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UPMC Sells Oncology Analytics Firm To Elsevier

Posted on January 22, 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.

Using analytics tools to improve cancer treatment can be very hard. That struggle is exemplified by the problems faced by IBM Watson Health, which dove into the oncology analytics field a few years ago but made virtually no progress in improving cancer treatment.

With any luck, however, Via Oncology will be more successful at moving the needle in cancer care. The company, which offers decision support for cancer treatment and best practices in cancer care management, was just acquired by information analytics firm Elsevier, which plans to leverage the company’s technology to support its healthcare business.

Elsevier’s Clinical Solutions group works to improve patient outcomes, reduce clinical errors and optimize cost and reimbursements for providers. Via Oncology, a former subsidiary of the University of Pittsburgh Medical Center, develops and implements clinical pathways for cancer care. Via Oncology spent more than 15 years as part of UPMC prior to the acquisition.

Via Oncology’s Via Pathways tool relies on evidence-based content to create clinical algorithms covering 95% of cancer types treated in the US. The content was developed by oncologists. In addition to serving as a basis for algorithm development, Via Oncology also shares the content with physicians and their staff through its Via Portal, a decision support tool which integrates with provider EMRs.

According to Elsevier, Via Pathways addresses more than 2,000 unique patient presentations which can be addressed by clinical algorithms and recommendations for all major aspects of cancer care. The system can also offer nurse triage and symptom tracking, cost information analytics, quality reporting and medical home tools for cancer centers.

According to the prepared statement issued by Elsevier, UPMC will continue to be a Via Oncology customer, which makes it clear that the healthcare giant wasn’t dumping its subsidiary or selling it for a fire sale price.

That’s probably because in addition to UPMC, more than 1,500 oncology providers and community, hospital and academic settings hold Via Pathways licenses. What makes this model particularly neat is that these cancer centers are working collaboratively to improve the product as they use it. Too few specialty treatment professionals work together this effectively, so it’s good to see Via Oncology leveraging user knowledge this way.

While most of this seems clear, I was left with the question of what role, if any, genomics plays in Via Oncology’s strategy. While it may be working with such technologies behind the scenes, the company didn’t mention any such initiatives in its publicly-available information.

This approach seems to fly in the face of existing trends and in particular, physician expectations. For example, a recent survey of oncologists by medical publication Medscape found that 71% of respondents felt genomic testing was either very important or extremely important to their field.

However, Via Oncology may have something up its sleeve and is waiting for it to be mature before it dives into the genomics pool. We’ll just have to see what it does as part of Elsevier.

Are there other areas beyond cancer where a similar approach could be taken?

Doctors, Data, Diagnoses, and Discussions: Achieving Successful and Sustainable Personalized/Precision Medicine

Posted on January 10, 2018 I Written By

The following is a guest blog post by Drew Furst, M.D., Vice President Clinical Consultants at Elsevier Clinical Solutions.

Personalized/precision medicine is a growing field and that trend shows no sign of slowing down.

In fact, a 2016 Grand View Research report estimated the global personalized medicine market was worth $1,007.88 billion in 2014, with projected growth to reach $2,452.50 billion by 2022.

As these areas of medicine become more commonplace, understanding the interactions between biological factors with a range of personal, environmental and social impacts on health is a vital step towards achieving sustainable success.

A better understanding begins with answering important questions such as whether the focus should be precision population medicine (based on disease) or precision patient-specific medicine (based on the individual).

Specificity in terminology is needed. The traditional term of “personalized medicine” has evolved into the term “precision medicine,” but this new usage requires a more detailed look into the precise science of genetic, environmental and lifestyle factors that influence any approach to treatment.

Comprehending the interactions between biological factors with a range of personal, environmental, and social impacts on health can provide insights into success and we’ve learned that some areas of precision medicine are more effective than others.

Through pharmacogenomics – the study of understanding how a patient’s genetic make-up affects the response to a particular drug – we have identified key enzymes in cancer formation and cancer treatment, which aids in the customization of drugs.

Research shows us that drug-metabolizing enzyme activity is one of many factors that impact a patient’s response to medication. We also know that human cytochrome P450 (CYP) plays an important role in the metabolism of drugs and environmental chemicals.

Therapies that incorporate drug-specific pharmacogenomics are a boon to oncology treatments and a vast improvement over the “shotgun therapy” approach of the past. Today, treatments can be targeted to enzymes and receptors that vary from person to person.

In traditional chemotherapy, a drug developed to kill rapidly growing cancer cells will indiscriminately target other rapidly growing cells such as hair cells, hence the often-observed hair loss. However, a targeted drug and delivery method aimed at only the receptive cells can be a much more effective approach and treatment, while minimizing collateral damage.

Recently, the journal Nature published a study showing the promise this method holds.  In the pilot study, scientists led by Dr. Catherine Wu of Dana-Farber Cancer Institute in Boston gave six melanoma patients an experimental, custom-made vaccine and, two years later, all were tumor-free following treatment.

Looking Beyond Genetics

Precision medicine needs to include more than just genetics.

Factors such as environment and socio-economic status also must be included when approaching disease states and we must undertake a comprehensive overview of a patient’s situation, including, but not limited to, family history.

Cultural dietary traditions can play into disease susceptibility. As an example, the frequent consumption of smoked fish in some Asian cultures increases their risk of gastric (stomach) cancers. Lower socioeconomic status can force acceptance of substandard and overcrowded housing with increased risk of illness ranging from lead toxicity, asbestosis, and Hantavirus to name a just a few.

A patient with a genetic propensity for lung cancer who also smokes cigarettes and has high radon levels in their home is increasing the odds of developing disease due to these combined genetic, behavioral, and environmental factors.

Patient-derived Data and the Diagnosis

In addition to the information now available through state-of-the-art medical testing, patient-derived information from wearables, biometrics, and direct-to-consumer health testing kits, presents patients and physicians alike with new opportunities and challenges.

Armed with newly discovered health data, patients may present it to their doctors with a request that it be included in their health record. Many patients expect an interpretation of that data when they visit their doctor and an explanation of what it means for their present (and future) healthcare.

Doctors can be overwhelmed when unfiltered information is thrown at them. Doctors are not prepared and research has yet to offer definitive support for interpretation of patient-derived data.

Studying hereditary traits can offer some insights from generation to generation. By delving into genomics of individual patients, we get a clearer picture into a person’s risk factor for a certain disease, but often this information provides no immediate solutions. Discovering a genetic indicator for Alzheimer’s, may reflect a higher propensity for the disease, but symptoms may be decades away, if they appear at all.

Pitfalls and Possibilities

There are many concerns about genomic data collection, one of which is whether policies can keep pace with patient privacy and the related ethical questions that inevitably ensue. These questions are consistently surfacing and there is no clear direction on the best course of action.

Clearer policies are needed to delineate who has access to a patient’s genetic records and whether third parties, such as health or life insurance companies, can deny coverage or care based on genomics.

In addition, one cannot ignore the psychological burden associated with knowing your “potential” for a disease, based solely on your genetic testing, when it may never come to fruition. Not to mention, its effect on planning for one’s future decisions relative to career, residence, and relationship commitments.

Even some physicians are reticent to undergo genetic testing for fear of who might gain access to the information and the consequences thereof.

Physicians face an additional conundrum in dealing with patient-supplied information: How to counsel patients when, in some cases, the task should be the responsibility of a community resources representative? In addition, patients who request that certain information not be included in their personal health record, present a problem for a physician justifying a test or a procedure to a payer.

The consumerization of healthcare and patient engagement strategies employed to deliver better outcomes are driving the healthcare industry to open conversations that elevate the level of care delivered to patients. In addition, physicians need to demand more direction and initiate more discussions on how to deal with the opportunities and challenges presented in the era of patient-derived and pharmacogenomics data.

While improving patient-physician communication should always be a priority, discussing how and when to use genetic and patient-derived information is still a work in progress.

Dr. Furst is Vice President Clinical Consultants at Elsevier Clinical Solutions.

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.”

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 3 of 3)

Posted on November 17, 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 previous segment of this article covered one of the crucial themes in health care today: simplifying technology’s interactions with individuals over health care. This segment finishes my coverage of this year’s Connected Health Conference with two more themes: improved data sharing and blockchains.

Keynote at Connected Health Conference

Keynote at Connected Health Conference

Improved data sharing
The third trend I’m pursuing is interoperability. If data collection is the oxygen that fuels connected health, data sharing is the trachea that brings it where it’s needed. Without interoperability, clinicians cannot aid patients in their homes, analysts cannot derive insights that inform treatments, and transitions to assisted living facilities or other environments will lead to poor care.

But the health care field is notoriously bad at data sharing. The usual explanation is that doctors want to make it hard for competitors to win away their patients. If that’s true, fee-for-value reimbursements will make them even more possessive. After all, under fee-for-value, clinicians are held accountable for patient outcomes over a long period of time. They won’t want to lose control of the patient. I first heard of this danger at a 2012 conference (described in the section titled “Low-hanging fruit signals a new path for cost savings”).

So the trade press routinely and ponderously reports that once again, years have gone by without much progress in data sharing. The US government recognizes that support for interoperability is unsatisfactory, and has recently changed the ONC certification program to focus on it.

Carla Kriwet, CEO of Connected Care and Health Informatics at Philips, was asked in her keynote Fireside Chat to rate the interoperability of health data on a scale from 0 to 10, and chose a measly 3. She declared that “we don’t believe in closed systems at all” and told me in an interview that Philips is committed to creating integrated solutions that work with any and all products. Although Philips devices are legendary in many domains, Kriwet wants customers to pay for outcomes, not devices.

For instance, Philips recently acquired the Wellcentive platform that allows better care in hospitals by adopting population health approaches that look at whole patient populations to find what works. The platform works with a wide range of input sources and is meant to understand patient populations, navigate care and activate patients. Philips also creates dashboards with output driven by artificial intelligence–the Philips IntelliVue Guardian solution with Early Warning Scoring (EWS)–that leverages predictive analytics to present critical information about patient deterioration to nurses and physicians. This lets them intervene quickly before an adverse event occurs, without the need for logging in repeatedly. (This is an example of another trend I cover in this article, the search for simpler interfaces.)

Kriwet also told me that Philips has incorporated the principles of agile programming throughout the company. Sprints of a few weeks develop their products, and “the boundary comes down” between R&D and the sales team.

I also met with Jon Michaeli, EVP of Strategic Partnerships with Medisafe, a company that I covered two years ago. Medisafe is one of a slew of companies that encourage medication adherence. Always intensely based on taking in data and engaging patients in a personalized way, Medisafe has upped the sophistication of their solution, partly by integrating with other technologies. One recent example is its Safety Net, provided by artificial intelligence platform Neura. For instance, if you normally cart your cell phone around with you, but it’s lying quiet from 10:00 PM until 6:00 AM, Safety Net may determine your reason for missing your bedtime dose at 11:00 PM was that you had already fallen asleep. If Safety Net sees recurring patterns of behavior, it will adjust reminder time automatically.

Medisafe also gives users the option of recording the medication adherence through sensors rather than responding to reminders. They can communicate over Bluetooth to a pill bottle cap (“iCap”) that replaces the standard medicine cap and lets the service know when you have opened the bottle. The iCap fits the vast majority of medicine bottles dispensed by U.S. pharmacies and costs only $20 ($40 for a pack of 2), so you can buy several and use them for as long as you’re taking your medicine.

On another level, Mivatek provides some of the low-level scaffolding to connected health by furnishing data from devices to systems developed by the company’s clients. Suppose, for instance, that a company is developing a system that responds to patients who fall. Mivatek can help them take input from a button on the patient’s phone, from a camera, from a fall detector, or anything else to which Mivatek can connect. The user can add a device to his system simply by taking a picture of the bar code with his phone.

Jorge Perdomo, Senior Vice President Corporate Strategy & Development at Mivatek, told me that these devices work with virtually all of the available protocols on the market that have been developed to promote interoperability. In supporting WiFi, Mivatek loads an agent into its system to provide an additional level of security. This prevents device hacking and creates an easy-to-install experience with no setup requirements.

Blockchains
Most famous as a key technological innovation supporting BitCoin, blockchains have a broad application as data stores that record transactions securely. They can be used in health care for granting permissions to data and other contractual matters. The enticement offered by this technology is that no central institution controls or stores the blockchain. One can distribute the responsibility for storage and avoid ceding control to one institution.

Blockchains do, however, suffer from inherent scaling problems by design: they grow linearly as people add transactions, the additions must be done synchronously, and the whole chain must be stored in its entirety. But for a limited set of participants and relatively rate updates (for instance, recording just the granting of permissions to data and not each chunk of data exchanged), the technology holds great promise.

Although I see a limited role for blockchains, the conference gave considerable bandwidth to the concept. In a keynote that was devoted to blockchains, Dr. Samir Damani described how one of his companies, MintHealth, planned to use them to give individuals control over health data that is currently held by clinicians or researchers–and withheld from the individuals themselves.

I have previously covered the importance patient health records, and the open source project spotlighted by that article, HIE of One, now intends to use blockchain in a manner similar to MintHealth. In both projects, the patient owns his own data. MintHealth adds the innovation of offering rewards for patients who share their data with researchers, all delivered through the blockchain. The reward system is quite intriguing, because it would create for the first time a real market for highly valuable patient data, and thus lead to more research use along with fair compensation for the patients. MintHealth’s reward system also fits the connected health vision of promoting healthy behavior on a daily basis, to reduce chronic illness and health care costs.

Conclusion
Although progress toward connected health comes in fits and starts, the Connected Health Conference is still a bright spot in health care each year. For the first time this year, Partners’ Center for Connected Health partnered with another organization, the Personal Connected Health Alliance, and the combination seems to be a positive one. Certain changes were noticeable: for instance, all the breakout sessions were panels, and the keynotes were punctuated by annoying ads. An interesting focus this year was wellness in aging, the topic of the final panel. One surprising difference was the absence of the patient advocates from the Society for Participatory Medicine whom I’m used to meeting each year at this conference, perhaps because they held their own conference the day before.

The Center for Connected Health’s Joseph Kvedar still ran the program team, and the themes were familiar from previous years. This conference has become my touchstone for understanding health IT, and it will continue to be the place to go to track the progress of health care reform from a technological standpoint.

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 2 of 3)

Posted on November 15, 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 segment of this article introduced the themes of the Connected Health Conference and talked about the importance of validating what new technologies do using trials or studies like traditional medical advances. This segment continues my investigation into another major theme in health care: advanced interfaces.

Speaker from Validic at Connected Health Conference

Speaker from Validic at Connected Health Conference

Advanced interfaces
The compulsory picture of health care we’re accustomed to seeing, whenever we view hospital propaganda or marketing from health care companies, shows a patient in an awkward gown seated on an uncomfortable examination table. A doctor faces him or her full on–not a computer screen in site–exuding concern, wisdom, friendliness, and professionalism.

More and more, however, health sites are replacing this canonical photograph with one of a mobile phone screen speckled with indicators of our vital signs or thumbnail shot of our caregivers. The promise being conveyed is no longer care from a trusted clinician in the office, but instant access to all our information through a medium familiar to almost everyone everywhere–the personal mobile device.

But even touchscreen access to the world of the cloud is beginning to seem fusty. Typing in everything you eat with your thumbs, or even answering daily surveys about your mental state, gets old fast. As Dr. Yechiel Engelhard of TEVA said in his keynote, patients don’t want to put a lot of time into managing their illnesses, nor do doctors want to change their workflows. So I’m fascinated with connected health solutions that take the friction out of data collection and transmission.

One clear trend is the move to voice–or rather, I should say back to voice, because it is the original form of human communication for precise data. The popularity of Amazon Echo, along with Siri and similar interfaces, shows that this technology will hit a fever pitch soon. One research firm found that voice-triggered devices more than doubled in popularity between 2015 and 2016, and that more than half of Americans would like such a device in the home.

I recently covered a health care challenge using Amazon Alexa that demonstrates how the technology can power connected health solutions. Most of the finalists in the challenge were doing the things that the Connected Health Conference talks about incessantly: easy and frequent interactions with patients, analytics to uncover health problems, integration with health care providers, personalization, and so on.

Orbita is another company capitalizing on voice interfaces to deliver a range of connected health solutions, from simple medication reminders to complete care management applications for diabetes. I talked to CEO Bill Rogers, who explained that they provide a platform for integrating with AI engines provided by other services to carry out communication with individuals through whatever technology they have available. Thus, Orbita can talk through Echo, send SMS messages, interact with a fitness device or smart scale, or even deliver a reminder over a plain telephone interface.

One client of Orbita uses it platform to run a voice bot that talks to patients during their discharge process. The bot provides post-discharge care instructions and answers patients’ questions about things like pain management and surgery wound care. The results show that patients are more willing to ask questions of the bot than of a discharge nurse, perhaps because they’re not afraid of wasting someone’s time. Rogers also said services are improving their affective interfaces, which respond to the emotional tone of the patient.

Another trick to avoid complex interfaces is to gather as much data as possible from the patient’s behavior (with her consent, of course) to eliminate totally the need for her to manually enter data, or even press a button. Devices are getting closer to this kind of context-awareness. Following are some of the advances I enjoyed seeing at the Connected Health Conference.

  • PulseOn puts more health data collection into a wrist device than I’ve ever seen. Among the usual applications to fitness, they claim to detect atrial fibrillation and sleep apnea by shining a light on the user’s skin and measuring changes in reflections caused by variations in blood flow.
  • A finger-sized device called Gocap, from Common Sensing, measures insulin use and reports it over wireless connections to clinical care-takers. The device is placed over the needle end of an insulin pen, determines how much was injected by measuring the amount of fluid dispensed after a dose, and transmits care activity to clinicians through a companion app on the user’s smartphone. Thus, without having to enter any information by hand, people with diabetes can keep the clinicians up to date on their treatment.
  • One of the cleverest devices I saw was a comprehensive examination tool from Tyto Care. A small kit can carry the elements of a home health care exam, all focused on a cute little sphere that fits easily in the palm. Jeff Cutler, Chief Revenue Officer, showed me a simple check on the heart, ear, and throat that anyone can perform. You can do it with a doctor on the other end of a video connection, or save the data and send it to a doctor for later evaluation.

    Tyto Care has a home version that is currently being used and distributed by partners such as Heath Systems, providers, payers and employers, but will ultimately be available for sale to consumers for $299. They also offer a professional and remote clinic version that’s tailor-made for a school or assisted living facility.

A new Digital Therapeutics Alliance was announced just before the conference, hoping to promote more effective medical devices and allow solutions to scale up through such things as improving standards and regulations. Among other things, the alliance will encourage clinical trials, which I have already highlighted as critical.

Big advances were also announced by Validic, which I covered last year. Formerly a connectivity solution that unraveled the varying quasi-standard or non-standard protocols of different devices in order to take their data into electronic health records, Validic has created a new streaming API that allows much faster data transfers, at a much higher volume. On top of this platform they have built a notification service called Inform, which takes them from a networking solution to a part of the clinicians’ workflow.

Considerable new infrastructure is required to provide such services. For instance, like many medication adherence services, Validic can recognize when time has gone by without a patient reporting that’s he’s taken his pill. This level of monitoring requires storing large amounts of longitudinal data–and in fact, Validic is storing all transactions carried out over its platform. The value of such a large data set for discovering future health care solutions through analytics can make data scientists salivate.

The next segment of this article wraps up coverage of the conference with two more themes.

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 1 of 3)

Posted on November 13, 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.

Attendees at each Connected Health Conference know by now the architecture of health reform promoted there. The term “connected health” has been associated with a sophisticated amalgam of detailed wellness plans, modern sensors, continuous data collection in the field, patient control over data, frequent alerts and reminders, and analytics to create a learning health care system. The mix remains the same each year, so I go each time to seek out progress toward the collective goal. This year, I’ve been researching what’s happening in these areas:

  • Validation through clinical trials
  • Advanced interfaces to make user interaction easier
  • Improved data sharing (interoperability)
  • Blockchains

Panel at Connected Health Conference

Panel at Connected Health Conference

There were a few other trends of interest, which I’ll mention briefly here. Virtual reality (VR) and augmented reality (AR) turned up at some exhibitor booths and were the topic of a panel. Some of these technologies run on generic digital devices–such as the obsession-inducing Pokémon GO game–while others require special goggles such as the Oculus Rift (the first VR technology to show a promise for widespread adoption, and now acquired by Facebook) or Microsoft’s HoloLens. VR shuts out the user’s surroundings and presents her with a 360-degree fantasy world, whereas AR imposes information or images on the surroundings. Both VR and AR are useful for teaching, such as showing an organ in 3D organ in front of a medical student on a HoloLens, and rotating it or splitting it apart to show details.

I haven’t yet mentioned the popular buzzword “telehealth,” because it’s subsumed under the larger goal of connected health. I do use the term “artificial intelligence,” certainly a phrase that has gotten thrown around too much, and whose meaning is subject of much dissension. Everybody wants to claim the use of artificial intelligence, just as a few years ago everybody talked about “the cloud.” At the conference, a panel of three experts took up the topic and gave three different definitions of the term. Rather than try to identify the exact algorithms used by each product in this article and parse out whether they constitute “real” artificial intelligence, I go ahead and use the term as my interviewees use it.

Exhibition hall at Connected Health Conference

Exhibition hall at Connected Health Conference

Let’s look now at my main research topics.

Validation through clinical trials
Health apps and consumer devices can be marketed like vitamin pills, on vague impressions that they’re virtuous and that doing something is better than doing nothing. But if you want to hook into the movement for wellness–connected health–you need to prove your value to the whole ecosystem of clinicians and caretakers. The consumer market just doesn’t work for serious health care solutions. Expecting an individual to pay for a service or product would limit you to those who can afford it out-of-pocket, and who are concerned enough about wellness to drag out their wallets.

So a successful business model involves broaching the gates of Mordor and persuading insurers or clinicians to recommend your solution. And these institutions won’t budge until you have trials or studies showing that you actually make a difference–and that you won’t hurt anybody.

A few savvy app and device developers build in such studies early in their existence. For instance, last year I covered a typical connected health solution called Twine Health, detailing their successful diabetes and hypertension trials. Twine Health combines the key elements that one finds all over the Connected Health Conference: a care plan, patient tracking, data analysis, and regular check-ins. Their business model is to work with employer-owned health plans, and to expand to clinicians as they gradually migrate to fee-for-value reimbursement.

I sense that awareness is growing among app and device developers that the way to open doors in health care is to test their solutions rigorously and objectively. But I haven’t found many who do so yet.

In the next segment of this article continues my exploration of the key themes I identified at the start of this article.

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.

Alexa Can Truly Give Patients a Voice in Their Health Care (Part 1 of 3)

Posted on October 16, 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 leading pharmaceutical and medical company Merck, together with Amazon Web Services, has recently been exploring the potential health impacts of voice interfaces and natural language processing (NLP) through an Alexa Diabetes Challenge. I recently talked to the five finalists in this challenge. This article explores the potential of new interfaces to transform the handling of chronic disease, and what the challenge reveals about currently available technology.

Alexa, of course, is the ground-breaking system that brings everyday voice interaction with computers into the home. Most of its uses are trivial (you can ask about today’s weather or change channels on your TV), but one must not underestimate the immense power of combining artificial intelligence with speech, one of the most basic and essential human activities. The potential of this interface for disabled or disoriented people is particularly intriguing.

The diabetes challenge is a nice focal point for exploring the more serious contribution made by voice interfaces and NLP. Because of the alarming global spread of this illness, the challenge also presents immediate opportunities that I hope the participants succeed in productizing and releasing into the field. Using the challenge’s published criteria, the judges today announced Sugarpod from Wellpepper as the winner.

This article will list some common themes among the five finalists, look at the background about current EHR interfaces and NLP, and say a bit about the unique achievement of each finalist.

Common themes

Overlapping visions of goals, problems, and solutions appeared among the finalists I interviewed for the diabetes challenge:

  • A voice interface allows more frequent and easier interactions with at-risk individuals who have chronic conditions, potentially achieving the behavioral health goal of helping a person make the right health decisions on a daily or even hourly basis.

  • Contestants seek to integrate many levels of patient intervention into their tools: responding to questions, collecting vital signs and behavioral data, issuing alerts, providing recommendations, delivering educational background material, and so on.

  • Services in this challenge go far beyond interactions between Alexa and the individual. The systems commonly anonymize and aggregate data in order to perform analytics that they hope will improve the service and provide valuable public health information to health care providers. They also facilitate communication of crucial health data between the individual and her care team.

  • Given the use of data and AI, customization is a big part of the tools. They are expected to determine the unique characteristics of each patient’s disease and behavior, and adapt their advice to the individual.

  • In addition to Alexa’s built-in language recognition capabilities, Amazon provides the Lex service for sophisticated text processing. Some contestants used Lex, while others drew on other research they had done building their own natural language processing engines.

  • Alexa never initiates a dialog, merely responding when the user wakes it up. The device can present a visual or audio notification when new material is present, but it still depends on the user to request the content. Thus, contestants are using other channels to deliver reminders and alerts such as messaging on the individual’s cell phone or alerting a provider.

  • Alexa is not HIPAA-compliant, but may achieve compliance in the future. This would help health services turn their voice interfaces into viable products and enter the mainstream.

Some background on interfaces and NLP

The poor state of current computing interfaces in the medical field is no secret–in fact, it is one of the loudest and most insistent complaints by doctors, such as on sites like KevinMD. You can visit Healthcare IT News or JAMA regularly and read the damning indictments.

Several factors can be blamed for this situation, including unsophisticated electronic health records (EHRs) and arbitrary reporting requirements by Centers for Medicare & Medicaid Services (CMS). Natural language processing may provide one of the technical solutions to these problems. The NLP services by Nuance are already famous. An encouraging study finds substantial time savings through using NLP to enter doctor’s insights. And on the other end–where doctors are searching the notes they previously entered for information–a service called Butter.ai uses NLP for intelligent searches. Unsurprisingly, the American Health Information Management Association (AHIMA) looks forward to the contributions of NLP.

Some app developers are now exploring voice interfaces and NLP on the patient side. I covered two such companies, including the one that ultimately won the Alexa Diabetes Challenge, in another article. In general, developers using these interfaces hope to eliminate the fuss and abstraction in health apps that frustrate many consumers, thereby reaching new populations and interacting with them more frequently, with deeper relationships.

The next two parts of this article turn to each of the five finalists, to show the use they are making of Alexa.