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Healthcare CIOs Focused On Patient Experience And Innovation

Posted on August 2, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Not long ago, 22 healthcare CIOs had a sit-down to discuss their CEOs’ top IT-related priorities. At the meeting, which took place during the 2018 Scottsdale Institute Annual Conference, the participants found that they were largely on the same page, according to researchers that followed the conversation.

Impact Advisors, which co-sponsored the research, found that improving patient experiences was priority number one. More than 80% of CIOs said patient engagement and better patient experiences were critical, and that deploying digital health strategies could get the job done.

The technologies they cited included patient-facing options like wearables, mobile apps and self-service tools. They also said they were looking at a number of provider-facing solutions which could streamline transitions of care and improve patient flow, including care coordination apps and tools and next-generation decision support technologies such as predictive analytics.

Another issue near the top of the list was controlling IT costs and/or increasing IT value, which was cited by more than 60% of CIOs at the meeting. They noted that in the past, their organizations had invested large amounts of money to purchase, implement and upgrade enterprise EHRs, in an effort to capture Meaningful Use incentive payments, but that things were different now.

Specifically, as their organizations are still recovering from such investments, CIOs said they now need to stretch their IT budgets, They also said that they were being asked to prove that their organization’s existing infrastructure investments, especially their enterprise EHR, continue to demonstrate value. Many said that they are under pressure to prove that IT spending keeps offering a defined return on investment.

Yet another important item on their to-do list was to foster innovation, which was cited by almost 60% of CIOs present. To address this need, some CIOs are launching pilots focused on machine learning and AI, while others are forming partnerships with large employers and influential tech firms. Others are looking into establishing dedicated innovation centers within their organization. Regardless of their approach, the CIOs said, innovation efforts will only work if innovation efforts are structured and governed in a way that helps them meet their organization’s broad strategic goals.

In addition, almost 60% said that they were expected to support their organization’s growth. The CIOs noted that given the constant changes in the industry, they needed to support initiatives such as expansion of service lines or building out new ones, as well as strategic partnerships and acquisitions.

Last, but by no means least, more than half of the CIOs said cybersecurity was important. On the one hand, the participants at the roundtable said, it’s important to be proactive in defending their organization. At the same time, they emphasized that defending their organization involves having the right policies, processes, governance structure and culture.

Healthcare Leaders See AI Tech In Their Future

Posted on July 30, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

You’ve probably noticed that the movement of healthcare AI from visionary to commonplace has already begun. There are endless examples I could cite to demonstrate this, but here’s a taste:

  • A UK hospital is delegating some tasks usually performed by doctors and nurses to AI technology
  • The AMA is working to set standards for physician use of AI
  • Competition between AI-based disease management players is increasing
  • New AI software can detect signs of diabetic retinopathy without involving a physician

Of course, anytime a technology seems poised to take over the world, there’s a voice in our head saying “Are you sure?” And we all know there are many flashes in the technology pan.

When it comes to AI, however, we may be on the brink of such widespread adoption that no one could argue that it hasn’t arrived. According to a recent Intel survey of U.S. healthcare leaders, AI will be in use across the healthcare spectrum by 2023.

The research, which was conducted in partnership with Convergys Analytics, surveyed 200 US healthcare decision-makers in April 2018 on their attitudes about AI. The survey also asked subjects what barriers still existed to industry-wide AI adoption.

First, a significant number of respondents (54%) said that they expected AI to be in wide use in the industry within the next five years. Also, a substantial minority (37%) said they already used AI, though most reported that such use was limited.

Among those organizations that use AI, clinical use accounted for 77%, followed by operational use (41%) and financial use (26%). Meanwhile, respondents whose organizations hadn’t adopted AI still seem very enthusiastic about its possibilities, with 91% expecting that it will offer predictive analytics tools for early intervention, 88% saying it will improve care and 83% saying it will improve the accuracy of medical diagnoses.

Despite their enthusiasm, however, many of those surveyed were sure they could trust AI just yet. More than one-third of respondents said that patients wouldn’t trust AI enough to play an active role in their care (and they are probably right, at least for now). Meanwhile, 30% assume that clinicians wouldn’t trust AI either, predicting that concerns over fatal errors would kill their interest. Again, that’s probably a good guess.

In addition, there’s the issue of the AI “black box” to bear in mind. Though Intel didn’t go into detail on this, both clinicians and healthcare executives are concerned about the way AI gets its job done. My informal research suggests that until doctors and nurses understand how AI tools have made their decisions — and what data influenced these decisions — it will be hard to get them comfortable with it.

Healthcare Execs Investing In Intelligent Technologies Face Roadbumps

Posted on July 16, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

A recent report from Accenture concludes that healthcare executives are enthusiastic about “intelligence technologies” such as AI and IoT. It also suggests, however, that health organizations will need to add new capabilities to be sure they can manage these technologies responsibly.

The report, based on a survey of 100 health executives, found that 77% of respondents expect to invest in IoT and smart sensors and that 53% expect to invest in AI systems.  Presumably, they expect these technologies to offer benefits more quickly.

Why the gap in adoption? The truth is that healthcare leaders haven’t yet gotten their arms around AI just yet. While IoT and smart sensor technology can boost the flexibility and “judgment” of enterprise systems, AI arguably has the potential to be far more flexible and wide-reaching — and ultimately less than predictable.

This unpredictability makes AI investment a bit trickier to implement than other emerging technologies. Just over four-fifths of health leaders said they were not prepared to explain AI-based conclusions to their internal stakeholders nor outsiders.

To address this deficit, 73% said they plan to develop internal ethical standards for AI to make sure these systems can act responsibly. Before that, they’ll need to determine what “acting responsibly” actually means — and as far as I know there are no accepted guidelines for developing such standards. (They might want to start off by reviewing Google’s ethical principles for AI use here.)

Adding AI to the enterprise IT mix could also wreak havoc. I for one was surprised to read that almost one-fourth of respondents said that they had been the target of adversarial AI behaviors, including falsified location data or bot fraud. (This stat blew my mind. Why haven’t we heard more about these “adversarial behaviors” and what are they?)

This certainly adds another element of uncertainty for CIOs interested in AI investments. While AI technologies can’t “think” in the traditional sense, they can create a range of problems previous-gen technology couldn’t.

This is part of a larger picture in which health organizations aren’t sure if their data has been corrupted. In fact, 86% of health execs said they hadn’t yet invested in technologies which could verify their data sources. Adding AI to the mix could potentially compound these problems, as it might create a cascade in which the AI then draws false inferences and takes inappropriate actions.

Meanwhile, respondents were excited about blockchain and smart contracts technology, with 91% reporting that they believed it would be a critical tool for supporting frictionless businesses over the next three years. All told, expect to see IoT and blockchain investments right away, with AI lagging until health IT leaders can teach it to play nicely.

A Missed Opportunity For Telemedicine Vendors

Posted on June 29, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Today, most direct-to-consumer telemedicine companies operate on a very simple model.

You pay for a visit up front. You talk to the doctor via video, the doctor issues as a prescription if needed and you sign off. Thanks to the availability of e-prescribing options, it’s likely your medication will be waiting for you when you get to the pharmacy.

In my experience, the whole process often takes 45 minutes or less. This beats the heck out of having to wait in line at an urgent care center or worse, the emergency department.

But what about caring for chronic illnesses that can’t be managed by a drive-by virtual visit? Can telemedicine vendors play a role here? Maybe so.

We already know that combining telemedicine with remote monitoring devices can be very effective. In fact, some health systems have gone all-in on virtual chronic care management.

One fascinating example is the $54 million Mercy Virtual Care Center, which describes itself as a “hospital without beds.” The Center, which has a few hundred employees, monitors more than 3,800 remote patients; sponsors a telehealth stroke program offering neurology services to EDs nationwide; manages a team of virtual hospitalists caring for patient around-the-clock using virtual visit tools; and runs Mercy SafeWatch, which the Center says is the largest single-hub electronic intensive care unit in the U.S.

Another example of such hospital-based programs is Intermountain Healthcare’s ConnectCare Pro, which brings together 35 telehealth programs and more than 500 clinicians. Its purpose is to supplement existing staffers and offer specialized services in rural communities where some of the services aren’t available.

Given the success of programs that maintain complex patients remotely, I think a private telemedicine company managing chronic care services might work as well. While hospitals have financial reasons to keep such care in-house, I believe an outside vendor could profit in other ways. That’s especially the case given the emergence of wearable trackers and smartwatches, which are far cheaper than the specialized tools needed in the past.

One likely buyer for this service would be health plans.

I’ve heard some complain publicly that in essence, telemedicine coverage just encourages patients to access care more often, which defeats the purpose of using it to lower healthcare costs. However, if an outside vendor offered to manage patients with chronic illnesses, it might be a more attractive proposition.

After all, health plans are understandably wringing their hands over the staggering cost of maintaining the health of millions of diabetics. In 2017, for example, the average medical expense for people diagnosed with diabetes was about $16,750 per year, with $9,600 due to diabetes. If health plans could lay the cost off to a specialized telemedicine vendor, some real savings might be possible.

Of course, being a telemedicine-based chronic care management company would be far different than offering direct-to-consumer telemedicine services on an occasional basis. The vendor would have to have comprehensive health data management tools, an army of case managers, tight relationships with clinicians and a boatload of remote monitoring devices on hand. None of this would come cheaply.

Still, while I haven’t fully run the numbers, my guess is that this could be a sustainable business model. It’s worth a try.

An Interesting Overview Of Alphabet’s Healthcare Investments

Posted on June 27, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Recently I’ve begun reading a blog called The Medical Futurist which offers some very interesting fare. In addition to some intriguing speculation, it includes some research that I haven’t seen anywhere else. (It is written by a physician named Bertalan Mesko.)

In this case, Mesko has buried a shrewd and well-researched piece on Alphabet’s healthcare investments in an otherwise rambling article. (The rambling part is actually pretty interesting on its own, by the way.)

The piece offers a rather comprehensive update on Alphabet’s investments in and partnerships with healthcare-related companies, suggesting that no other contender in Silicon Valley is investing in this sector heavily as Alphabet’s GV (formerly Google Ventures). I don’t know if he’s right about this, but it’s probably true.

By Mesko’s count, GV has backed almost 60 health-related enterprises since the fund was first kicked off in 2009. These investments include direct-to-consumer genetic testing firm 23andme, health insurance company Oscar Health, telemedicine venture Doctor on Demand and Flatiron Health, which is building an oncology-focused data platform.

Mesko also points out that GV has had an admirable track record so far, with five of the companies it first backed going public in the last year. I’m not sure I agree that going public is per se a sign of success — a lot depends on how the IPO is received by Wall Street– but I see his logic.

In addition, he notes that Alphabet is stocking up on intellectual resources. The article cites research by Ernest & Young reporting that Alphabet filed 186 healthcare-related patents between 2013 and 2017.

Most of these patents are related to DeepMind, which Google acquired in 2014, and Verily Life Sciences (formerly Google Life Sciences). While these deals are interesting in and of themselves, on a broader level the patents demonstrate Alphabet’s interest in treating chronic illnesses like diabetes and the use of bioelectronics, he says.

Meanwhile, Verily continues to work on a genetic data-collecting initiative known as the Baseline Study. It plans to leverage this data, using some of the same algorithms behind Google’s search technology, to pinpoint what makes people healthy.

It’s a grand and somewhat intimidating picture.

Obviously, there’s a lot more to discuss here, and even Mesko’s in-depth piece barely scratches the surface of what can come out of Alphabet and Google’s health investments. Regardless, it’s worth keeping track of their activity in the sector even if you find it overwhelming. You may be working for one of those companies someday.

IBM Watson Health Layoffs Suggests AI Strategy Isn’t Working

Posted on June 6, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

IBM Watson Health is apparently making massive cuts to its staff, in a move suggesting that its healthcare AI isn’t working.

Watson Health leaders have argued that AI (which Watson Health leaders call “cognitive computing”) as the solution to many of the healthcare industry’s problems. IBM pitched Watson technology as a revolutionary tool which could get to the root of difficult medical problems.

Over time, however, it’s begun to look like this wasn’t going to happen, at least for the present. Among other high-profile goofs, IBM Watson has struggled with applying the supercomputing tech to oncology, which was one of its main goals.

Now IBM Watson Health has slashed up to 70% of its staff, according to sources speaking to The Register. The site reports that most of the layoffs are cutting staff within companies IBM has brought in an effort to build out its healthcare credentials. These include medical data company Truven, acquired in 2016 for $2.6 billion, medical imaging firm Merge, bought in 2015 for $1 billion and healthcare management firm Phytel, the site reports.

The cuts reflect a major strategic shift for Watson Health, which was one of IBM’s flagship divisions until recently. Having invested heavily in businesses that might have helped it dominate the health IT world, it now appears to be rethinking it’s all in approach.

That being said, no one has suggested that IBM Watson Health will disappear in a poof of smoke. IBM corporate leaders seem dedicated to an AI future. However, if this report is correct, Watson Health is being reorganized completely. Not too much of a surprise since given how hyped it was, it would have been almost impossible for it to live up to the hype.

To me, this suggests that rolling out healthcare AI tools might call for a completely different business model. Rather than applying brute force supercomputing tools to enterprise healthcare issues, it may be better to build from the ground up.

For example, consider Google’s approach to healthcare AI supercomputing. UK-based DeepMind is building relationships and products from the ground up. Working with the National Health Service DeepMind Health is bringing mobile tools and AI research to hospitals. Its mobile health tools include Streams, a secure mobile phone app which feeds critical medical information to doctors and hospitals.

In my opinion, the future of AI in healthcare will look more like the DeepMind model and less like IBM Watson’s top-down approach. Building out AI-based tools and platforms for physicians and nurses first just makes sense.

Healthcare AI Needs a Breadth and Depth of Data

Posted on May 17, 2018 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.

Today I’m enjoying the New England HIMSS Spring Conference including an amazing keynote session by Dale Sanders from Health Catalyst. Next week I’ll be following up this blog post with some other insights that Dale shared at the New England HIMSS event, but today I just wanted to highlight one powerful concept that he shared:

Healthcare AI Needs a Breadth and Depth of Data

As part of this idea, Dale shared the following image to illustrate how much data is really needed for AI to effectively assess our health:

Dale pointed out that in healthcare today we really only have access to the data in the bottom right corner. That’s not enough data for AI to be able to properly assess someone’s health. Dale also suggested the following about EHR data:

Long story short, the EHR data is not going to be enough to truly assess someone’s health. As Google recently proved, a simple algorithm with more data is much more powerful than a sophisticated algorithm with less data. While we think we have a lot of data in healthcare, we really don’t have that much data. Dale Sanders made a great case for why we need more data if we want AI to be effective in healthcare.

What are you doing in your organization to collect data? What are you doing to get access to this data? Does collection of all of this data scare anyone? How far away are we from this data driven, AI future? Let us know your thoughts in the comments.

Google And Fitbit Partner On Wearables Data Options

Posted on May 7, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Fitbit and Google have announced plans to work together, in a deal intended to “transform the future of digital health and wearables.” While the notion of transforming digital health is hyperbole even for companies the size of Google and Fitbit, the pairing does have plenty of potential.

In a nutshell, Fitbit and Google expect to take on both consumer and enterprise health projects that integrate data from EMRs, wearables and other sources of patient information together. Given the players involved, it’s hard to doubt that at least something neat will emerge from their union.

Among the first things the pair plans to use Google’s new Cloud Healthcare API to connect Fitbit data with EMRs. Of course, readers will know that it’s one thing to say this and another to actually do it, but gross oversimplifications aside, the idea is worth pursuing.

Also, using services such as those offered by Twine Health– a recent Fitbit acquisition — the two companies will work to better manage chronic conditions such as diabetes and hypertension. Twine offers a connected health platform which leverages Fitbit data to offer customized health coaching.

Of course, as part of the deal Fitbit is moving to the Google Cloud Platform, which will supply the expected cloud services and engineering support.

The two say that moving to the Cloud Platform will offer Fitbit advanced security capabilities which will help speed up the growth of Fitbit Health Solutions business. They also expect to make inroads in population health analysis. For its part, Google also notes that it will bring its AI, machine learning capabilities and predictive analytics algorithms to the table.

It might be worth a small caution here. Google makes a point of saying it is “committed” to meeting HIPAA standards, and that most Google Cloud products do already. That “most” qualifier would make me a little bit nervous as a provider, but I know, why worry about these niceties when big deals are afoot. However, fair warning that when someone says general comments like this about meeting HIPAA standards, it probably means they already employ high security standards which are likely better than HIPAA. However, it also means that they probably don’t comply with HIPAA since HIPAA is about more than security and requires a contractual relationship between provider and business associate and the associated liability of being a business associate.

Anyway, to round out all of this good stuff, Fitbit and Google said they expect to “innovate and transform” the future of wearables, pairing Fitbit’s brand, community, data and high-profile devices with Google’s extreme data management and cloud capabilities.

You know folks, it’s not that I don’t think this is interesting. I wouldn’t be writing about if I didn’t. But I do think it’s worth pointing out how little this news announcement says, really.

Yes, I realize that when partnerships begin, they are by definition all big ideas and plans. But when giants like Google, much less Fitbit, have to fall back on words like innovate and transform (yawn!), the whole thing is still pretty speculative. Just sayin’.

Privacy Fears May Be Holding Back Digital Therapeutics Adoption

Posted on May 3, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Consumers were already afraid that their providers might not be able to protect the privacy of their health data. Given the daily news coverage of large data breaches and since the Facebook data scandal blew up, consumers may be even less likely try out new digital health approaches.

For example, a new study by innovation consultancy Enspektos has concluded that patients may be afraid to adopt digital therapeutics options. Many fear that the data might be compromised or the technology may subject them to unwanted personal surveillance.

Without a doubt, digital therapeutics could have a great future. Possibilities include technologies such as prescription drugs with embedded sensors tracking medication compliance, as well as mobile apps that could potentially replace drugs. However, consumers’ appetite for such innovations may be diminishing as consumer fears over data privacy grow.

The research, which was done in collaboration with Savvy Cooperative, found that one-third of respondents fear that such devices will be used to track their behavior in invasive ways or that the data might be sold to a third party without the permission. As the research authors note, it’s hard to argue that the Facebook affair has ratcheted up these concerns.

Other research by Enspektos includes some related points:

  • Machine-aided diagnosis is growing as AI, wearables and data analytics are combined to predict and treat diseases
  • The deployment of end-to-end digital services is increasing as healthcare organizations work to create comprehensive platforms that embrace a wide range of conditions

It’s worth noting that It’s not just consumers who are worried about new forms of hacker intrusions. Industry CIOs have been fretting as it’s become more common for cybercriminals to attack healthcare organizations specifically. In fact, just last month Symantec identified a group known as Orangeworm that is breaking into x-ray, MRI and other medical equipment.

If groups like Orangeworm have begun to attack medical devices — something cybersecurity experts have predicted for years — we’re looking at a new phase in the battle to protect hospital devices and data. If one cybercriminal decides to focus on healthcare specifically, it’s likely that others will as well.

It’s bad enough that people are worried about the downsides of digital therapeutics. If they really knew how insecure their overall medical data could be going forward, they might be afraid to even sign in to their portal again.

More Ways AI Can Transform Healthcare

Posted on April 25, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

You’ve probably already heard a lot about how AI will change healthcare. Me too. Still, given its potential, I’m always interested in hearing more, and the following article struck me as offering some worthwhile ideas.

The article, which was written by Humberto Alexander Lee of Tesser Health, looks at ways in which AI tools can reduce data complexity and detect patterns which would be difficult or even impossible for humans to detect.

His list of AI’s transformative powers includes the following:

  • Identifying diseases and providing diagnoses

AI algorithms can predict when people are likely to develop heart disease far more accurately than humans. For example, at Google healthcare technology subsidiary Verily, scientists created an algorithm that can predict heart disease by looking at the back of a person’s eyes and pinpoint early signs of specific heart conditions.

  • Crowdsourcing treatment options and monitoring drug response

As wearable devices and mobile applications mature, and data interoperability improves thanks to standards such as FHIR, data scientists and clinicians are beginning to generate new insights using machine learning. This is leading to customizable treatments that can provide better results than existing approaches.

  • Monitoring health epidemics

While performing such a task would be virtually impossible for humans, AI and AI-related technologies can sift through staggering pools of data, including government intelligence and millions of social media posts, and combine them with ecological, biogeographical and public health information, to track epidemics. In some cases, this process will predict health threats before they blossom.

  • Virtual assistance helping patients and physicians communicate clearly

AI technology can improve communication between patients and physicians, including by creating software that simplifies patient communication, in part by transforming complex medical terminology into digestible information. This helps patients and physicians engage in a meaningful two-way conversation using mobile devices and portals.

  • Developing better care management by improving clinical documentation

Machine learning technology can improve documentation, including user-written patient notes, by analyzing millions of rows of data and letting doctors know if any data is missing or clarification is needed on any procedures. Also, Deep Neural Network algorithms can sift through information in written clinical documentation. These processes can improve outcomes by identifying patterns almost invisible to human eyes.

Lee is so bullish on AI that he believes we can do even more than he has described in his piece. And generally speaking, it’s hard to disagree with him that there’s a great deal of untapped potential here.

That being said, Lee cautions that there are pitfalls we should be aware of when we implement AI. What risks do you see in widespread AI implementation in healthcare?