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!!

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.

The Healthcare IT Field is Unique, Yorktel Discovers

Posted on September 11, 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.

Health care professionals love to vaunt the uniqueness of the medical industry, and tend to demand special, expensive treatment on that basis. Reformers tend to discount this special status. (For instance, the security problems in health care are identical to those in other industries, and are caused by the same factors of insufficient investment and training.) Yet telecommunications in hospitals and clinics really is special, and video giant Yorktel has spent the past five years adjusting to that reality. On September 5, Yorktel announced that it has enhanced its solutions for patient telemedicine with Univago HE that includes robust video connections, monitoring, and analytics as a service.

To learn how the company enhanced their video teleconferencing for healthcare, I recently talked to Peter McLain, Senior Vice President of Healthcare, and John Vitale, Senior Vice President of Project Management. They disassembled the various features of Univago that deal with hospital environments, which require reliable 24/7 connectivity, deal with a good deal of noise (both audible and electronic), and demand fast, faultless authorization to protect privacy.

Directional audio

The triangular table-top sets, familiar to so many of us from business teleconferencing, are omni-directional in order to facilitate use by people seated around the table. In a hospital, they pick up the whirr of carts going by, the chatter in the hallway, and the beeps and gurgles of machines in the patients’ rooms themselves. So Yorktel had to substitute directional microphones.

Camera positioning

Remote monitoring requires much more detail than talking heads in a teleconference. For instance, a remote nurse may want to check whether an IV bag is getting empty. So the person on the remote end of the video connection can direct the camera at particular points in the room and zoom in. Originally offering joystick-like controls for this purpose, Yorktel found them too confusing and cumbersome, so they created a system where a user can just double-click on her own screen to focus in on the place she indicated.

Infrared cameras

Remote monitoring takes place continuously, including when the room is dark. The staff don’t want to wake the patient while monitoring him, so Yorktel cameras support the display of scenes scanned from infrared light. A mild alert, such as a soft buzz, lets an awake patient know that he’s being monitored, without disturbing a sleeping patient.

Integration with dashboards

Yorktel software can be seamlessly integrated with other applications so that staff can see vital signs and other data while in a video call. The developers have made the systems adhere to relevant standards, including Skype, Web RTC, and H.323.

Robustness

Conventional business teleconference systems are used for a few hours each day; hospital systems are used 24/7 and must promise long mean times between failures. Yorktel addressed this on both a hardware and a software level. In hardware, they broke down large, integrated components into modules that would be easy to replace. In software, they built a custom operating system on Unix, feeling that would offer maximum reliability. They use artificial intelligence techniques to detect whether the camera has frozen (a common failure) and reboot the system before it interferes with a video session. Components can still fail, but McLain says they can be replaced within 15 minutes instead of 3 to 6 hours.

Security

Yorktel has hardened its authentication and authorization process to make sure that no one at random can dial into a system and see a patient in his bed. At the same time, they have integrated that process into mobile devices so the physician can check in from home or the road in case of an emergency.

The systems follow industry best practices, as specified by the ISO 27001 security standard and HIPAA. In order to expand into UK’s National Health Service and the European Union, Yorktel achieved Privacy Shield certification. They also get penetration testing from a third party expert, and incorporate anti-microbial technology into their systems. The systems are pending approval as Class 1 medical devices (the most reliable level of use) by the FDA.

Following security by design principles, Yorktel maintains no information for a patient. A physician finds the right room through an external service and calls that room. (If the patient wants to be called, he presses a button by the bedside, and a message is sent through some appropriate alert, such as a text message or a flashing screen.) No information on the traffic is preserved, and the call records have no personally identifying information.

Specialized services

Each department in a hospital has different needs, and Yorktel has provided specific enhancements to make their systems more useful in various settings.

For instance, family visits are an excellent use case for videoconferencing. A session can be shared with family members who can’t get in to the hospital. It can also be recorded and saved by the hospital (as mentioned earlier, Yorktel does not preserve session traffic) so it can be viewed again or brought out to prove that the hospital fulfilled its responsibility. To enable family visits, Yorktel allows the staff to designate members of the call as guests. The visitors are called “guests” because they have no control over the systems, but can see and hear what goes on during the session.

For general use in medical settings, Yorktel also allows sidebar conversations. The patient can be put on hold while physicians discuss treatment candidly and privately among themselves.

Via these enhancements targeted at hospitals and clinics, Yorktel has expanded its business in health care. It started with a common application, remote monitoring in the ICU, but expanded to telestroke care, family health, behavioral health, and translational services. They also knew that hospitals already have expensive, dedicated systems for many of these tasks, and don’t want to throw them away, especially if the outcome is to be locked in yet again to some proprietary system. Hence Yorktel’s dedication to standards.

Currently, video conferencing in the hospital is so expensive that it tends to be restricted to ICUs and a few other applications. Ultimately, Yorktel’s subscription plans should offer systems at a low enough cost that they can be deployed universally in hospitals and clinics.

What can other technology developers, outside of two-way video, learn about health care from the Yorktel experience? Most of all, go into the environments where you want your systems used and get to know the needs and workflows of the participants. Systems must be flexible, because each user is different. The systems must also be secure from the ground up, robust, and conformant to standards. Cost is also an important issue in most settings, particularly given the cuts in reimbursement that are widespread.

As it designs systems to interact along standards with other vendors, Yorktel’s strength in software has grown exponentially. This parallels trends throughout many industries, from manufacturers through retailers. Marc Andreessen famously said in 2011 that software is eating the world, and along these line, many analysts say that all companies will soon be software companies–or be drowned by their more agile competition. In this sense, we can all learn from Yorktel.

Analytics Take an Unusual Turn at PeraHealth

Posted on August 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.

Data scientists in all fields have learned to take data from unusual places. You’d think that monitoring people in a hospital for changes in their conditions would be easier than other data-driven tasks, such as tracking planets in far-off solar systems, but in all cases some creativity is needed. That’s what PeraHealth, a surveillance system for hospital patients, found out while developing alerts for clinicians.

It’s remarkably hard to identify at-risk patients in hospitals, even with so many machines and staff busy monitoring them. For instance, a nurse on each shift may note in the patient’s record that certain vital signs are within normal range, and no one might notice that the vital signs are gradually trending worse and worse–until a crisis occurs.

PeraHealth identifies at-risk patients through analytics and dashboards that doctors and nurses can pull up. They can see trends over a period of several shifts, and quickly see which patients in the ward are the most at risk. PeraHealth is a tool for both clinical surveillance and communication.

Michael Rothman, co-founder and Chief Science Officer, personally learned the dangers of insufficient monitoring in 2003 when a low-risk operation on his mother led to complications and her unfortunate death. Rothman and his brother decided to make something positive from the tragedy. They got permission from the hospital to work there for three weeks, applying Michael’s background in math and data analysis (he has worked in the AI department of IBM’s Watson research labs, among other places) and his brother’s background in data visualization. Their goal, arguably naive: to find a single number that summarizes patient risk, and expose that information in a usable way to clinicians.

Starting with 70 patients from the cardiac unit, they built a statistical model that they tested repeatedly with 1,200 patients, 6,000 patients, and finally 25,000 patients. At first they hoped to identify extra data that the nurse could enter into the record, but the chief nurse laid down, in no uncertain terms, that the staff was already too busy and that collecting more data was out of the question. It came time to get creative with data that was already being collected and stored.

The unexpected finding was that vital signs were not a reliable basis for assessing a patient’s trends. Even though they’re “hard” (supposedly objective) data, they bounce around too much.

Instead of relying on just vital signs, PeraHealth also pulls in nursing assessments–an often under-utilized source of information. On each shift, a nurse records information on a dozen different physical systems as well as essential facts such as whether a patient stopping eating or was having trouble walking. It turns out that this sort of information reliably indicates whether there’s a problem. Many of the assessments are simple, yes/no questions.

Rothman analyzed hospital data to find variables that predicted risk. For instance, he compared the heart rates of 25,000 patients before they left the hospital and checked who lived for a year longer. The results formed a U-shaped curve, showing that heart rates above a certain level or below a certain level predicted a bad outcome. It turns out that this meaure works equally well within the hospital, helping to predict admission to the ICU, readmission to the ICU, and readmission after discharge.

The PeraHealth team integrated their tool with the hospital’s EHR and started producing graphs for the clinicians in 2007. Now they can point to more than 25 peer-reviewed articles endorsing their approach, some studies comparing before-and-after outcomes, and others comparing different parts of the hospital with some using PeraHealth and others not using it. The service is now integrated with major EHR vendors.

PeraHealth achieved Rothman’s goal of producing a single meaningful score to rate patient risk. Each new piece of data that goes into the EHR triggers a real-time recalculation of the score and a new dot on a graph presented to the nurses. In order to save the nurses from signing into the EHR, PeraHealth put a dashboard on the nurse’s kiosk with all the patients’ graphs. Color-coding denotes which patients are sickest. PeraHealth also shows which patients to attend to first. In case no one looks at the screen, at some hospitals the system sends out text alerts to doctors about the most concerned patients.

PeraHealth is now expanding. In an experiment, they did phone interviews with people in a senior residential facility, and identified many of those who were deteriorating. So the basic techniques may be widely applicable to data-driven clinical decision support. But without analytics, one never knows which data is most useful.

Wellpepper and SimplifiMed Meet the Patients Where They Are Through Modern Interaction Techniques

Posted on August 9, 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.

Over the past few weeks I found two companies seeking out natural and streamlined ways to connect patients with their doctors. Many of us have started using web portals for messaging–a stodgy communication method that involves logins and lots of clicking, often just for an outcome such as message “Test submitted. No further information available.” Web portals are better than unnecessary office visits or days of playing phone tag, and so are the various secure messaging apps (incompatible with one another, unfortunately) found in the online app stores. But Wellpepper and SimplifiMed are trying to bring us a bit further into the twenty-first century, through voice interfaces and natural language processing.

Wellpepper’s Sugarpod

Wellpepper recently ascended to finalist status in the Alexa Diabetes Challenge, which encourages research into the use of Amazon.com’s popular voice-activated device, Alexa, to improve the lives of people with Type 2 Diabetes. For this challenge, Wellpepper enhanced its existing service to deliver messages over Amazon Echo and interview patients. Wellpepper’s entry in the competition is an integrated care plan called Sugarpod.

The Wellpepper platform is organized around a care plan, and covers the entire cycle of treatment, such as delivering information to patients, managing their medications and food diaries, recording information from patients in the health care provider’s EHR, helping them prepare for surgery, and more. Messages adapt to the patient’s condition, attempting to present the right tone for adherent versus non-adherent patients. The data collected can be used for analytics benefitting both the provider and the patient–valuable alerts, for instance.

It must be emphasized at the outset that Wellpepper’s current support for Alexa is just a proof of concept. It cannot be rolled out to the public until Alexa itself is HIPAA-compliant.

I interviewed Anne Weiler, founder and CEO of Wellpepper. She explained that using Alexa would be helpful for people who have mobility problems or difficulties using their hands. The prototype proved quite popular, and people seem willing to open up to the machine. Alexa has some modest affective computing features; for instance, if the patient reports feeling pain, the device will may respond with “Ouch!”

Wellpepper is clinically validated. A study of patients with Parkinson’s showed that those using Wellpepper showed 9 percent improvement in mobility, whereas those without it showed a 12% decline. Wellpepper patients adhered to treatment plans 81% of the time.

I’ll end this section by mentioning that integration EHRs offer limited information of value to Wellpepper. Most EHRs don’t yet accept patient data, for instance. And how can you tell whether a patient was admitted to a hospital? It should be in the EHR, but Sugarpod has found the information to be unavailable. It’s especially hidden if the patient is admitted to a different health care providers; interoperability is a myth. Weiler said that Sugarpod doesn’t depend on the EHR for much information, using a much more reliable source of information instead: it asks the patient!

SimplifiMed

SimplifiMed is a chatbot service that helps clinics automate routine tasks such as appointments, refills, and other aspects of treatment. CEO Chinmay A. Singh emphasized to me that it is not an app, but a natural language processing tool that operates over standard SMS messaging. They enable a doctor’s landline phone to communicate via text messages and route patients’ messages to a chatbot capable of understanding natural language and partial sentences. The bot interacts with the patients to understand their needs, and helps them accomplish the task quickly. The result is round-the-clock access to the service with no waiting on the phone a huge convenience to busy patients.

SimplifiMed also collects insurance information when the patient signs up, and the patient can use the interface to change the information. Eventually, they expect the service to analyze patient’s symptom in light of data from the EHR and help the patient make the decision about whether to come in to the doctor.

SMS is not secure, but HIPAA does not get violated because the patient can choose what to send to the doctor, and the chatbot’s responses contain no personally identifiable information. Between the doctor and the SimplifiMed service, data is sent in encrypted form. Singh said that the company built its own natural language processing engine, because it didn’t want to share sensitive patient data with an outside service.

Due to complexity of care, insurance requirements, and regulations, a doctor today needs support from multiple staff members: front desk, MA, biller, etc. MACRA and value-based care will increase the burden on staff without providing the income to hire more. Automating routine activities adds value to clinics without breaking the bank.

Earlier this year I wrote about another company, HealthTap, that had added Alexa integration. This trend toward natural voice interfaces, which the Alexa Diabetes Challenge finalists are also pursuing, along with the natural language processing that they and SimplifiMed are implementing, could put health care on track to a new era of meeting patients where they are now. The potential improvements to care are considerable, because patients are more likely to share information, take educational interventions seriously, and become active participants in their own treatment.

Hands-On Guidance for Data Integration in Health: The CancerLinQ Story

Posted on June 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.

Institutions throughout the health care field are talking about data sharing and integration. Everyone knows that improved care, cost controls, and expanded research requires institutions who hold patient data to safely share it. The American Society of Clinical Oncology’s CancerLinQ, one of the leading projects analyzing data analysis to find new cures, has tackled data sharing with a large number of health providers and discovered just how labor-intensive it is.

CancerLinQ fosters deep relationships and collaborations with the clinicians from whom it takes data. The platform turns around results from analyzing the data quickly and to give the clinicians insights they can put to immediate use to improve the care of cancer patients. Issues in collecting, storing, and transmitting data intertwine with other discussion items around cancer care. Currently, CancerLinQ isolates the data from each institution, and de-identifies patient information in order to let it be shared among participating clinicians. CancerLinQ LLC is a wholly-owned nonprofit subsidiary of ASCO, which has registered CancerLinQ as a trademark.

CancerLinQ logo

Help from Jitterbit

In 2015, CancerLinQ began collaborating with Jitterbit, a company devoted to integrating data from different sources. According to Michele Hazard, Director of Healthcare Solutions, and George Gallegos, CEO, their company can recognize data from 300 different sources, including electronic health records. At the beginning, the diversity and incompatibility of EHRs was a real barrier. It took them several months to figure out each of the first EHRs they tackled, but now they can integrate a new one quickly. Oncology care, the key data needed by CancerLinQ, is a Jitterbit specialty.

Jitterbit logo

One of the barriers raised by EHRs is licensing. The vendor has to “bless” direct access to EHR and data imported from external sources. HIPAA and licensing agreements also make tight security a priority.

Another challenge to processing data is to find records in different institutions and accurately match data for the correct patient.

Although the health care industry is moving toward the FHIR standard, and a few EHRs already expose data through FHIR, others have idiosyncratic formats and support older HL7 standards in different ways. Many don’t even have an API yet. In some cases, Jitterbit has to export the EHR data to a file, transfer it, and unpack it to discover the patient data.

Lack of structure

Jitterbit had become accustomed to looking in different databases to find patient information, even when EHRs claimed to support the same standard. One doctor may put key information under “diagnosis” while another enters it under “patient problems,” and doctors in the same practice may choose different locations.

Worse still, doctors often ignore the structured fields that were meant to hold important patient details and just dictate or type it into a free-text note. CancerLinQ anticipated this, unpacking the free text through optical character recognition (OCR) and natural language processing (NLP), a branch of artificial intelligence.

It’s understandable that a doctor would evade the use of structured fields. Just think of the position she is in, trying to keep a complex cancer case in mind while half a dozen other patients sit in the waiting room for their turn. In order to use the structured field dedicated to each item of information, she would have to first remember which field to use–and if she has privileges at several different institutions, that means keeping the different fields for each hospital in mind.

Then she has to get access to the right field, which may take several clicks and require movement through several screens. The exact information she wants to enter may or may not be available through a drop-down menu. The exact abbreviation or wording may differ from EHR to EHR as well. And to carry through a commitment to using structured fields, she would have to go through this thought process many times per patient. (CancerLinQ itself looks at 18 Quality eMeasures today, with the plan to release additional measures each year.)

Finally, what is the point of all this? Up until recently, the information would never come back in a useful form. To retrieve it, she would have to retrace the same steps she used to enter the structured data in the first place. Simpler to dump what she knows into a free-text note and move on.

It’s worth mentioning that this Babyl of health care information imposes negative impacts on the billing and reimbursement process, even though the EHRs were designed to support those very processes from the start. Insurers have to deal with the same unstructured data that CancerLinQ and Jitterbit have learned to read. The intensive manual process of extracting information adds to the cost of insurance, and ultimately the entire health care system. The recent eClinicalWorks scandal, which resembles Volkswagon’s cheating on auto emissions and will probably spill out to other EHR vendors as well, highlights the failings of health data.

Making data useful

The clue to unblocking this information logjam is deriving insights from data that clinicians can immediately see will improve their interventions with patients. This is what the CancerLinQ team has been doing. They run analytics that suggest what works for different categories of patients, then return the information to oncologists. The CancerLinQ platform also explains which items of data were input to these insights, and urges the doctors to be more disciplined about collecting and storing the data. This is a human-centered, labor-intensive process that can take six to twelve months to set up for each institution. Richard Ross, Chief Operating Officer of CancerLinQ calls the process “trench warfare,” not because its contentious but because it is slow and requires determination.

Of the 18 measures currently requested by CancerLinQ, one of the most critical data elements driving the calculation of multiple measures is staging information: where the cancerous tumors are and how far it has progressed. Family history, treatment plan, and treatment recommendations are other examples of measures gathered.

The data collection process has to start by determining how each practice defines a cancer patient. The CancerLinQ team builds this definition into its request for data. Sometimes they submit “pull” requests at regular intervals to the hospital or clinic, whereas other times the health care provider submits the data to them at a time of its choosing.

Some institutions enforce workflows more rigorously than others. So in some hospitals, CancerLinQ can persuade the doctors to record important information at a certain point during the patient’s visit. In other hospitals, doctors may enter data at times of their own choosing. But if they understand the value that comes from this data, they are more likely to make sure it gets entered, and that it conforms to standards. Many EHRs provide templates that make it easier to use structured fields properly.

When accepting information from each provider, the team goes through a series of steps and does a check-in with the provider at each step. The team evaluates the data in a different stage for each criterion: completeness, accuracy of coding, the number of patients reported, and so on. By providing quick feedback, they can help the practice improve its reporting.

The CancerLinQ/Jitterbit story reveals how difficult it is to apply analytics to health care data. Few organizations can afford the expertise they apply to extracting and curating patient data. On the other hand, CancerLinQ and Jitterbit show that effective data analysis can be done, even in the current messy conditions of electronic data storage. As the next wave of technology standards, such as FHIR, fall into place, more institutions should be able to carry out analytics that save lives.