.  Patient influence analytics can tell you simple things like what type of communication modality is preferred by a patient.  This can be used on an individual level to understand whether you should send an email, text, or make a phone call or it can be used on the macro level to drive the type of technologies you buy and content you create.

Higher level patient influence analytics take it one step further as they analyze a patient’s unique preferences and what influences the patient’s healthcare decision making.  This often includes pulling in outside consumer data that helps you understand and build a relationship with the patient.  This analytic might tell you that the patient is a huge sports fan and which is their favorite team.  It might also tell you that this person has a type A personality.  Together these analytics can inform you on the most appropriate ways and methods to interact and influence the patient.

What’s Holding Healthcare Analytics Back?

Both of these healthcare analytics approaches have tremendous promise, but many of them are being held back by a healthcare organization’s current analytics infrastructure.

The first problem many organizations have is where they are storing their data.  I’d describe their data as being stored in virtual prisons.  We need to unlock this data and free it so that it can be used in healthcare analytics.  If you can’t get at the data within your own organization, how can we even start talking about all the health data being stored outside the four walls of your organization?  Plus, we need to invest in the right storage that can support the growth of this data.  If you don’t solve these data access and storage pieces, you’ll miss out on a lot of the benefits of healthcare analytics.

Second, do you trust your data?  Most hospital CIOs I talk to usually respond, “Mostly.”  If you can’t trust your data, you can’t trust your analytics.  A fundamental building block of successful analytics is building trust in your data.  This starts by implementing effective workflows that capture the data properly on the front end.

Next, do you have the processing power required to process all these analytics and data?  Healthcare analytics in many healthcare organizations reminds me of the old days when graphic designers and video producers would have to wait hours for graphics programs to load or videos to render.  Eventually we learned not to skimp on processing power for these tasks.  We need to learn this same lesson with healthcare analytics.  Certainly cloud makes this easier, but far too often we under fund the processing power needed for these projects.

Finally, all the processing power in the world won’t help if you don’t have your most important piece of analytics infrastructure: people.  No doubt, finding experienced people in healthcare data analytics is a challenge.  It is the hardest thing to do on this list since it is very competitive and very expensive.  The good news is that if you solve the other problems above, then you become an attractive place for these experts to work.

In your search for a healthcare analytics expert, you can likely find a data expert.  You can find a clinical expert.  You can find an EHR expert.  Finding someone who can work across all three is the Holy Grail and nearly impossible to find.  This is why in most organizations healthcare analytics is a team sport.  Make sure that as you build your infrastructure of healthcare analytics people, you make sure they are solid team players.

It’s time we start getting more value out of our EHR and health IT systems.  Analytics is one of those tools that will get us there.  Just be sure that your current infrastructure isn’t holding you back from achieving those goals.

If this topic interests you and you’ll be at HIMSS 2017, join us at the Intel Health Booth #2661 on Tuesday, 2/21 from 2:00-2:45 PM where we’ll be holding a special meetup to discuss Getting Ready for Precision Health.  This meetup will also be available virtually via Periscope on the @IntelHealth Twitter account.

UCSF Partners With Intel On Deep Learning Analytics For Health

Posted on January 30, 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.

UC San Francisco’s Center for Digital Health Innovation has agreed to work with Intel to deploy and validate a deep learning analytics platform. The new platform is designed to help clinicians make better treatment decisions, predict patient outcomes and respond quickly in acute situations.

The Center’s existing projects include CareWeb, a team-based collaborative care platform built on Salesforce.com social and mobile communications tech; Tidepool, which is building infrastructure for next-gen smart diabetes management apps; Health eHeart, a clinical trials platform using social media, mobile and realtime sensors to change heart disease treatment; and Trinity, which offers “precision team care” by integrating patient data with evidence and multi-disciplinary data.

These projects seem to be a good fit with Intel’s healthcare efforts, which are aimed at helping providers succeed at distributed care communication across desktop and mobile platforms.

As the two note in their joint press release, creating a deep learning platform for healthcare is extremely challenging, given that the relevant data is complex and stored in multiple incompatible systems. Intel and USCF say the next-generation platform will address these issues, allowing them to integrate not only data collected during clinical care but also inputs from genomic sequencing, monitors, sensors and wearables.

To support all of this activity obviously calls for a lot of computing power. The partners will run deep learning use cases in a distributed fashion based on a CPU-based cluster designed to crunch through very large datasets handily. Intel is rolling out the computing environment on its Xeon processor-based platform, which support data management and the algorithm development lifecycle.

As the deployment moves forward, Intel leaders plan to study how deep learning analytics and machine-driven workflows can optimize clinical care and patient outcomes, and leverage what they learn when they create new platforms for the healthcare industry. Both partners believe that this model will scale for future use case needs, such as larger convolutional neural network models, artificial networks patterned after living organizations and very large multidimensional datasets.

Once implemented, the platform will allow users to conduct advanced analytics on all of this disparate data, using machine learning and deep learning algorithms. And if all performs as expected, clinicians should be able to draw on these advanced capabilities on the fly.

This looks like a productive collaboration. If nothing else, it appears that in this case the technology platform UCSF and Intel are developing may be productized and made available to other providers, which could be very valuable. After all, while individual health systems (such as Geisinger) have the resources to kick off big data analytics projects on their own, it’s possible a standardized platform could make such technology available to smaller players. Let’s see how this goes.

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?

“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.