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The More Hospital IT Changes, The More It Remains The Same

Posted on June 23, 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.

Once every year or two, some technical development leads the HIT buzzword list, and at least at first it’s very hard to tell whether that will stick. But over time, the technologies that actually work well are subsumed into the industry as it exists, lose their buzzworthy quality and just do their job.

Once in a while, the hot new thing sparks real change — such as the use of mobile health applications — but more often the ideas are mined for whatever value they offer and discarded.  That’s because in many cases, the “new thing” isn’t actually novel, but rather a slightly different take on existing technology.

I’d argue that this is particularly true when it comes to hospital IT, given the exceptionally high cost of making large shifts and the industry’s conservative bent. In fact, other than the (admittedly huge) changes fostered by the adoption of EMRs, hospital technology deployments are much the same as they were ten years ago.

Of course, I’d be undercutting my thesis dramatically if I didn’t stipulate that EMR adoption has been a very big deal. Things have certainly changed dramatically since 2007, when an American Hospital Association study reported that 32% percent of hospitals had no EMR in place and 57% had only partially implemented their EMR, with only the remaining 11% having implemented the platform fully.

Today, as we know, virtually every hospital has implemented an EMR integrated it with ancillary systems (some more integrated and some less).  Not only that, some hospitals with more mature deployments in place have used EMRs and connected tools to make major changes in how they deliver care.

That being said, the industry is still struggling with many of the same problems it did in a decade ago.

The most obvious example of this is the extent to which health data interoperability efforts have stagnated. While hospitals within a health system typically share data with their sister facilities, I’d argue that efforts to share data with outside organizations have made little material progress.

Another major stagnation point is data analytics. Even organizations that spent hundreds of millions of dollars on their EMR are still struggling to squeeze the full value of this data out of their systems. I’m not suggesting that we’ve made no progress on this issue (certainly, many of the best-funded, most innovative systems are getting there), but such successes are still far from common.

Over the longer-term, I suspect the shifts in consciousness fostered by EMRs and digital health will gradually reshape the industry. But don’t expect those technology lightning bolts to speed up the evolution of hospital IT. It’s going take some time for that giant ship to turn.

We Can’t Afford To Be Vague About Population Health Challenges

Posted on June 19, 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.

Today, I looked over a recent press release from Black Book Research touting its conclusions on the role of EMR vendors in the population health technology market. Buried in the release were some observations by Alan Hutchison, vice president of Connect & Population Health at Epic.

As part of the text, the release observes that “the shift from quantity-based healthcare to quality-based patient-centric care is clearly the impetus” for population health technology demand. This sets up some thoughts from Hutchison.

The Epic exec’s quote rambles a bit, but in summary, he argues that existing systems are geared to tracking units of care under fee-for-service reimbursement schemes, which makes them dinosaurs.

And what’s the solution to this problem? Why, health systems need to invest in new (Epic) technology geared to tracking patients across their path of care. “Single-solution systems and systems built through acquisition [are] less able to effectively understand the total cost of care and where the greatest opportunities are to reduce variation, improve outcomes and lower costs,” Hutchison says.

Yes, I know that press releases generally summarize things in broad terms, but these words are particularly self-serving and empty, mashing together hot air and jargon into an unappetizing patty. Not only that, I see a little bit too much of stating as fact things which are clearly up for grabs.

Let’s break some of these issues down, shall we?

  • First, I call shenanigans on the notion that the shift to “value-based care” means that providers will deliver quality care over quantity. If nothing else, the shifts in our system can’t be described so easily. Yeah, I know, don’t expect much from a press release, but words matter.
  • Second, though I’m not surprised Hutchison made the argument, I challenge the notion that you must invest in entirely new systems to manage population health.
  • Also, nobody is mentioning that while buying a new system to manage pop health data may be cleaner in some respects, it could make it more difficult to integrate existing data. Having to do that undercuts the value of the new system, and may even overshadow those benefits.

I don’t know about you, but I’m pretty tired of reading low-calorie vendor quotes about the misty future of population health technology, particularly when a vendor rep claims to have The Answer.  And I’m done with seeing clichéd generalizations about value-based care pass for insight.

Actually, I get a lot more out of analyses that break down what we *don’t* know about the future of population health management.

I want to know what hasn’t worked in transitioning to value-based reimbursement. I hope to see stories describing how health systems identified their care management weaknesses. And I definitely want to find out what worries senior executives about supporting necessary changes to their care delivery models.

It’s time to admit that we don’t yet know how this population health management thing is going to work and abandon the use of terminally vague generalizations. After all, once we do, we can focus on the answering our toughest questions — and that’s when we’ll begin to make real progress.

Real-Time Health Systems (RTHS) and Experiential Wayfinding

Posted on May 19, 2017 I Written By

The following is a guest blog post by Jody Shaffer from Jibestream.

You have probably heard about Real-Time Health Systems (RTHS). This is a game-changing trend among healthcare providers where the delivery of healthcare is transforming to a more aware and patient-centric system. Providers are leveraging technology to get pertinent information to decision makers as quickly as possible empowering them to make more informed decisions in real-time. Facilities that are amenable to change will remain strong in competitive markets, while those who are reluctant to adapt will fall behind.

As we entered this new era in healthcare, providers are faced with a series of challenges. Smart medical devices are transforming the healthcare dynamic as medical data and information is produced and multiplying at an exponential rate, yet it’s use has not been keeping pace. This data overload has created a significant obstacle for healthcare providers to overcome. There is also intense pressure to create a consumer and patient experience that is dynamic, accessible and engaging.

So the question is, how can healthcare providers quickly process and interpret copious amounts of data into a digestible format for immediate patient consumption while internalizing and translating the same data into operational intelligence?

The answer lies in evolving to a paradigm that is situationally aware and patient-centric in both operations and management. Not only is this pivotal in successfully achieving a RTHS, it ensures that healthcare providers connect, communicate and collaborate more effectively than they have in the past.

When looking to achieve a Real-Time Healthcare System, there are four primary phases that need to be addressed:

Phase 1 – Collecting data

Phase 2 – Processing data

Phase 3 – Translating data into intelligence

Phase 4 – Utilizing/sharing data

The final two phases are essential for healthcare providers to excel in this changing market dynamic and meet increasing patient expectations.

To yield valuable intelligence, data needs to be presented with situational context. Raw data is in itself useful for analytics, but can only be leveraged to create spatial awareness when augmented with location-based data.

Consumers have grown accustomed to the convenience of real-time access to information from mobile devices and apps, and healthcare is no exception. Through a combination of location-aware technologies, hospitals can eliminate some of patient’s biggest frustrations fostering a more positive patient experience across the continuum of care.

Mobile apps, digital maps and interactive kiosks leverage connected technologies to help create a more familiar and engaging environment promoting an effortless and seamless patient experience.

Experiential wayfinding, made available through these technologies, form the foundation for enhancing patient experience, which is paramount to the success of a healthcare organization. Experiential wayfinding reduces the complexity of indoor spaces by anticipating where people are going and what they are looking for. It can be used to direct visitors to a facility and identify parking availability nearest their desired location. Once there, it can be used to guide visitors to destination(s) within a facility using turn-by turn directions making it easy and less stressful to get where they need to go.

An integrated platform can also enable proactive interactions engaging patients before, during, and after hospital visits. The use of mobile messaging to deliver contextual content based on a patient’s location and profile help create a more pleasant and efficient patient experience. Prior to a visitor’s departure to a hospital, the facility’s mobile apps can share information such as appointment delays or traffic delays to take into account on the way there. Mobile messaging also enables facilities to communicate with visitors by sending appointment reminders, context-aware messages, preparation guidelines, post-care instructions, and more. Another application of this can save patients the frustration of intolerable wait-times when a hospital is stretched beyond capacity by sending notifications to offer a change of appointment or alternate appointment location.

Location awareness and spatial context benefit both patients and healthcare providers alike. For clinicians and healthcare teams, this translates to accelerated productivity facilitated through visibility, the streamlining of processes resulting in the elimination of inefficiencies, minimizing staff interruptions, and a balance between resources and demand.

When managed properly, a RTHS enables healthcare providers to improve patient satisfaction and outcomes by leveraging the vast amount of data made available through connected computers, technologies and medical equipment across hospitals, clinics, and patient homes.

By merging the location dimension into healthcare systems, providers are able to bring order to complex data. Through geoenrichment and data visualization, providers can improve patient experiences and outcomes, uncover previously unseen data patterns, realize workflow efficiencies through connected technologies and enrich business insights leading to better more actionable decisions.

Behind the Scenes: Preparing for a RTHS Transition

  • Digitization of Space (converting CAD/DWG map files to SVG)
    Before data can be presented in the context of a map, healthcare providers need to digitize their space. This provides a scalable platform for plotting data to support multiple use cases.
  • Connect core systems and data
    Leveraging technology that offers interoperability allows for seamless integration of core systems and data
  • Connect assets and people
    Create situational awareness by connecting to assets and people
  • Connect maps to data with Indoor Positioning Systems (IPS)
    Look for a solution that offer a technology agnostic architecture to calibrate maps Indoor Positioning
  • Implementation
    Make all this available by extending solution to patient and nonpatient hospital workflows

About Jody Shaffer
Jody Shaffer is an experienced marketer with more than 13 years in the software industry. Jody currently leads the marketing department at Jibestream, an award-winning company specializing in indoor mapping and location intelligence solutions. The company’s platform provides developers with the tools to build custom map-enabled applications unlocking the full potential of the Internet of Things (IoT). Jibestream’s platform can be found implemented in hospitals and health care facilities across north America.

Healthcare Analytics are the Problem. Applied AI is the Solution.

Posted on May 17, 2017 I Written By

The following is a guest blog post by Gurjeet Singh, Executive Chairman and Co-founder of Ayasdi.

The combination of electronic medical records, financial data, clinical data, and advanced analytics promised to revolutionize healthcare.

It hasn’t happened.

The common excuse is that healthcare wasn’t really prepared for the enormity and complexity of the data challenge and that, over time, with the next EMR implementation, that healthcare will be positioned to reap the benefits. Unfortunately, the next generation of EMR, or the one after that, isn’t going to solve the problem.

They problem is on the analytics side.

Healthcare analytics are still driven by a question-first approach. The start of our analytics journey still begins with the question.  The challenge is which question? The more data we have at our disposal, the more potential questions there are and the lower the likelihood that we will ask the one that generates new value for the patient, the provider, or the payer. Even when we are successful in asking the right question, we have engaged in a confirmatory process – we have confirmed something we already knew.

Some will suggest that predictive analytics solves the problem, but it too is hypothesis driven – just in a different way. With predictive analytics, the set of variables selected, the choice of algorithms are, in effect, guesses as to what will produce the best outcome.

Ultimately, both approaches are flawed.

We need a new approach that surfaces trends we humans haven’t even considered, and that delivers a host of meaningful insights to clinicians before they even ask any questions. We need technology solutions that combine the best qualities of human intelligence (artificial intelligence) with the best computing capabilities that exceed human ability (machine learning).  When these technologies are operationalized systematically across an enterprise, it’s called Applied AI.  Applied AI is here to replace healthcare analytics, and we all stand to benefit.

Five Keys to Applied AI

Applied AI has already begun driving care improvement, cost-reduction, and improved clinical and financial decision-making across the healthcare enterprise – and the entire healthcare continuum. Applied AI is not a concept, but a series of intelligent applications that target discrete healthcare problems from clinical variation to population health. These intelligent applications have a collection of capabilities that make them intelligent – of which all need to be present. Let’s look at those capabilities:

DiscoveryIntelligent applications need to support both unsupervised and semi-supervised discovery. These capabilities are quite rare but serve as the foundation for our efforts to move past hypothesis driven inquiry. In practical terms, this means that an intelligent application considers all the data and all the possibilities within that data to detect the patterns, groups or anomalies that elude traditional approaches. Using their own systems of records, including EMRs, financial data, patient-generated data, and socio-economic data, healthcare organizations can automatically discover groups of patients that share unique combinations of characteristics. These groups can then be used to tailor and personalize diagnostics and care paths, for example. Alternatively, healthcare organizations may also discover unique patterns or outliers within their claims data to aid in member retention or preventing fraud or waste. This type of holistic discovery is unique to AI and improves prediction and makes operational insights possible.

Predictions Intelligent applications must also be able to predict the future with high accuracy. Holistic discovery enables even better predictive models through the unbiased creation of groups or the identification of patterns. Superior prediction gives healthcare organizations foresight into the future needs, costs, disease burden, and risks of patients. For example, intelligent applications can determine the groups of patients projected to have the highest escalation of costs over time, as well as other outcomes such as the conditions likely to appear for each group, and an individual’s predicted change in utilization. Predictions can be made across multiple targets and are multi-faceted, considering all factors whether they’re health- or non-healthcare-related occurring outside of the healthcare system.

JustificationAn intelligent solution must justify its predictions, discoveries, and actions in a transparent way so human operators feel confident to act upon its recommendations. For example, a healthcare app may reveal differentiating characteristics of patient risk trajectories, what factors make them high or low-risk, and descriptions of individual factors that lead to variation in cost and quality. Justification is key because without a thorough understanding of the “why” behind predictions, organizations are unable to adopt AI into day-to-day decision-making.

Action An intelligent system that is not effectively operationalized will become less intelligent over time. Actionable information that guides and augments human decision-making is what makes AI a part of daily operations. For these systems to deliver optimal value they need humans in the loop providing feedback and governance. Whether it be a recommended care path or a detailed risk profile, intelligent applications allow organizations to collaborate on the best actions tailored for each patient population, or to physicians or organizations. Across the care continuum, within health systems and health plans, this allows them to better assess individuals and the best course of care, and more confidently prescribe care and programs for each individual.

LearningIntelligent applications “learn” to improve predictions over time. As more and more data is analyzed, the technology learns from these complex data points to improve predictions over time. Whether it be claims, medical records, or socio-economic data, AI taps into these data points to generate more accurate, personalized predictions that continuously improve. Further, intelligent apps learn the impact of actions over time to support and continuously improve decision making.

Applied AI in action

A large hospital system decided it wanted to reduce clinical variation across its enterprise to improve outcomes for all patients. It implemented machine intelligence, including unsupervised machine learning techniques that run algorithms using the system’s own data—not benchmarks—to uncover actionable insights. The technology correlates and analyzes electronic medical record and financial data including treatments prescribed, procedures performed, drugs administered, length of stay, and costs per patient. The goal was to discover and refine clinical pathways that are optimized to drive higher quality of care and lower costs.

The machine intelligence solution identified a group of orthopedic surgeons who consistently had better outcomes among their knee replacement patients. These patients had shorter hospital stays and shorter time to ambulation than other total knee surgery replacements across the system. The solution also told clinicians why:  these doctors prescribed a unique, not widely used medication at an earlier postsurgical time than their peers. The medication reduced patients’ pain so they could get out of bed and walk around sooner – improving their outcomes and reducing costs.

Clinicians hadn’t previously known to look for variation based on what medication was given post-operatively. But machine intelligence identified a pod of doctors with better outcomes that were statistically significant. By comparing very large numbers of data points, the solution quickly uncovered why.  Now the hospital system has operationalized these best practices throughout their hospitals, lowering costs for knee replacement by more than 5 percent, and reducing pain for patients.

The last piece of the puzzle – AI applications

As healthcare organizations increasingly see the value of Applied AI, they may worry that more robust technology means greatly increased technical headcount to manage this strategy. But an important component of a successful Applied AI strategy is that it leverages the unique capabilities of both machines and humans. Hiring a dozen data scientists won’t make the most of the human intelligence within your organization. That’s because these new data scientists likely would not have the subject matter expertise needed to recognize and deploy the meaningful insights that surface. Meanwhile, the people who are the best suited to learn from the data, domain experts, usually do not have an interface to read data themselves. Subject matter experts typically only interact with data using rudimentary applications like PowerPoint or Excel.

So, the last key to a successful Applied AI strategy is to wrap the results of machine learning and artificial intelligence into business-facing applications. These applications can be customized for the types of insights they uncover, such as the optimal way to perform surgical procedures. It’s critical that the results of machine learning and machine intelligence actually make it to clinicians, instead of ending up siloed somewhere in the IT department. The successor technology to healthcare analytics must not only be more powerful and more precise, it must also be more user-friendly.

What’s Next

Healthcare analytics simply aren’t living up to their promise. We can wring our hands, we can wait, we can soldier on with insights that only marginally move the needle to improve outcomes and lower costs. Or we can combine artificial intelligence with powerful machine learning to turn enormous datasets into business insights that really matter. Then we can deliver those insights, via easy-to-use business applications, to the best clinician minds, to operationalize this machine intelligence approach across the enterprise. That’s Applied AI, and it’s a bright future.

About Gurjeet Singh
Gurjeet Singh is Ayasdi’s Executive Chairman and co-founder. As the Executive Chairman, he leads a technology movement that emphasizes the importance of extracting insight from data, not just storing and organizing it.

Gurjeet developed key mathematical and machine learning algorithms for Topological Data Analysis (TDA) and their applications during his tenure as graduate student in Stanford’s Mathematics Department where he was advised by Ayasdi co-founder Prof. Gunnar Carlsson.

Gurjeet is the author of numerous patents and has published in a variety of top mathematics and computer science journals. Before starting Ayasdi, he worked at Google and Texas Instruments. Gurjeet was named by Silicon Valley Business Journal as one of their 40 Under 40 in 2015.

Gurjeet holds a B.Tech. from Delhi University, and a Ph.D. in Computational Mathematics from Stanford University. He lives in Palo Alto with his wife and two children, and develops multi-legged robots in his spare time.

E-Patient Update: Before You Call Me A “Frequent Flier,” Check Your EMR

Posted on April 28, 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.

While there’s some debate about what constitutes an emergency, there’s no doubt I’ve had a bunch of ambiguous, potentially symptoms lately that needed to be addressed promptly. Unfortunately, that’s exposed me to providers brainwashed to believe that anyone who comes to the emergency department regularly is a problem.

Not only is that irritating, and sometimes intimidating, it’s easy to fix. If medical providers were to just dig a bit further into my existing records – or ideally, do a sophisticated analysis of my health history – they’d understand my behavior, and perhaps even provide more effective care.

If they looked at the context their big ‘ol EMR could provide, they wouldn’t waste time wondering whether I’m overreacting or wasting their time.

As I see it, slapping the “frequent flier” label on patients is particularly inappropriate when they have enough data on hand to know better. (Actually, the American College of Emergency Physicians notes that a very small number of frequent ED visitors are actually homeless, drug seekers or mentally ill, all of which is in play when you show up a bit often. But that’s a topic for another time.)

Taking no chances

The truth is, I’ve only been hitting the ED of late because I’ve been responding to issues that are truly concerning, or doing what my primary doctor or HMO nurse line suggests.

For example, my primary care doctor routed me straight to the local emergency department for a Doppler when my calves swelled abruptly, as I had a DVT episode and subsequent pulmonary embolism just six months ago.

More recently, when I had a sudden right-sided facial droop, I wasn’t going to wait around and see if it was caused by a stroke. It turns out that I probably had an atypical onset of Bell’s Palsy, but there was no way I was going to try and sort that out on my own.

And given that I have a very strong history of family members dropping dead of MI, I wasn’t going to fool around when I felt breathless, my heart was racing and I my chest ached. Panic attack, you’re thinking? No, as it turned out that like my mother, I had aFib. Once again, I don’t have a lab or imaging equipment in my apartment – and my PCP doesn’t either – so I think I did the right thing.

The truth is, in each case I’d probably have been OK, but I erred on the side of caution. You know what? I don’t want to die needlessly or sustain major injuries to prove I’m no wimp.

The whole picture

Nonetheless, having been to the ED pretty regularly of late, I still encounter clinicians that wonder if I’m a malingerer, an attention seeker or a hypochondriac. I pick up just a hint of condescension, a sense of being delicately patronized from both clinicians and staffer who think I’m nuts. It’s subtle, but I know it’s there.

Now, if these folks kept up with their industry, they might have read the following, from Health Affairs. The article in question notes that “the overwhelming majority of frequent [ED} users have only episodic periods of high ED use, instead of consistent use over multiple years.” Yup, that’s me.

If they weren’t so prone to judging me and my choices – OK, not everyone but certainly some – it might occur to them to leverage my data. Hey, if I’m being screened but in no deep distress, why not ask what my wearable or health app data has told me of late? More importantly, why haven’t the IT folks at this otherwise excellent hospital equipped providers with even basic filters the ED treatment team can use to spot larger patterns? (Yeah, bringing big data analytics into today’s mix might be a stretch, but still, where are they?)

Don’t get me wrong. I understand that it’s hard to break long-established patterns, change attitudes and integrate any form of analytics into the extremely complex ED workflow. But as I see it, there’s no excuse to just ignore these problems. Soon, the day will come when on-the-spot analytics is the minimum professional requirement for treating ED patients, so confront the problem now.

Oh, and by the way, treat me with more respect, OK?

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.