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Healthcare Execs See New Digital Health Technologies As Critical To Success

Posted on October 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.

Healthcare organizations have spent massively on HIT in recent years, in hopes of preparing for success by building next-generation tech infrastructure.  If a new survey is any indication, while the current set of efforts haven’t born as much fruit as their leaders like, they remain hopeful that the next wave will better support their goals.

The SAP Digital Transformation Executive Study, which surveyed about 400 healthcare executives, looked at whether the healthcare industry was prepared for the digital economy.

Respondents told SAP (and survey partner Oxford Economics) that the existing technology investments weren’t delivering the value they wanted, with only 22% saying they supported customer satisfaction efforts and 23% saying that they helped foster innovation.

Fortunately for health IT vendors, however, that wasn’t the whole story. Perhaps because hope springs eternal, healthcare leaders predicted that in two years thing should look different.

In fact, 70% said that the latest technologies were essential to growth, competitive advantage and customer experience. In two years, 61% expect technology investments to boost customer satisfaction, and 59% believe the technologies will help support innovation.

This may be, at least in part, because many healthcare organizations are in the process of kicking off digital transformation efforts and are relying on new technologies to achieve their goals. Though the process hasn’t advanced too far in many organizations, respondents all seem to be making some progress.

According to the survey, healthcare execs expect the importance of digital transformation to climb over the next several years. While 61% said it’s important today, 79% expect it to be important in two years and 86% believe that it will be important in five years.

To prepare for these eventualities, 23% of respondents said are planning digital transformation initiatives and 54% are piloting these approaches. In addition, 32% reported that their efforts were complete in some areas and 2% said their process was complete in all areas. Almost half (48%) said a lack of mature technology was holding back their efforts.

When asked to name the technologies they expected to use, 76% of healthcare leaders predicted that big data and analytics will help them transform their business. They also named cloud computing (65%), IoT technologies (46%) and AI (28%) as tools likely to foster digital transformation process.

I don’t know about you, but personally, I’d be pretty upset if I’d spent tens or hundreds of millions of dollars on this wave of health IT and felt that I’d gotten little value out of it. And given that history, I’d be reluctant to make any new investments until I was confident things play out differently this time.

Under these circumstances, it’s not surprising that healthcare execs are taking their time with implementing digital transformation, as important as this process may be. With any luck, the next wave of digital technology will be more flexible and offer greater ROI than the previous generation.

Predictive Analytics Will Save Hospitals, Not IT Investment

Posted on October 27, 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.

Most hospitals run on very slim operating margins. In fact, not-for-profit hospitals’ mean operating margins fell from 3.4% in fiscal year 2015 to 2.7% in fiscal year 2016, according to Moody’s Investors Service.

To turn this around, many seem to be pinning their hopes on better technology, spending between 25% and 35% of their capital budget on IT infrastructure investment. But that strategy might backfire, suggests an article appearing in the Harvard Business Review.

Author Sanjeev Agrawal, who serves as president of healthcare and chief marketing officer at healthcare predictive analytics company LeanTaaS, argues that throwing more money at IT won’t help hospitals become more profitable. “Healthcare providers can’t keep spending their way out of trouble by investing in more and more infrastructure,” he writes. “Instead, they must optimize the use of the assets currently in place.”

Instead, he suggests, hospitals need to go the way of retail, transportation and airlines, industries which also manage complex operations and work on narrow margins. Those industries have improved their performance by improving their data science capabilities.

“[Hospitals] need to create an operational ‘air traffic control’ for their hospitals — a centralized command-and-control capability that is predictive, learns continually, and uses optimization algorithms and artificial intelligence to deliver prescriptive recommendations throughout the system,” Agrawal says.

Agrawal predicts that hospitals will use predictive analytics to refine their key care-delivery processes, including resource utilization, staff schedules, and patient admits and discharges. If they get it right, they’ll meet many of their goals, including better patient throughput, lower costs and more efficient asset utilization.

For example, he notes, hospitals can optimize OR utilization, which brings in 65% of revenue at most hospitals. Rather than relying on current block-scheduling techniques, which have been proven to be inefficient, hospitals can use predictive analytics and mobile apps to give surgeons more control of OR scheduling.

Another area ripe for process improvements is the emergency department. As Agrawal notes, hospitals can avoid bottlenecks by using analytics to define the most efficient order for ED activities. Not only can this improve hospital finances, it can improve patient satisfaction, he says.

Of course, Agrawal works for a predictive analytics vendor, which makes him more than a little bit biased. But on the other hand, I doubt any of us would disagree that adopting predictive analytics strategies is the next frontier for hospitals.

After all, having spent many billions collectively to implement EMRs, hospitals have created enormous data stores, and few would argue that it’s high time to leverage them. For example, if they want to adopt population health management – and it’s a question of when, not if — they’ve got to use these tools to reduce outcome variations and improve quality of cost across populations. Also, while the deep-pocketed hospitals are doing it first, it seems likely that over time, virtually every hospital will use EMR data to streamline operations as well.

The question is, will vendors like LeanTaaS take a leading role in this transition, or will hospital IT leaders know what they want to do?  At this stage, it’s anyone’s guess.

Predictive Analytics with Andy Bartley from Intel

Posted on September 20, 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.

#Paid content sponsored by Intel.

In the latest Healthcare Scene video interview, I talk with Andy Bartley, Senior Solutions Architect in the Health and Life Sciences Group at Intel. Andy and I talk about the benefits of and challenges to using predictive analytics in healthcare.

Andy offers some great insights on the subject, having had a long and varied career in the industry. Before joining Intel, he served in multiple healthcare organizations, including nurse communication and scheduling application startup NurseGrid, primary care practice One Medical Group and medical device manufacturer Stryker.

In my interview, he provides a perspective on what hospitals and health systems should be doing to leverage predictive analytics to improve care and outcomes, even if they don’t have a massive budget. Plus, he talks about predictive analytics that are already happening today.

Here are the list of questions I asked him if you’d like to skip to a specific topic in the video. Otherwise, you can watch the full video interview in the embedded video at the bottom of this post:

What are your thoughts on predictive analytics? How is it changing healthcare as we know it? What examples have you seen of effective predictive analytics? We look forward to seeing your thoughts in the comments and on social media.

Open Source Tool Offers “Synthetic” Patients For Hospital Big Data Projects

Posted on September 13, 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 readers will know, using big data in healthcare comes with a host of security and privacy problems, many of which are thorny.

For one thing, the more patient data you accumulate, the bigger the disaster when and if the database is hacked. Another important concern is that if you decide to share the data, there’s always the chance that your partner will use it inappropriately, violating the terms of whatever consent to disclose you had in mind. Then, there’s the issue of working with incomplete or corrupted data which, if extensive enough, can interfere with your analysis or even lead to inaccurate results.

But now, there may be a realistic alternative, one which allows you to experiment with big data models without taking all of these risks. A unique software project is underway which gives healthcare organizations a chance to scope out big data projects without using real patient data.

The software, Synthea, is an open source synthetic patient generator that models the medical history of synthetic patients. It seems to have been built by The MITRE Corporation, a not-for-profit research and development organization sponsored by the U.S. federal government. (This page offers a list of other open source projects in which MITRE is or has been involved.)

Synthea is built on a Generic Module Framework which allows it to model varied diseases and conditions that play a role in the medical history of these patients. The Synthea modules create synthetic patients using not only clinical data, but also real-world statistics collected by agencies like the CDC and NIH. MITRE kicked off the project using models based on the top ten reasons patients see primary care physicians and the top ten conditions that shorten years of life.

Its makers were so thorough that each patient’s medical experiences are simulated independently from their “birth” to the present day. The profiles include a full medical history, which includes medication lists, allergies, physician encounters and social determinants of health. The data can be shared using C-CDA, HL7 FHIR, CSV and other formats.

On its site, MITRE says its intent in creating Synthea is to provide “high-quality, synthetic, realistic but not real patient data and associated health records covering every aspect of healthcare.” As MITRE notes, having a batch of synthetic patient data on hand can be pretty, well, handy in evaluating new treatment models, care management systems, clinical support tools and more. It’s also a convenient way to predict the impact of public health decisions quickly.

This is such a good idea that I’m surprised nobody else has done something comparable. (Well, at least as far as I know no one has.) Not only that, it’s great to see the software being made available freely via the open source distribution model.

Of course, in the final analysis, healthcare organizations want to work with their own data, not synthetic substitutes. But at least in some cases, Synthea may offer hospitals and health systems a nice head start.

A New Hospital Risk-Adjustment Model

Posted on August 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.

Virtually all of the risk adjustment models with which I’m familiar are based on retrospective data. This data clearly has some predictive benefits – maybe it’s too cliché to say the past is prologue – and is already in our hands.

To look at just one example of what existing data archives can do, we need go no further than the pages of this blog. Late last year, I shared the story of a group of French hospitals which are working to predict admission rates as much as 15 days in advance by mining a store of historical data. Not surprisingly, the group’s key data includes 10 years’ worth of admission records.

The thing is, using historical data may not be as helpful when you’re trying to develop risk-adjustment models. After all, among other problems, the metrics by which evaluate care shift over time, and our understanding of disease states changes as well, so using such models to improve care and outcomes has its limitations.

I’ve been thinking about these issues since John shared some information on a risk-adjustment tool which leverages relevant patient care data collected almost in real time.

The Midas Hospital Risk Adjustment Model, which is created specifically for single organizations, samples anywhere from 20 to 600 metrics, which can include data on mortality, hospital-acquired complications, unplanned readmission, lengths of stay and charges. It’s built using the Midas Health Analytics Platform, which comes from a group within healthcare services company Conduent. The platform captures data across hospital functional areas and aggregates it for use in care management

The Midas team chooses what metrics to include using its in-house tools, which include a data warehouse populated with records on more than 100 million claims as well as data from more than 800 hospitals.

What makes the Midas model special, Conduent says, is that it incorporates a near-time feed of health data from hospital information systems. One of the key advantages to doing so is that rather than basing its analysis on ICD-9 data, which was in use until relatively recently, it can leverage clinically-detailed ICD-10 data, the company says.

The result of this process is a model which is far more capable of isolating small but meaningful differences between individual patients, Conduent says. Then, using this model, hospitals risk-adjust clinical and financial outcomes data by provider for hospitalized patients, and hopefully, have a better basis for making future decisions.

This approach sounds desirable (though I don’t know if it’s actually new). We probably need to move in the direction of using fresh data when analyzing care trends. I suspect few hospitals or health system would have the resources to take this on today, but it’s something to consider.

Still, I’d want to know two things before digging into Midas further. First, while the idea sounds good, is there evidence to suggest that collecting recent data offers superior clinical results? And in that vein, how much of an improvement does it offer relative to analysis of historical data? Until we know these things, it’s hard to tell what we’ve got here.

Hospital CIOs Still Think Outcomes Improvement Is The Best Use Of EMR Data

Posted on August 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.

Sure, there might be a lot of ways to leverage data found within EMRs, but outcomes improvement is still king. This is one of the standout conclusions from a recently-released survey of CHIME CIOs, sponsored by the trade group and industry vendor LeanTaaS, in which the two asked hospital CIOs five questions about their perceptions about the impact of EMR data use in growing operating margins and revenue.

I don’t know about you, but I wasn’t surprised to read that 24% of respondents felt that improving clinical outcomes was the most effective use of their EMR data. Hey, why else would their organizations have spent so much money on EMRs in the first place?  (Ok, that’s probably a better question than I’ve made it out to be.)

Ten percent of respondents said that increasing operational efficiencies was the best use of EMR data, an idea which is worth exploring further, but the study didn’t offer a whole lot of additional detail on their thought process. Meanwhile, 6% said that lowering readmissions was the most effective use of EMR data, and 2% felt that its highest use was reducing unnecessary admissions. (FWIW, the press release covering the survey suggested that the growth in value-based payment should’ve pushed the “reducing  readmissions” number higher, but I think that’s oversimplifying things.)

In addition to looking at EMR data benefits, the study looked at other factors that had an impact on revenue and margins. For example, respondents said that reducing labor costs (35%) and boosting OR and ED efficiency (27%) would best improve operating margins, followed by 24% who favored optimizing inpatient revenue by increasing access. I think you’d see similar responses from others in the hospital C-suite. After all, it’s hard to argue that labor costs are a big deal.

Meanwhile, 52% of the CIOs said that optimizing equipment use was the best approach for building revenue, followed by optimizing OR use (40%). Forty-five percent of responding CIOs said that OR-related call strategies had the best chance of improving operating margins.

That being said, the CIOs don’t exactly feel free to effect changes on any of these fronts, though their reasons varied.

Fifty-four percent of respondents said that budget limitations the biggest constraint they faced in launching new initiatives, and 33% of respondents said the biggest obstacle was lack of support resources. This was followed by 17% who said that new initiatives were being eclipsed by higher priority projects, 17% said they lacked buy-in from management and 10% who said he lack the infrastructure to pursue new projects.

Are any of these constraints unfamiliar to you, readers? Probably not. Wouldn’t it be nice if we did at least solved these predictable problems and could move on to different stumbling blocks?

Thoughts On Innovation In Healthcare

Posted on June 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.

Sure, innovation can be fun and interesting and energizing. But how do you move from innovation as a sport to innovation as a true growth strategy, especially in a conservative business like healthcare? New research by consulting firm PwC might offer some answers.

To conduct its study, P2C surveyed more than 1,200 executives in 44 countries, conducting in-depth interviews with leaders responsible for managing innovation initiatives.

The research, which cut across multiple industries, found that firms that applied customer-engagement strategies leveraging design thinking and user-driven requirements — from idea generation to a product or service launch — saw better results. In fact, they were twice as likely to expect growth of 15% or more over the next five years, PwC found.

In conducting the research, PwC researchers identified five strategies which contribute to effective innovation efforts. They include:

  • Use smart metrics to measure innovation success: Whatever you invest, if you track the benefits of innovation by how it boosts revenue and contains cost – along with building sales – you’ve likely got a sustainable model. Sixty-nine percent of respondents named sales growth as the most important way to measure innovation success.
  • Don’t make “blind bets” — build viable business initiatives: Make sure you find a way to square your innovation strategy with your business strategy. And be aware that doing so may be challenging. The PwC report notes that 65% of companies investing 15% or more of their revenue in innovation saw connecting innovation with business goals was their top strategic challenge.
  • Create silo-busting innovation models: To succeed at innovation, break down traditional organization barriers within and outside of your organization, which helps you leverage a wider pool of ideas, insights, talents and technology. Consider more-inclusive operating models like open innovation, design thinking and co-creation with partners, customers and supplies rather than traditional R&D. Thirty-five percent of PwC respondents reported that customers were their most important innovation partners.
  • Leverage a broad base of human experience: See to it that your innovation teams seek input from across a variety of disciplines, rather than letting technology drive your process. For example, while big data may help you know how customers behave, data alone won’t explain why they behave that way. It’s better to bring the right human judgment and intuition to bear on the data rather than sticking strictly with IT experts. Sixty percent of companies surveyed said that internal employees help to drive innovation within their organization.
  • Support technical innovation: While technology is far from the only tool you can use to innovate, it remains a compelling option. Many companies looking to technology to create markets for novel products and services that don’t yet exist, and to meet needs that customers may not even know they have. Half of PwC’s respondents rated technology partners as their most important innovation collaborators.

So, what can the healthcare industry learn from this study? A few things come to mind.

For one thing, I believe that healthcare leaders could do far more to turn silo-busting activities into group innovation projects. In other words, don’t just merge data from different departments into a common database, involve the people in those departments with the process, and ask them how breaking down barriers could change the organization in a positive way.

Another thing that comes to mind that healthcare technology leaders could stand to integrate non-technical opinions into innovation efforts. Right now, health IT organizations are remarkably siloed themselves, and while they may involve clinicians in their process at times, it’s rare for them to take in the opinions of non-medical employees who don’t use advanced IT functions very often. (Yes, a janitorial services worker may have something to offer.)

And what about picking the right metrics to measure innovation success? Of course, existing models emphasizing clinical improvement aren’t misguided, nor are measures of IT performance, but there’s more to consider. Particularly within the ecosystem of a large hospital, as many departments outside IT care delivery which contribute to the organization’s overall health.

Ultimately, what makes innovation valuable is the extent to which it draws upon an organization’s unique strengths.  But it never hurts to take broad principles like these into account, as they may help you extract the full benefits of the innovation process.

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.

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?