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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.

Healthcare Robots for Elderly – Triumph or Tragedy?

Posted on August 21, 2017 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He is currently an independent marketing consultant working with leading healthIT companies. Colin is a member of #TheWalkingGallery. His Twitter handle is: @Colin_Hung.

When most people talk about robots in healthcare they often are referring to the self-guided machines that help deliver medications, food and other items to patient rooms.

Robots of this type can be very helpful and alleviate some of the mechanical tasks on overburdened staff. There is, however, another type of robot that is making in-roads in healthcare – companion robots like Softbank’s Pepper and Intelligent Systems Research’s PARO.

Instead of performing a physical action, the aim of these robots is to serve as patient companions. PARO, for example, has been used extensively in Japan with patients suffering from Alzheimer’s and Dementia – where it has helped reduce wandering, agitation, depression and loneliness. Below is a short video from Alzheimer’s Australia about PARO.

From a technology standpoint this is an amazing triumph. A machine providing emotional support to a patient was the stuff of Science Fiction a decade ago. At the same time, however, do these robots represent a failure of society? Does the fact that these robots exist demonstrate that we would rather delegate human contact with elderly patients to machines rather than go ourselves?

The #hcldr community debated this topic on a recent tweetchat.

A manual analysis of the tweets shows that approximately 80% of the community saw robot companions as a positive development. Most people, like Grace Cordova, pointed to the fact that our aging population already outstrips the existing infrastructure so why shouldn’t we invest in robots to help us manage (as a society).

Some responded with tempered positivity. Jon McBride saw the potential in robots but cautioned against relying on them solely for human companionship.

Many echoed Jon’s sentiment.


Personally I see robots as an innovative solution to addressing a problem that already exists – the lack of human interaction with elderly patients. I believe we have to admit to ourselves that staff are stretched thin in elder-care facilities and there are long stretches where patients are on their own. If those hours can be filled by interacting with a robot companion that responds in a human or animal-like way…I’m all for it.

What are your thoughts?

Talking Secure Healthcare Communication with Telmediq Founder and CEO

Posted on June 9, 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.

I’ve had a keen interest in the secure text message space ever since I started advising a company in the space many years ago. That company has since been acquired, but I’ve still been keeping watch over the secure text message market. Even back in the early days, we knew that the real holy grail of secure text was to integrate with the EHR and other applications and become a full communication suite and not just a simple text message platform. However, it would take time to really get there. What’s exciting is that we’re starting to see companies that are finally getting there.

One company that’s been making great progress in this direction is a company called Telmediq. Unlike most secure text message companies who started with the physicians, Telmediq approached the secure healthcare communication problem initially from the perspective of nurses. This together with a number of their integrations with EHR and other hospital IT systems prompted me to sit down with Ben Moore, Founder and CEO at Telmediq to learn more about their company and the evolving healthcare communication market.

If you’ve never heard about Telmediq or if you’re interested in what’s happening in the healthcare communication space now and where it’s heading in the future, then you’ll enjoy our interview with Ben Moore. We cover a lot of ground including things like EHR integration, voice integration, alert fatigue, hands free communication, and future items we’re just starting to see like AI and chatbots.

Enjoy our interview with Ben Moore, Founder and CEO at Telmediq:

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.