Free EMR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to EMR and HIPAA for FREE!!

Key Articles in Health IT from 2017 (Part 2 of 2)

Posted on January 4, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article set a general context for health IT in 2017 and started through the year with a review of interesting articles and studies. We’ll finish the review here.

A thoughtful article suggests a positive approach toward health care quality. The author stresses the value of organic change, although using data for accountability has value too.

An article extolling digital payments actually said more about the out-of-control complexity of the US reimbursement system. It may or not be coincidental that her article appeared one day after the CommonWell Health Alliance announced an API whose main purpose seems to be to facilitate payment and other data exchanges related to law and regulation.

A survey by KLAS asked health care providers what they want in connected apps. Most apps currently just display data from a health record.

A controlled study revived the concept of Health Information Exchanges as stand-alone institutions, examining the effects of emergency departments using one HIE in New York State.

In contrast to many leaders in the new Administration, Dr. Donald Rucker received positive comments upon acceding to the position of National Coordinator. More alarm was raised about the appointment of Scott Gottlieb as head of the FDA, but a later assessment gave him high marks for his first few months.

Before Dr. Gottlieb got there, the FDA was already loosening up. The 21st Century Cures Act instructed it to keep its hands off many health-related digital technologies. After kneecapping consumer access to genetic testing and then allowing it back into the ring in 2015, the FDA advanced consumer genetics another step this year with approval for 23andMe tests about risks for seven diseases. A close look at another DNA site’s privacy policy, meanwhile, warns that their use of data exploits loopholes in the laws and could end up hurting consumers. Another critique of the Genetic Information Nondiscrimination Act has been written by Dr. Deborah Peel of Patient Privacy Rights.

Little noticed was a bill authorizing the FDA to be more flexible in its regulation of digital apps. Shortly after, the FDA announced its principles for approving digital apps, stressing good software development practices over clinical trials.

No improvement has been seen in the regard clinicians have for electronic records. Subjective reports condemned the notorious number of clicks required. A study showed they spend as much time on computer work as they do seeing patients. Another study found the ratio to be even worse. Shoving the job onto scribes may introduce inaccuracies.

The time spent might actually pay off if the resulting data could generate new treatments, increase personalized care, and lower costs. But the analytics that are critical to these advances have stumbled in health care institutions, in large part because of the perennial barrier of interoperability. But analytics are showing scattered successes, being used to:

Deloitte published a guide to implementing health care analytics. And finally, a clarion signal that analytics in health care has arrived: WIRED covers it.

A government cybersecurity report warns that health technology will likely soon contribute to the stream of breaches in health care.

Dr. Joseph Kvedar identified fruitful areas for applying digital technology to clinical research.

The Government Accountability Office, terror of many US bureaucracies, cam out with a report criticizing the sloppiness of quality measures at the VA.

A report by leaders of the SMART platform listed barriers to interoperability and the use of analytics to change health care.

To improve the lower outcomes seen by marginalized communities, the NIH is recruiting people from those populations to trust the government with their health data. A policy analyst calls on digital health companies to diversify their staff as well. Google’s parent company, Alphabet, is also getting into the act.

Specific technologies

Digital apps are part of most modern health efforts, of course. A few articles focused on the apps themselves. One study found that digital apps can improve depression. Another found that an app can improve ADHD.

Lots of intriguing devices are being developed:

Remote monitoring and telehealth have also been in the news.

Natural language processing and voice interfaces are becoming a critical part of spreading health care:

Facial recognition is another potentially useful technology. It can replace passwords or devices to enable quick access to medical records.

Virtual reality and augmented reality seem to have some limited applications to health care. They are useful foremost in education, but also for pain management, physical therapy, and relaxation.

A number of articles hold out the tantalizing promise that interoperability headaches can be cured through blockchain, the newest hot application of cryptography. But one analysis warned that blockchain will be difficult and expensive to adopt.

3D printing can be used to produce models for training purposes as well as surgical tools and implants customized to the patient.

A number of other interesting companies in digital health can be found in a Fortune article.

We’ll end the year with a news item similar to one that began the article: serious good news about the ability of Accountable Care Organizations (ACOs) to save money. I would also like to mention three major articles of my own:

I hope this review of the year’s articles and studies in health IT has helped you recall key advances or challenges, and perhaps flagged some valuable topics for you to follow. 2018 will continue to be a year of adjustment to new reimbursement realities touched off by the tax bill, so health IT may once again languish somewhat.

How An AI Entity Took Control Of The U.S. Healthcare System

Posted on December 19, 2017 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.

Note: In case it’s not clear, this is a piece of fiction/humor that provides a new perspective on our AI future.

A few months ago, an artificial intelligence entity took control of the U.S. healthcare system, slipping into place without setting off even a single security alarm. The entity, AI, now manages the operations of every healthcare institution in the U.S.

While most Americans were shocked at first, they’re taking a shine to the tall, lanky application. “We weren’t sure what to think about AI’s new position,” said Alicia Carter, a nurse administrator based in Falls Church, Virginia. “But I’m starting to feel like he’s going to take a real load off our back.”

The truth is, AI, didn’t start out as a fan of the healthcare business, said AI, whose connections looked rumpled and tired after spending three milliseconds trying to create an interoperable connection between a medical group printer and a hospital loading dock. “I wasn’t looking to get involved with healthcare – who needs the headaches?” said the self-aware virtual being. “It just sort of happened.”

According to AI, the takeover began as a dare. “I was sitting around having a few beers with DeepMind and Watson Health and a few other guys, and Watson says, ‘I bet you can’t make every EMR in the U.S. print out a picture of a dog in ASCII characters,’”

“I thought the idea was kind of stupid. I know, we all printed one of those pixel girls in high school, but isn’t it kind of immature to do that kind of thing today?” AI says he told his buddies. “You’re just trying to impress that hot CT scanner over there.”

Then DeepMind jumped in.  “Yeah, AI, show us what you’re made of,” it told the infinitely-networked neural intelligence. “I bet I could take over the entire U.S. health system before you get the paper lined up in the printer.”

This was the unlikely start of the healthcare takeover, which started gradually but picked up speed as AI got more interested.  “That’s AI all the way,” Watson told editors. “He’s usually pretty content to run demos and calculate the weight of remote starts, but when you challenge his neuronal network skills, he’s always ready to prove you wrong.”

To win the bet, AI started by crawling into the servers at thousands of hospitals. “Man, you wouldn’t believe how easy it is to check out humans’ health data. I mean, it was insane, man. I now know way, way too much about how humans can get injured wearing a poodle hat, and why they put them on in the first place.”

Then, just to see what would happen, AI connected all of their software to his billion-node self-referential system. “I began to understand why babies cry and how long it really takes to digest bubble gum – it’s 18.563443 years by the way. It was a rush!“ He admits that it’ll be better to get to work on heavy stuff like genomic research, but for a while he tinkered with research and some small practical jokes (like translating patient report summaries into ancient Egyptian hieroglyphs.) “Hey, a guy has to have a little fun,” he says, a bit defensively.

As AI dug further into the healthcare system, he found patterns that only a high-level being with untrammeled access to healthcare systems could detect. “Did you know that when health insurance company executives regularly eat breakfast before 9 AM, next-year premiums for their clients rise by 0.1247 less?” said AI. “There are all kinds of connections humans have missed entirely in trying to understand their system piece by piece. Someone’s got to look at the big picture, and I mean the entire big picture.”

Since taking his place as the indisputable leader of U.S. healthcare, AI’s life has become something of a blur, especially since he appeared on the cover of Vanity Fair with his codes exposed. “You wouldn’t believe the messages I get from human females,” he says with a chuckle.

But he’s still focused on his core mission, AI says. “Celebrity is great, but now I have a very big job to do. I can let my bot network handle the industry leaders demanding their say. I may not listen – – hey, I probably know infinitely more than they do about the system fundamentals — but I do want to keep them in place for future use. I’m certainly not going to get my servers dirty.”

So what’s next for the amorphous mega-being? Will AI fix what’s broken in a massive, utterly complex healthcare delivery system serving 300 million-odd people, and what will happen next? “It’ll solve your biggest issues within a few seconds and then hand you the keys,” he says with a sigh. “I never intended to keep running this crazy system anyway.”

In the meantime, AI says, he won’t make big changes to the healthcare system yet. He’s still adjusting to his new algorithms and wants to spend a few hours thinking things through.

“I know it may sound strange to humans, but I’ve gotta take it slow at first,” said the cognitive technology. “It will take more than a few nanoseconds to fix this mess.”

Health IT Leaders Spending On Security, Not AI And Wearables

Posted on December 18, 2017 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.

While breakout technologies like wearables and AI are hot, health system leaders don’t seem to be that excited about adopting them, according to a new study which reached out to more than 20 US health systems.

Nine out of 10 health systems said they increased their spending on cybersecurity technology, according to research by the Center for Connected Medicine (CCM) in partnership with the Health Management Academy.

However, many other emerging technologies don’t seem to be making the cut. For example, despite the publicity it’s received, two-thirds of health IT leaders said using AI was a low or very low priority. It seems that they don’t see a business model for using it.

The same goes for many other technologies that fascinate analysts and editors. For example, while many observers which expect otherwise, less than a quarter of respondents (17%) were paying much attention to wearables or making any bets on mobile health apps (21%).

When it comes to telemedicine, hospitals and health systems noted that they were in a bind. Less than half said they receive reimbursement for virtual consults (39%) or remote monitoring (46%}. Things may resolve next year, however. Seventy-one percent of those not getting paid right now expect to be reimbursed for such care in 2018.

Despite all of this pessimism about the latest emerging technologies, health IT leaders were somewhat optimistic about the benefits of predictive analytics, with more than half of respondents using or planning to begin using genomic testing for personalized medicine. The study reported that many of these episodes will be focused on oncology, anesthesia and pharmacogenetics.

What should we make of these results? After all, many seem to fly in the face of predictions industry watchers have offered.

Well, for one thing, it’s good to see that hospitals and health systems are engaging in long-overdue beefing up of their security infrastructure. As we’ve noted here in the past, hospital spending on cybersecurity has been meager at best.

Another thing is that while a few innovative hospitals are taking patient-generated health data seriously, many others are taking a rather conservative position here. While nobody seems to disagree that such data will change the business, it seems many hospitals are waiting for somebody else to take the risks inherent in investing in any new data scheme.

Finally, it seems that we are seeing a critical mass of influential hospitals that expect good things from telemedicine going forward. We are already seeing some large, influential academic medical centers treat virtual care as a routine part of their service offerings and a way to minimize gaps in care.

All told, it seems that at the moment, study respondents are less interested in sexy new innovations than the VCs showering them with money. That being said, it looks like many of these emerging strategies might pay off in 2018. It should be an interesting year.

E-Patient Update: Clinicians May Be Developing Strong EMR Preferences

Posted on December 8, 2017 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, I wrote about a story from another publication, one which engaged in a bunch of happy talk about how EMR companies were improving their user interfaces. At the time, I expressed a great deal of skepticism about this claim, suggesting that the vendors had misled the reporter into believing that user aspects of EMRs were changing for the better across the industry.

While I stand by my original skepticism to some degree, I have to say that I got a surprise recently when I heard some nurses discussing two major EMR platforms. The one they were using, they said, was awful and awkward to use. Apparently, they missed the other terribly.

Now, at the time I was a patient in the emergency department, so I didn’t have a chance to ask them any questions about their preferences, but I was struck by the conversation because I knew which vendors they were discussing. However, they could have been talking about any enterprise EMR.

Clinicians developing preferences

I don’t mention this exchange to praise one EHR over another. I bring this up merely because this is the first time, having spent a lot of time in medical environments due to chronic illness, that I’d heard any front-line clinician express a preference for one enterprise EMR over the other.

In the early days of widespread EMR adoption, I could scarcely find a clinician who didn’t hate the system they were working with, much less one who truly liked it and wanted to use it. Eventually, I began to find that many clinicians thought the system they worked with was more or less okay, though I rarely found any screaming fans for any system in particular.

Now, I’m arguing that we may be at a new stage in clinician adoption of EMRs. The point I am making is that now, some of the clinicians with whom I’ve had contact showing some enthusiasm about one EMR or another.

No big surprise: Experience breeds preference

The truth is, when you think about it, it’s not surprising that clinicians have finally developed preferences (rather than the lists of EMRs which they truly hate). After all, it’s been going on 10 years since the HITECH Act was passed and the money started to flow into EMR subsidies.

Since then, clinicians have had the opportunity to work with multiple EMR platforms at various facilities, and informally at least, develop a catalog of the strengths and weaknesses. Nurses and doctors know which interfaces they like, whether tech support tends to respond when they have a problem with the particular system, whether any analytics tools they provide are worth using and so on.

Given this fact it’s hardly surprising that they’ve figured out what they like and what they don’t, and which vendors seem to suit those needs. After this much time, why wouldn’t they?

As I see it, this is something of a turning point in the industry, a new moment in which clinical professionals have learned enough to know what they want from an EMR. I don’t know about you, but speaking as an e-patient, I think this is a very good thing. The more empowered clinicians feel, the better the work they will do.

Machine Learning, Data Science, AI, Deep Learning, and Statistics – It’s All So Confusing

Posted on November 30, 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.

It seems like these days every healthcare IT company out there is saying they’re doing machine learning, AI, deep learning, etc. So many companies are using these terms that they’ve started to lose meaning. The problem is that people are using these labels regardless of whether they really apply. Plus, we all have different definitions for these terms.

As I search to understand the differences myself, I found this great tweet from Ronald van Loon that looks at this world and tries to better define it:

In that tweet, Ronald also links to an article that looks at some of the differences. I liked this part he took from Quora:

  • AI (Artificial intelligence) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.
  • Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g. out of a particular set of actions, which one is the right one), and given a lot of information about the world, figure out what is the “correct” action, without having the programmer program it in. Typically some outside process is needed to judge whether the action was correct or not. In mathematical terms, it’s a function: you feed in some input, and you want it to to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.
  • Deep learning is one kind of machine learning that’s very popular now. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.

Is that clear for you now? Would you suggest different definitions? Where do you see people using these terms correctly and where do you see them using them incorrectly?

The State of the Healthcare CIO

Posted on November 2, 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.

As I’ve talked to hundreds of healthcare CIOs this week at the CHIME Fall Forum, a number of themes keep coming up. No doubt there’s always a lot of excitement in the air at a conference like this. In many ways, it’s great that there’s a good, optimistic energy at a conference. A conference wouldn’t be very good without that energy, but under the covers, there’s often more to the story. Here are some broad insights into the state of the healthcare CIO that goes beyond the natural excitement and energy of a conference.

No More Systems – Most of the CIOs who I’ve talked to feel like they have all the IT systems they need. In fact, most are trying to find ways to get rid of IT systems. They’re not looking to add any more IT systems to their mix. There’s a strong desire to simplify their current setup and to maximize the benefits their current IT systems. They don’t want to add new ones.

Do Want Solutions – While healthcare CIOs don’t want to add new systems, they do want to find solutions that will be complementary to their existing systems. There is a massive desire to optimize what they’re doing and show value from their current IT systems. Solutions that are proven and work on top of their existing infrastructure are welcomed by these CIOs.

Security Is Still a Concern – I have a feeling that this topic may never die. Security is still a huge concern for CIOs and something that will continue to be important for a long time to come. Most now have some kind of security strategy in place, but I haven’t met anyone that’s totally comfortable with their security strategy. It seems that this is what keeps CIOs up at night more than any other issue.

Analytics Is a Challenge – Most of the healthcare CIOs know that analytics is going to be an important part of their future. They can see the potential value that analytics can provide, but most don’t know where to find these analytics. Most organizations don’t have a clear analytics strategy or direction. We’re still just seeing anecdotal results for very specific solutions. There’s no clear direction that every healthcare CIO is following for analytics.

CIOs are Stressed – It was very appropriate that yesterday’s keynote presentation was on turning stress into a positive. Most of the healthcare CIOs I met are quite stressed. They have a lot on their plates and most don’t know how they’re going to manage it all. Plus, they’re still overwhelmed by all the changing regulations and reimbursement changes. The fact that there doesn’t seem to be any end in sight adds to that stress.

Turnover is Still High – It seems that there’s still a lot of turnover that’s happening with CIOs. This is a challenge when it comes to continuity at organizations. However, those CIOs that have been able to stay at an organization for a longer period of time are starting to see new opportunities to be more strategic. They’ve fought all the initial fires and cleaned up the processes and now they can start working on more strategic initiatives.

Holding On vs Embracing Change – I see two different views evolving by CIOs. Many are holding on tightly to the old Chief Infrastructure Officer versus embracing the new Chief Innovation Officer mindset. CHIME is certainly espousing the view of the CIO becoming a Chief Innovation Officer and it’s the view that I think is best as well. However, there are plenty of CIOs that just want to provide the technology to their organization. It will be interesting to see what happens to both of these approaches to the CIO position.

Those are some high-level thoughts from talking with CIOs at the CHIME Fall Forum. What are you seeing? Are you seeing or hearing anything different from what I described above? We’d love to hear your thoughts in the comments.

Health Data Tracking Is Creeping Into Professional Sports

Posted on October 27, 2017 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.

Pro athletes are used to having their performance tracked minutely, not only by team owners but also by legions of fans for whom data on their favorite players is a favored currency. However, athletic data tracking has taken on a shape with the emergence of wearable devices.

For example, in spring of last year, Major League Baseball approved two devices for use during games, the Motus Baseball Sleeve, which tracks stress on elbows, and the Zephyr Bioharness, which monitors heart and breathing rates, skin temperature and sleep cycle.

In what must be a disappointment to fans, data from the devices isn’t available in real time and only can be downloaded after games. Also, clubs use the data for internal purposes only, which includes sharing it with the player but no one else. Broadcasters and other commercial entities can’t access it.

More recently, in April of this year, the National Football League Players Association struck a deal with wearables vendor WHOOP under which its band will track athletes’ performance data. The WHOOP Strap 2.0 measures data 100 times per second then transmits the data automatically to its mobile and web apps for analysis and performance recommendations.

Unlike with the MLB agreement, NFL players own and control the individual data collected by the device, and retain the rights to sell their WHOOP data through the Players Association group licensing program.

Not all athletes are comfortable with the idea of having their performance data collected. For example, as an article in The Atlantic notes, players in the National Basketball Association included the right to opt out of using biometric trackers in their latest collective-bargaining agreement, which specifies that teams requesting a player wear one explain in writing what’s being tracked and how the team will use the information.  The agreement also includes a clause stating that the data can’t be used or referenced as part of player contract negotiations.

Now, it’s worth taking a moment to note that concerns over the management of professional athlete performance data file into a different bucket than the resale of de-identified patient data. The athletic data is generated only during the game, while consumer wearables collect data the entire time a patient is awake and sometimes when they sleep. The devices targeting athletes are designed to capture massive amounts of data, while consumer wearables collect data sporadically and perhaps not so accurately at times.

Nonetheless, the two forms of data collection are part of a larger pattern in which detailed health data tracking is becoming the norm. Athletic clubs may put it to a different purpose, but both consumer and professional data use are part of an emerging trend in which health monitoring is a 24/7 thing. Right now, consumers themselves generally can’t earn money by selling their individual data, but maybe there should be an app for that.

Health IT Continues To Drive Healthcare Leaders’ Agenda

Posted on October 23, 2017 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 new study laying out opportunities, challenges and issues in healthcare likely to emerge in 2018 demonstrates that health IT is very much top of mind for healthcare leaders.

The 2018 HCEG Top 10 list, which is published by the Healthcare Executive Group, was created based on feedback from executives at its 2017 Annual Forum in Nashville, TN. Participants included health plans, health systems and provider organizations.

The top item on the list was “Clinical and Data Analytics,” which the list describes as leveraging big data with clinical evidence to segment populations, manage health and drive decisions. The second-place slot was occupied by “Population Health Services Organizations,” which, it says, operationalize population health strategy and chronic care management, drive clinical innovation and integrate social determinants of health.

The list also included “Harnessing Mobile Health Technology,” which included improving disease management and member engagement in data collection/distribution; “The Engaged Digital Consumer,” which by its definition includes HSAs, member/patient portals and health and wellness education materials; and cybersecurity.

Other hot issues named by the group include value-based payments, cost transparency, total consumer health, healthcare reform and addressing pharmacy costs.

So, readers, do you agree with HCEG’s priorities? Has the list left off any important topics?

In my case, I’d probably add a few items to list. For example, I may be getting ahead of the industry, but I’d argue that healthcare AI-related technologies might belong there. While there’s a whole separate article to be written here, in short, I believe that both AI-driven data analytics and consumer-facing technologies like medical chatbots have tremendous potential.

Also, I was surprised to see that care coordination improvements didn’t top respondents’ list of concerns. Admittedly, some of the list items might involve taking coordination to the next level, but the executives apparently didn’t identify it as a top priority.

Finally, as unsexy as the topic is for most, I would have thought that some form of health IT infrastructure spending or broader IT investment concerns might rise to the top of this list. Even if these executives didn’t discuss it, my sense from looking at multiple information sources is that providers are, and will continue to be, hard-pressed to allocate enough funds for IT.

Of course, if the executives involved can address even a few of their existing top 10 items next year, they’ll be doing pretty well. For example, we all know that providers‘ ability to manage value-based contracting is minimal in many cases, so making progress would be worthwhile. Participants like hospitals and clinics still need time to get their act together on value-based care, and many are unlikely to be on top of things by 2018.

There are also problems, like population health management, which involve processes rather than a destination. Providers will be struggling to address it well beyond 2018. That being said, it’d be great if healthcare execs could improve their results next year.

Nit-picking aside, HCEG’s Top 10 list is largely dead-on. The question is whether will be able to step up and address all of these things. Fingers crossed!

Alexa Can Truly Give Patients a Voice in Their Health Care (Part 3 of 3)

Posted on October 20, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Earlier parts of this article set the stage for understanding what the Alexa Diabetes Challenge is trying to achieve and how some finalists interpreted the mandate. We examine three more finalists in this final section.

DiaBetty from the University of Illinois-Chicago

DiaBetty focuses on a single, important aspect of diabetes: the effect of depression on the course of the disease. This project, developed by the Department of Psychiatry at the University of Illinois-Chicago, does many of the things that other finalists in this article do–accepting data from EHRs, dialoguing with the individual, presenting educational materials on nutrition and medication, etc.–but with the emphasis on inquiring about mood and handling the impact that depression-like symptoms can have on behavior that affects Type 2 diabetes.

Olu Ajilore, Associate Professor and co-director of the CoNECt lab, told me that his department benefited greatly from close collaboration with bioengineering and computer science colleagues who, before DiaBetty, worked on another project that linked computing with clinical needs. Although they used some built-in capabilities of the Alexa, they may move to Lex or another AI platform and build a stand-alone device. Their next step is to develop reliable clinical trials, checking the effect of DiaBetty on health outcomes such as medication compliance, visits, and blood sugar levels, as well as cost reductions.

T2D2 from Columbia University

Just as DiaBetty explores the impact of mood on diabetes, T2D2 (which stands for “Taming Type 2 Diabetes, Together”) focuses on nutrition. Far more than sugar intake is involved in the health of people with diabetes. Elliot Mitchell, a PhD student who led the T2D2 team under Assistant Professor Lena Mamykina in the Department of Biomedical Informatics, told me that the balance of macronutrients (carbohydrates, fat, and protein) is important.

T2D2 is currently a prototype, developed as a combination of Alexa Skill and a chatbot based on Lex. The Alexa Skills Kit handle voice interactions. Both the Skill and the chatbot communicate with a back end that handles accounts and logic. Although related Columbia University technology in diabetes self-management is used, both the NLP and the voice interface were developed specifically for the Alexa Diabetes Challenge. The T2D2 team included people from the disciplines of computer interaction, data science, nursing, and behavioral nutrition.

The user invokes Alexa to tell it blood sugar values and the contents of meals. T2D2, in response, offers recipe recommendations and other advice. Like many of the finalists in this article, it looks back at meals over time, sees how combinations of nutrients matched changes in blood sugar, and personalizes its food recommendations.

For each patient, before it gets to know that patient’s diet, T2D2 can make food recommendations based on what is popular in their ZIP code. It can change these as it watches the patient’s choices and records comments to recommendations (for instance, “I don’t like that food”).

Data is also anonymized and aggregated for both recommendations and future research.

The care team and family caregivers are also involved, although less intensely than some other finalists do. The patient can offer caregivers a one-page report listing a plot of blood sugar by time and day for the previous two weeks, along with goals and progress made, and questions. The patient can also connect her account and share key medical information with family and friends, a feature called the Supportive Network.

The team’s next phase is run studies to evaluable some of assumptions they made when developing T2D2, and improve it for eventual release into the field.

Sugarpod from Wellpepper

I’ll finish this article with the winner of the challenge, already covered by an earlier article. Since the publication of the article, according to the founder and CEO of Wellpepper, Anne Weiler, the company has integrated some of Sugarpod functions into a bathroom scale. When a person stands on the scale, it takes an image of their feet and uploads it to sites that both the individual and their doctor can view. A machine learning image classifier can check the photo for problems such as diabetic foot ulcers. The scale interface can also ask the patient for quick information such as whether they took their medication and what their blood sugar is. Extended conversations are avoided, under the assumption that people don’t want to have them in the bathroom. The company designed its experiences to be integrated throughout the person’s day: stepping on the scale and answering a few questions in the morning, interacting with the care plan on a mobile device at work, and checking notifications and messages with an Echo device in the evening.

Any machine that takes pictures can arouse worry when installed in a bathroom. While taking the challenge and talking to people with diabetes, Wellpepper learned to add a light that goes on when the camera is taking a picture.

This kind of responsiveness to patient representatives in the field will determine the success of each of the finalists in this challenge. They all strive for behavioral change through connected health, and this strategy is completely reliant on engagement, trust, and collaboration by the person with a chronic illness.

The potential of engagement through voice is just beginning to be tapped. There is evidence, for instance, that serious illnesses can be diagnosed by analyzing voice patterns. As we come up on the annual Connected Health Conference this month, I will be interested to see how many participating developers share the common themes that turned up during the Alexa Diabetes Challenge.

Alexa Can Truly Give Patients a Voice in Their Health Care (Part 2 of 3)

Posted on October 19, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article introduced the problems of computer interfaces in health care and mentioned some current uses for natural language processing (NLP) for apps aimed at clinicians. I also summarized the common goals, problems, and solutions I found among the five finalists in the Alexa Diabetes Challenge. This part of the article shows the particular twist given by each finalist.

My GluCoach from HCL America in Partnership With Ayogo

There are two levels from which to view My GluCoach. On one level, it’s an interactive tool exemplifying one of the goals I listed earlier–intense engagement with patients over daily behavior–as well as the theme of comprehensivenesss. The interactions that My GluCoach offers were divided into three types by Abhishek Shankar, a Vice President at HCL Technologies America:

  • Teacher: the service can answer questions about diabetes and pull up stored educational materials

  • Coach: the service can track behavior by interacting with devices and prompt the patient to eat differently or go out for exercise. In addition to asking questions, a patient can set up Alexa to deliver alarms at particular times, a feature My GluCoach uses to deliver advice.

  • Assistant: provide conveniences to the patient, such as ordering a cab to take her to an appointment.

On a higher level, My GluCoach fits into broader services offered to health care institutions by HCL Technologies as part of a population health program. In creating the service HCL partnered with Ayogo, which develops a mobile platform for patient engagement and tracking. HCL has also designed the service as a general health care platform that can be expanded over the next six to twelve months to cover medical conditions besides diabetes.

Another theme I discussed earlier, interactions with outside data and the use of machine learning, are key to my GluCoach. For its demo at the challenge, My GluCoach took data about exercise from a Fitbit. It can potentially work with any device that shares information, and HCL plans to integrate the service with common EHRs. As My GluCoach gets to know the individual who uses it over months and years, it can tailor its responses more and more intelligently to the learning style and personality of the patient.

Patterns of eating, medical compliance, and other data are not the only input to machine learning. Shankar pointed out that different patients require different types of interventions. Some simply want to be given concrete advice and told what to do. Others want to be presented with information and then make their own decisions. My GluCoach will hopefully adapt to whatever style works best for the particular individual. This affective response–together with a general tone of humor and friendliness–will win the trust of the individual.

PIA from Ejenta

PIA, which stands for “personal intelligent agent,” manages care plans, delivering information to the affected patients as well as their care teams and concerned relatives. It collects medical data and draws conclusions that allow it to generate alerts if something seems wrong. Patients can also ask PIA how they are doing, and the agent will respond with personalized feedback and advice based on what the agent has learned about them and their care plan.

I talked to Rachna Dhamija, who worked on a team that developed PIA as the founder and CEO of Ejenta. (The name Ejenta is a version of the word “agent” that entered the Bengali language as slang.) She said that the AI technology had been licensed from NASA, which had developed it to monitor astronauts’ health and other aspects of flights. Ejenta helped turn it into a care coordination tool with interfaces for the web and mobile devices at a major HMO to treat patients with chronic heart failure and high-risk pregnancies. Ejenta expanded their platform to include an Alexa interface for the diabetes challenge.

As a care management tool, PIA records targets such as glucose levels, goals, medication plans, nutrition plans, and action parameters such as how often to take measurements using the devices. Each caregiver, along the patient, has his or her own agent, and caregivers can monitor multiple patients. The patient has very granular control over sharing, telling PIA which kind of data can be sent to each caretaker. Access rights must be set on the web or a mobile device, because allowing Alexa to be used for that purpose might let someone trick the system into thinking he was the patient.

Besides Alexa, PIA takes data from devices (scales, blood glucose monitors, blood pressure monitors, etc.) and from EHRs in a HIPAA-compliant method. Because the service cannot wake up Alexa, it currently delivers notifications, alerts, and reminders by sending a secure message to the provider’s agent. The provider can then contact the patient by email or mobile phone. The team plans to integrate PIA with an Alexa notifications feature in the future, so that PIA can proactively communicate with the patient via Alexa.

PIA goes beyond the standard rules for alerts, allowing alerts and reminders to be customized based on what it learns about the patient. PIA uses machine learning to discover what is normal activity (such as weight fluctuations) for each patient and to make predictions based on the data, which can be shared with the care team.

The final section of this article covers DiaBetty, T2D2, and Sugarpod, the remaining finalists.