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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.

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!

Diving Into Population Health

Posted on April 21, 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.

Population Health is a nebulous term that seems to be applied a lot of different directions. To get a better understanding of what’s happening with Population Health, Healthcare Scene sat down with Arthur Kapoor, President and CEO of HealthEC. HealthEC has been working in healthcare and the population health space for more than 24 years, so they have an interesting perspective on how that space has evolved over the years and where we are today.

You can watch the full video embedded below, or skip to any of the following population health topics we discussed with Arthur:

Utilizing data to understand and better serve populations is only going to become more important in healthcare. A big thanks to Arthur for sharing his insights with us.

If you liked this video, be sure to subscribe to Healthcare Scene on YouTube and watch other Healthcare Scene interviews.

EHR Implementation Accomplished – What’s Next?

Posted on April 12, 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.

When you look at the world of hospital and health system EHR implementations, it’s fair to say that we can say Mission Accomplished. Depending on which numbers you use, they are all in the range of about 90% EHR adoption in hospitals. That’s a big shift from even 5-10 years ago when it comes to EHR adoption in hospitals. It’s amazing how quickly it shifted.

While it’s easy to sit back and think “Mission Accomplished” the reality is that we still have a LONG way to go when it comes to how we use the EHR. Yes, it’s “Mission Accomplished” as far as getting EHRs implemented. However, it’s just the start of the mission to make EHRs useful. I’d suggest that this is the task that will take up CIOs time the most over the next 5 years.

I think that most people looking at their EHR think about next steps in two large baskets:EHR Optimization and Extracting Value from EHR Data.

EHR Optimization
Most EHR software was slammed in so quickly that it left the users’ heads spinning. Hospitals were chasing the government money and so there was no time to think how the EHR was implemented and the best way to implement the EHR. We’re paying the price for these rushed EHR implementations now.

What’s most shocking to me is how many little things can be done for EHR end users to make their lives better. Many EHR users are suffering from poor training, lack of training, or at least an ignorance to what’s possible with the EHR. I’ve seen this first hand in the EHR implementations I’ve done. I know very clearly that a feature of the EHR was introduced and the users were shown how to do it and 6 months later when you show that feature to them they ask “Why didn’t you teach us this earlier?” Although, they then usually sheepishly say, “Did you teach us this before? I don’t remember it.” At this point it’s not about who we blame, but is about ensuring that every user is trained to the highest degree possible.

The other EHR optimization that many need is an evaluation of their EHR workflow. In most EHR implementations the organization replicates the paper processes. This is often not ideal. Now that the EHR is implemented, it’s a great time to think about why a process was done a certain way and see if there is a different workflow that makes more sense in the digital world. It’s amazing the efficiency you will find.

Extracting Value from EHR Data
As I just suggested, most EHR implementations end up being paper processes replicated electronically. This is not a bad thing, but it can often miss out on the potential value an EHR can provide. This is particularly true when it comes to how you use your EHR data. Most hospitals are still using EHR data the way they did in the paper world. We need to change our thinking if we want to extract the value from the EHR data.

I’ve always looked at EHR data like it was discovering a new world. Reports and analysis that were not even possible in the paper world now become so basic and obvious. The challenge often isn’t the reporting, but the realization that these new opportunities exist. In many cases, we haven’t thought this way and a change in thinking is always a challenge.

When thinking about extracting value from the EHR data, I like to think about it from two perspectives. First, can you provide information at the point of care that will make the patient care experience better for the provider and the patient? Second, can you use the EHR data to better understand an address the issues of a patient population? I’m sure there are other frames of reference as well, but these are two great places to start.

EHR Optimization and creating value from EHR data is going to be a great thing for everyone involved in healthcare and we’re just at the beginning of this process. I think it’s a huge part of what’s next for EHR. What’s your take? What are your plans for your EHR?

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.

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.

An Approach For Privacy – Protecting Big Data

Posted on February 6, 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.

There’s little doubt that the healthcare industry is zeroing in on some important discoveries as providers and researchers mine collections of clinical and research data. Big data does come with some risks, however, with some observers fearing that aggregated and shared information may breach patient privacy. However, at least one study suggests that patients can be protected without interrupting data collection.

In what it calls a first, a new study appearing in the Journal of the American Medical Informatics Association has demonstrated that protecting the privacy of patients can be done without too much fuss, even when the patient data is pulled into big data stores used for research.

According to the study, a single patient anonymization algorithm can offer a standard level of privacy protection across multiple institutions, even when they are sharing clinical data back and forth. Researchers say that larger clinical datasets can protect patient anonymity without generalizing or suppressing data in a manner which would undermine its use.

To conduct the study, researchers set a privacy adversary out to beat the system. This adversary, who had collected patient diagnoses from a single unspecified clinic visit, was asked to match them to a record in a de-identified research dataset known to include the patient. To conduct the study, researchers used data from Vanderbilt University Medical Center, Northwestern Memorial Hospital in Chicago and Marshfield Clinic.

The researchers knew that according to prior studies, the more data associated with each de-identified record, and the more complex and diverse the patient’s problems, the more likely it was that their information would stick out from the crowd. And that would typically force managers to generalize or suppress data to protect patient anonymity.

In this case, the team hoped to find out how much generalization and suppression would be necessary to protect identities found within the three institutions’ data, and after, whether the protected data would ultimately be of any use to future researchers.

The team processed relatively small datasets from each institution representing patients in a multi-site genotype-disease association study; larger datasets to represent patients in the three institutions’ bank of de-identified DNA samples; and large sets which stood in for each’s EMR population.

Using the algorithm they developed, the team found that most of the data’s value was preserved despite the occasional need for generalization and suppression. On average, 12.8% of diagnosis codes needed generalization; the medium-sized biobank models saw only 4% of codes needing generalization; and among the large databases representing EMR populations, only 0.4% needed generalization and no codes required suppression.

More work like this is clearly needed as the demand for large-scale clinical, genomic and transactional datasets grows. But in the meantime, this seems to be good news for budding big data research efforts.