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How Do We Include Every Generation in our HIT? – #HITsm Chat Topic

Posted on March 14, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 3/17 at Noon ET (9 AM PT). This week’s chat will be hosted by Erica Johansen with Splash Media (@thegr8chalupa and @SplashMedia). We’ll be discussing the topic “How Do We Include Every Generation in our HIT?”

Commentary on generational nuances have made their way into previous #HITsm chats and those comments usually sparked quite a discussion. So, we couldn’t think of a better way to give the people what they want!

This week, our chat will spark conversations on how generational perspectives are influencing healthcare technology, and additionally, how can we (as health IT leaders) can strive to incorporate and include diverse generational needs into the industry roadmap.

Be sure to join the #HITsm chat this Friday, March 17th, 2017 at 12:00pm ET.

The Topics
T1: What generation do you identify with & are there any stereotypes that you feel are inaccurate? #HITsm

T2: What #healthIT initiatives are setting up future generations for success? What initiatives are setting up for failure? #HITsm

T3: Is there a health concern might you share with someone in another generation? If so, how could #healthIT anticipate that concern? #HITsm

T4: If you could advise #healthIT leadership on behalf of your generation, what would you tell them? #HITsm

T5: In what ways can #healthIT cultivate meaningful engagement from every generation? #HITsm

Bonus: What healthcare technology that exists today holds untapped potential? #HITsm

Upcoming #HITsm Chat Schedule
3/24 – How Technology Helps and Hurts Healthy Behavior Change
Hosted by @MelissaxxMcCool

3/31 – AI and Cognitive Science
Hosted by @HBI_Solutions

4/7 – TBD

4/14 – TBD

4/21 – Innovation vs Incremental
Hosted by @Colin_Hung

We look forward to learning from the #HITsm community! As always let us know if you have ideas for how to make #HITsm better.

If you’re searching for the latest #HITsm chat, you can always find the latest #HITsm chat and schedule of chats here.

AMIA Asks NIH To Push For Research Data Sharing

Posted on January 23, 2017 I Written By

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

The American Medical Informatics Association has is urging leaders at the NIH to take researchers’ data sharing plans into account when considering grant proposals.

AMIA is responding to an NIH Request for Information (topic: “Strategies for NIH Data Management, Sharing and Citation”) was published in November 2016. In the RFI, it asked for feedback on how digital scientific data generated by NIH-funded research should be managed and disclosed to the public. It also asked for input on how to set standards for citing shared data and software.

In its response, AMIA said that the agency should give researchers “institutional incentives” designed to boost data sharing and strengthen data management. Specifically, the trade group suggested that NIH make data sharing plans a “scoreable” part of grant applications.

“Data sharing has become such an important proximal output of research that we believe the relative value of a proposed project should include consideration of how its data will be shared,” AMIA said in its NIH response. “By using the peer-review process, we will make incremental improvements to interoperability, while identifying approaches to better data sharing practices over time.”

To help the agency implement this change, AMIA recommended that applicants earmark funds for data curation and sharing as part of the grants’ direct costs. Doing so will help assure that data sharing becomes part of research ecosystems.

AMIA also recommends that NIH offer rewards to scholars who either create or contribute to publicly-available datasets and software. The trade group argues that such incentives would help those who create and analyze data advance their careers. (And this, your editor notes, would help foster a virtuous cycle in which data-oriented scientists are available to foster such efforts.)

Right now, to my knowledge, few big data integration projects include the kind of front-line research data we’re talking about here.  On the other hand, while few community hospitals are likely to benefit from research data in the near term, academic medical organizations are having a bit more luck, and offer us an attractive picture of how things could be.

For example, look at this project at Vanderbilt University Medical Center which collects and manages translational and clinical research data via an interface with its EMR system.

At Vanderbilt, research data collection is integrated with clinical EMR use. Doctors there use a module within the research platform (known as REDCap) to collect data for prospective clinical studies. Once they get their research project approved, clinicians use menus to map health record data fields to REDCap. Then, REDCap automatically retrieves health record data for selected patients.

My feeling is that if NIH starts pushing grantees to share data effectively, we’ll see more projects like REDCap, and in turn, better clinical care supported by such research. It looks to me like everybody wins here. So I hope the NIH takes AMIA’s proposal seriously.

IBM Watson Partners With FDA On Blockchain-Driven Health Sharing

Posted on January 16, 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.

IBM Watson Health has partnered with the FDA in an effort to create scalable exchange of health data using blockchain technology. The two will research the exchange of owner-mediated data from a variety of clinical data sources, including EMRs, clinical trial data and genomic health data. The researchers will also incorporate data from mobiles, wearables and the Internet of Things.

The initial project planned for IBM Watson and the FDA will focus on oncology-related data. This makes sense, given that cancer treatment involves complex communication between multispecialty care teams, transitions between treatment phases, and potentially, the need to access research and genomic data for personalized drug therapy. In other words, managing the communication of oncology data is a task fit for Watson’s big brain, which can read 200 million pages of text in 3 seconds.

Under the partnership, IBM and the FDA plan to explore how the blockchain framework can benefit public health by supporting information exchange use cases across varied data types, including both clinical trials and real-world data. They also plan to look at new ways to leverage the massive volumes of diverse data generated by biomedical and healthcare organizations. IBM and the FDA have signed a two-year agreement, but they expect to share initial findings this year.

The partnership comes as IBM works to expand its commercial blockchain efforts, including initiatives not only in healthcare, but also in financial services, supply chains, IoT, risk management and digital rights management. Big Blue argues that blockchain networks will spur “dramatic change” for all of these industries, but clearly has a special interest in healthcare.  According to IBM, Watson Health’s technology can access the 80% of unstructured health data invisible to most systems, which is clearly a revolution in the making if the tech giant can follow through on its potential.

According to Scott Lundstrom, group vice president and general manager of IDC Government and Health Insights, blockchain may solve some of the healthcare industry’s biggest data management challenges, including a distributed, immutable patient record which can be secured and shared, s. In fact, this idea – building a distributed, blockchain-based EMR — seems to be gaining traction among most health IT thinkers.

As readers may know, I’m neither an engineer nor a software developer, so I’m not qualified to judge how mature blockchain technologies are today, but I have to say I’m a bit concerned about the rush to adopt it nonetheless.  Even companies with a lot at stake  — like this one, which sells a cloud platform backed by blockchain tech — suggest that the race to adopt it may be a bit premature.

I’ve been watching tech fashions come and go for 25 years, and they follow a predictable pattern. Or rather, they usually follow two paths. Go down one, and the players who are hot for a technology put so much time and money into it that they force-bake it into success. (Think, for example, the ERP revolution.) Go down the other road, however, and the new technology crumbles in a haze of bad results and lost investments. Let’s hope we go down the former, for everyone’s sake.

A New Meaning for Connected Health at 2016 Symposium (Part 4 of 4)

Posted on November 8, 2016 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 section of this article continued our exploration of the integration of health care into daily life. This section wraps up the article with related insights, including some thoughts about the future.

Memorable moments
I had the chance to meet with Casper de Clercq, who has set up a venture capital plan devoted to health as a General Partner at Norwest Venture Partners. He recommends that manufacturers and clinicians give patients a device that collects data while doing something else they find useful, so that they are motivated to keep wearing it. As an example, he cited the Beddit sleep tracker, which works through sensors embedded (no pun intended) in the user’s bed.

He has found that successful companies pursue gradual, incremental steps toward automated programs. It is important to start with a manual process that works (such as phoning or texting patients from the provider), then move to semi-automation and finally, if feasible, full automation. The product must also be field-tested; one cannot depend on a pilot. This advice matches what Glen Tullman, CEO of Livongo Health, said in his keynote: instead of doing a pilot, try something out in the field and change quickly if it doesn’t work.

Despite his call for gradual change, de Clercq advises that companies show an ROI within one year–otherwise, the field of health care may have evolved and the solution may be irrelevant.

He also recommends a human component in any health program. The chief barrier to success is getting the individual to go along with both the initial activation and continuing motivation. Gamification, behavioral economics, and social connections can all enhance this participation.

A dazzling keynote on videogames for health was delivered by Adam Gazzaley, who runs Neuroscience labs at the University of California at San Francisco. He pointed out that conventional treatments get feedback on patient reactions far too slowly–sometimes months after the reaction has occurred. In the field of mental health, His goal is to supplement (not replace) medications with videogames, and to provide instant feedback to game players and their treatment staff alike. Videogames not only provide a closed-loop system (meaning that feedback is instantaneous), but also engage patients by being fun and offering real-time rewards. Attention spans, anxiety, and memory are among the issues he expects games to improve. Education and wellness are also on his game plan. This is certainly one talk where I did not multitask (which is correlated with reduced performance)!

A future, hopefully bigger symposium
The Connected Health symposium has always been a production of the Boston Partners Health Care conglomerate, a part of their Connected Health division. The leader of the division, Dr. Joseph Kvedar, introduced the symposium by expressing satisfaction that so many companies and organizations are taking various steps to make connected health a reality, then labeled three areas where leadership is still required:

  • Reassuring patients that the technologies and practices work for them. Most people will be willing to adopt these practices when urged by their doctors. But their privacy must be protected. This requires low-cost solutions to the well-known security problems in EHRs and devices–the latter being part of the Internet of Things, whose vulnerability was exposed by the recent attack on Dyn and other major Internet sites.

  • Relieving the pressures on clinicians. Kvedar reported that 45 percent of providers would like to adopt connected health practices, but only 12 percent do so. One of the major concerns holding them back is the possibility of data overload, along with liability for some indicator of ill health that they miss in the flood of updates. Partners Connected Health will soon launch a provider adoption initiative that deals with their concerns.

  • Scaling. Pilot projects in connected health invest a lot of researcher time and offers a lot of incentives to develop engagement among their subjects. Because engagement is the whole goal of connected health, the pilot may succeed but prove hard to turn into a widespread practice. Another barrier to scaling is consumers’ lack of tolerance for the smallest glitches or barriers to adoption. Providers, also, insist that new practices fit their established workflows.

Dr. Kvedar announced at this symposium that they would be doing future symposia in conjunction with the Personal Connected Health Alliance (Formerly the mHealth Summit owned by HIMSS), a collaboration that makes sense. Large as Partners Health Care is, the symposium reaches much farther into the health care industry. The collaboration should bring more resources and more attendees, establishing the ideals of connected health as a national and even international movement.

A New Meaning for Connected Health at 2016 Symposium (Part 3 of 4)

Posted on November 7, 2016 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 section of this article paused during a discussion of the accuracy and uses of devices. At a panel on patient generated data, a speaker said that one factor holding back the use of patient data was the lack of sophistication in EHRs. They must be enhanced to preserve the provenance of data: whether it came from a device or from a manual record by the patient, and whether the device was consumer-grade or a well-tested medical device. Doctors invest different levels of trust in different methods of collecting data: devices can provide more objective information than other ways of asking patients for data. A participant in the panel also pointed out that devices are more reliable in the lab than under real-world conditions. Consumers must be educated about the proper use of devices, such as whether to sit down and how to hold their arms when taking their blood pressure.

Costantini decried the continuing silos in both data sharing and health care delivery. She said only half of doctors share patient data with other doctors or caretakers. She also praised the recent collaboration between Philips and Qualcomm to make it easier for device data to get into medical records. Other organizations that have been addressing that issue for some time include Open mHealth, which I reviewed in an earlier article, and Validic.

Oozing into workflow
The biggest complaint I hear from clinicians about EHRs–aside from the time wasted in their use, which may be a symptom of the bigger problem-is that the EHRs disrupt workflow. Just as connected health must integrate with patient lives as seamlessly as possible, it should recognize how teams work and provide them with reasonable workflows. This includes not only entering existing workflows as naturally as capillary action, but helping providers adopt better ones.

The Veterans Administration is forging into this area with a new interface called the Enterprise Health Management Platform (eHMP). I mentioned it in a recent article on the future of the VA’s EHR. A data integration and display tool, eHMP is agnostic as to data source. It can be used to extend the VistA EHR (or potentially replace it) with other offerings. Although eHMP currently displays a modern dashboard format, as described in a video demo by Shane Mcnamee, the tool aims to be much more than that. It incorporates Business Process Modeling Notation (BPMN) and the WS-Human Task Specification to provide workflow support. The Activity Management Service in eHMP puts Clinical Best Practices directly into the workflow of health care providers.

Clinicians can use eHMP to determine where a consultation request goes; currently, the system is based on Red Hat’s BPMN engine. If one physician asks another to examine the patient, that task turns up on the receiving physician’s dashboard. Teams as well as individuals can be alerted to a patient need, and alerts can be marked as routine or urgent. The alerts can also be associated with time-outs, so that their importance is elevated if no one acts on them in the chosen amount of time.

eHMP is just in the beginning stages of workflow support. Developers are figuring out how to increase the sophistication of alerts, so that they offer a higher signal-to-noise ratio than most hospital CDS systems, and add intelligence to choose the best person to whom an alert should be directed. These improvements will hopefully free up time in the doctor’s session to discuss care in depth–what both patients and providers have long said they most want from the health care field.

At the Connected Health symposium, I found companies working on workflow as well. Dataiku (whose name is derived from “haiku”) has been offering data integration and analytics in several industries for the past three years. Workflows, including conditional branches and loops, can be defined through a graphical interface. Thus, a record may trigger a conditional inquiry: does a lab value exceed normal limits? if not, it is merely recorded, but if so, someone can be alerted to follow up.

Dataiku illustrates an all-in-one, comprehensive approach to analytics that remains open to extensions and integration with other systems. On the one hand, it covers the steps of receiving and processing data pretty well.

To clean incoming data (the biggest task on most data projects), their DSS system can use filters and even cluster data to find patterns. For instance, if 100 items list “Ohio” for their location, and one lists “Oiho”, the system can determine that the outlier is a probably misspelling. The system can also assign data to belonging to broad categories (string or integer) as well as more narrowly defined categories (such as social security number or ZIP code).

For analysis, Dataiku offers generic algorithms that are in wide use, such as linear regressions, and a variety of advanced machine learning (artificial intelligence) algorithms in the visual backend of the program–so the users don’t need to write a single line of code. Advanced users can also add their own algorithms coded in a variety of popular languages such as Python, R, and SQL. The software platform offers options for less technically knowledgeable users, pre-packaged solutions for various industries such as health care, security features such as audits, and artificial intelligence to propose an algorithm that works on the particular input data.

Orbita Health handles workflows between patients and providers to help with such issues as pain management and medication adherence. The company addresses ease of use by supporting voice-activated devices such as Amazon Echo, as well as some 250 other devices. Thus, a patient can send a message to a provider through a single statement to a voice-activated device or over another Internet-connected device. For workflow management, the provider can load a care plan into the system, and use Orbita’s orchestration engine (similar to the Business Process Modeling Notation mentioned earlier) to set up activities, such as sending a response to a patient’s device or comparing a measurement to the patient’s other measurements over time. Orbita’s system supports conditional actions, nests, and trees.

CitiusTech, founded in 2005, integrates data from patient devices and apps into provider’s data, allowing enterprise tools and data to be used in designing communications and behavioral management in the patient’s everyday life. The company’s Integrated Analytix platform offer more than 100,000 apps and devices from third-party developers. Industry studies have shown effective use of devices, with one study showing a 40% reduction in emergency room admissions among congestive heart failure patients through the use of scales, engaging the patients in following health protocols at home.

In a panel on behavior change and the psychology of motivation, participants pointed out that long-range change requires multiple, complex incentives. At the start, the patient may be motivated by a zeal to regain lost functioning, or even by extrinsic rewards such as lower insurance premiums. But eventually the patient needs to enfold the exercise program or other practice into his life as a natural activity. Rewards can include things like having a beer at the end of a run, or sharing daily activities with friends on social media.

In his keynote on behavioral medicine, the Co-founder & CEO of Omada Health, Sean Duffy, put up a stunningly complex chart showing the incentives, social connections, and other factors that go into the public’s adoption of health practices. At a panel called “Preserving the Human Touch in the Expanding World of Digital Therapies”, a speaker also gave the plausible advice that we tell patients what we can give back to them when collecting data.

The next section of this article offers some memorable statements at the conference, and a look toward the symposium’s future.

A New Meaning for Connected Health at 2016 Symposium (Part 2 of 4)

Posted on November 4, 2016 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 section of this article talked about making health a routine part of everyday life, particularly where consumer devices are concerned. We’ll continue in this section with other considerations aired at the symposium.

Tullman’s principles of simplicity, cited in the previous section, can be applied to a wide range of health IT. For instance, AdhereTech pill bottles can notify the patient with a phone call or text message if she misses a dose. Another example of a technology that is easily integrated into everyday life is a thermometer built into a vaginal ring that a woman can insert and use without special activation. This device was mentioned by Costantini during her keynote. The device can alert a woman–and, if she wants, her partner–to when she is most fertile.

Super-compact devices and fancy interfaces are not always necessary for a useful intervention. In a keynote, John Dwyer, Jr., President of the Global Alzheimer’s Platform Foundation, discussed a simple survey that his organization got large numbers of people to take. They uncovered a lot of undiagnosed cases of mental decline. I imagine that the people who chose to take the survey were experiencing possible symptoms and therefore were concerned about their mental abilities. Yet they apparently had not expressed concerns to their doctors; instead they responded to the online suggestion to take a survey.

Most of us spend a large chunk of our day at work, so wellness programs there are theoretically promising. A panel on workplace-connected health solutions talked about some of the barriers:

  • Inadequate communications. Employees need to be informed regularly that a program is available, and its benefits

  • Privacy guarantees. Employees must feel assured of a firewall between their employer and the organization handling their sensitive data.

  • Clear goals. A wellness program is not just a check-off box. Employers must know what they want to achieve and design programs around these goals.

I would add that employers should examine their own environment honestly before setting up a wellness program. It’s pretty hypocritical to offer a wellness program on the one hand while subjecting employees to stress, overwork, and bad ergonomics on the other.

Telehealth is also likely to grow, and in fact, 200 bills to improve regulation of telehealth are pending in Congress. A speaker at a panel on preserving the human touch said that the Centers for Medicare & Medicaid Services are held back by uncertainty about how to measure telehealth’s value. Another speaker pointed out that we have a severe shortage of mental health professionals, and that many areas lack access to them. Telehealth may improve access.

It all comes down to the environment
Health care has to fully acknowledge the role of environmental factors in creating sickness. These include the marketing of fatty and sugary foods, the trapping of poor and minority people in areas with air and water pollution, the barriers to getting health care (sick leave, geography, insurance gaps, ignorance of gender issues, and so forth), the government subsidization of gambling, and much more. Similar issues came up during a keynote by David Torchiana, President & CEO and Partners HealthCare.

In her keynote, Jo Ann Jenkins, the CEO of AARP, quoted Atul Gawande as saying that we have medicalized aging and are failing to support the elderly. We have to see them as functioning individuals and help to support their health instead of focusing on when things go wrong. This includes focusing on prevention and ensuring that they have access to professional health care while they are still well. It also means restructuring our living spaces and lifestyles so the elderly can remain safely in their homes, get regular exercise, and eat well.

These problems call for a massive legislative and regulatory effort. But as a participant said on the panel of disruptive women in health care, plenty of money goes into promoting the interests of large hospitals, insurers, and device manufacturers, but nobody knows how to actually lobby for health care. Look at the barriers reached by Michelle Obama’s Let’s Move campaign, which fell short of ambitious goals in improving American’s nutrition.

Grounding devices on a firm foundation
A repeated theme at this symposium was making data collection by patients easier–so easy in fact that they can just launch data collection and not think about it. To be sure, some people are comfortable with health technology: according to Costantini, 60 percent of US smartphone users manage their health in some way through those devices. Nevertheless, if people have to consciously choose when to send data–even a click of a button–many will drop out of the program.

At a break-out session during the 2015 Health Datapalooza, I heard prospective device makers express anxiety over the gargantuan task of getting their products accepted by the industry. The gold standard for health care adoption, of course, is FDA approval based on rigorous clinical trials. One participant in the Datapalooza workshop assured the others that he had gotten his device through the FDA process, and that they could to.

Attitudes seem to have shifted over the past year, and many more manufacturers are treating FDA approval as a natural step in their development process, keeping their eyes on the prize of clinical adoption. Keith Carlton, CEO of HUINNO, in a panel on wearables, said that accuracy is critical to stand out in the marketplace and to counter the confusion caused by manufacturers that substitute hype for good performance.

Clinical trials for devices don’t have to be the billion-dollar, drawn-out ordeals suffered by pharma companies. Devices are rarely responsible for side effects (except for implantables) and therefore can be approved after a few months of testing.

A representative of BewellConnect told me that their road to approval took 9-12 months, and involved comparing the results of their devices to those of robust medical devices that had been previously approved. Typical BewellConnect devices include blood pressure cuffs and an infrared thermometer that quickly shows the patient’s temperature after being held near his temple. This thermometer has been used around the world in situations where it’s important to avoid contact with patients, such as in Ebola-plagued regions.

What’s new over the past three years is Bluetooth-enabled devices that can transmit their results over the network. BewellConnect includes this networking capability in 17 current devices. The company tries to provide a supremely easy path for the patient to transmit the device over a phone app to the cloud. The patient can register multiple family members on the app, and is prompted twice to indicate who was using the device so as to prevent errors. BewellConnect is working on an alert system for providers, a simple use case for data collection.

Many products from BewellConnect are in widespread use in France, where the company is based, and they have launched a major entry into the US market. I asked BewellConnect’s CEO, Olivier Hua, whether the US market presents greater problems than France. He said that the two markets are more similar than we think.

Health care in the US has historically been fragmented, whereas in France it was unified under government control. But the Affordable Care Act in the US has brought more regulation to the market here, whereas private health care providers (combining insurance and treatment) have been growing in France. As of January 1 of this year, France has required all employers to include a private option in their health care offerings. For the first time, French individuals are being hit with the copays and deductibles familiar to Americans, and are weighing how often to go to the doctor. Although the US market is still more diverse, and burdened by continuing fee-for-service plans, it is comparable to the French market for a vendor such as BewellConnect.

The next section of this article will continue with a discussion of barriers in the use of patient data, and other insights from the Connected Health symposium.

A New Meaning for Connected Health at 2016 Symposium (Part 1 of 4)

Posted on November 3, 2016 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.

Those of us engaged in health care think constantly about health. But at the Connected Health symposium, one is reminded that the vast majority of people don’t think much about health at all. They’re thinking about child care, about jobs, about bills, about leisure time. Health comes into the picture only through its impacts on those things.

Certainly, some people who have suffered catastrophic traumas–severe accidents, cancer, or the plethora of unfortunate genetic conditions–become obsessed about health to the same extent as health professionals. These people become e-patients and do all the things they need to do regain the precious state of being they enjoyed before their illness, often clashing with the traditional medical establishment in pursuit of health.

But for most people with chronic conditions, the opposite holds true. A whimsical posting points out that we willingly pay more to go to a masseur or hairdresser than to a doctor. I appreciate this observation more than the remedies offered by the author, which fall into the usual “patient engagment” activities that I have denigrated in an earlier article.

Understanding health as a facet and determinant of everyday life becomes even more important as we try to reverse the rise of costs, which in many nations are threatening economic progress and even the social contract. (Witness the popular anger in the current US election over rising insurance premiums and restrictions on choice.) We have to provide health solutions to people who are currently asymptomatic. The conventional focus on diagnosed conditions won’t serve us.

It’s thus commendable that the Connected Health symposium for 2016 has evolved to the point where participants can think not only of reaching out to patients, but to embedding their interventions so deeply into patient life that the patient no longer has to think about her health to benefit. This gives a new meaning to the word “connected”. Whereas, up to now, it referred to connecting a patient more closely with their clinicians and care-takers (through data collection, messaging, and online consultations), “connected” can also mean connecting our healthful interventions to the patient’s quotidian concerns about work, family, and leisure.

We can do this by such means as choosing data collection that the patient can enable and then stop thinking about, and integrating care with the social media they use regularly. In her keynote, Nancy Brown, CEO of the American Heart Association, pointed out that social connections are critical to health and are increasingly taking place online, instead of someone dropping by her neighbor for coffee. The AHA’s Go Red For Women program successfully exploited social connections to improve heart health.

If you want an overview of what people mean by the term “connected health,” you would do well to get The Internet of Healthy Things, by Dr. Joseph Kvedar, leader of Partners Connected Health and chief organizer of this symposium. For a shorter overview, you can read my review of the book, and my report from an earlier symposium. Now in its 13th year, the annual symposium signed up 1200 registered attendees–the biggest number yet. This article looks over the people and companies I heard from there.

Exhausting the possibilities of passive data collection
Glen Tullman, CEO of Livongo Health, offered basic principles for consumer health in a keynote: it must be personal, simple, context-aware, and actionable. As an example, he cited Livongo’s own program for sending text messages to diabetes patients: they are tailored to the individual and offer actionable advice such as, “Drink a glass of water”.

A panel on consumer technology extolled the value of what analysts like to call data exhaust: the use of data that can be collected from people’s everyday behavior. After all, this exhaust is what marketers used all the time to figure out what we want to buy, and what governments use to decide whether we’re dangerous actors. It can have value in health too.

As pointed out by Jim Harper, Co-Founder and COO of Sonde Health, providers and researchers can learn a lot from everyday interactions with devices–diagnosing activity levels from accelerometers, for instance, or depression from a drop in calls or text messages. Similarly, a symposium attendee suggested to me that colleges could examine social connections among students to determine which ones are at risk of abusing alcohol.

Lauren Costantini, President and CEO of Prima-Temp, said in a keynote that we can predict all kinds of things from your circadian rhythm–as measured by a sensor–such as an oncoming infection, or the best way to deliver chemotherapy.

Spire offers a device that claims to help people suffering from anxiety, with a low barrier to adoption and instant feedback. It’s a device worn on the body that can alert the user in various ways (buzzes, text messages) when the user’s anxiety level is rising.

Does the Spire device work? They got a partial answer to this in a study by Partners Health Care, where people had an option of using the device on its own or in conjunction with a headband from Muse that helps train people to meditate. (There was no control group.) Unlike the Spire device, which one can put on and forget about, the Muse purchaser is expected to make a conscious decision to meditate using the device regularly.

The Partners study showed modest benefits to these devices, but had mixed results. For instance, fewer than half the subjects continued use of the devices after the study finished. Those who did continue showed a strong positive effect on stress, and those who discontinued use showed a very small positive effect. Strangely there was a small overall increase in tension for all participants, even though they also demonstrated increases in “calm” periods. There is no correlation between the length of time that individuals used their devices and their outcomes.

Jonathan Palley, CEO & Co-founder of Spire, said participants often liked their devices, but stopped using them because they have learned from the devices how to identify stress and felt they could self-regulate and no longer needed the devices. I believe this finding may apply to other consumer devices as well. The huge rate at which devices are abandoned after six months, the subject of frequent reports and agonized commentaries, may simply indicate that users have reached their goal and can continue their fitness programs on their own. Graeme Moffat, VP of Scientific & Regulatory Affairs at Muse, reported that many purchasers use their headband for only three months, but come back to it over time to refresh their training.

We’ll look at some more aspects of integrating devices into patient lives in the next section of this article.

Can Machine Learning Tame Healthcare’s Big Data?

Posted on September 20, 2016 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.

Big data is both a blessing and a curse. The blessing is that if we use it well, it will tell us important things we don’t know about patient care processes, clinical improvement, outcomes and more. The curse is that if we don’t use it, we’ve got a very expensive and labor-hungry boondoggle on our hands.

But there may be hope for progress. One article I read today suggests that another technology may hold the key to unlocking these blessings — that machine learning may be the tool which lets us harvest the big data fields. The piece, whose writer, oddly enough, was cited only as “Mauricio,” lead cloud expert at Cloudwards.net, argues that machine learning is “the most effective way to excavate buried patterns in the chunks of unstructured data.” While I am an HIT observer rather than techie, what limited tech knowledge I possess suggests that machine learning is going to play an important role in the future of taming big data in healthcare.

In the piece, Mauricio notes that big data is characterized by the high volume of data, including both structured and non-structured data, the high velocity of data flowing into databases every working second, the variety of data, which can range from texts and email to audio to financial transactions, complexity of data coming from multiple incompatible sources and variability of data flow rates.

Though his is a general analysis, I’m sure we can agree that healthcare big data specifically matches his description. I don’t know if you who are reading this include wild cards like social media content or video in their big data repositories, but even if you don’t, you may well in the future.

Anyway, for the purposes of this discussion, let’s summarize by saying that in this context, big data isn’t just made of giant repositories of relatively normalized data, it’s a whirlwind of structured and unstructured data in a huge number of formats, flooding into databases in spurts, trickles and floods around the clock.

To Mauricio, an obvious choice for extracting value from this chaos is machine learning, which he defines as a data analysis method that automates extrapolated model-building algorithms. In machine learning models, systems adapt independently without any human interaction, using automatically-applied customized algorithms and mathematical calculations to big data. “Machine learning offers a deeper insight into collected data and allows the computers to find hidden patterns which human analysts are bound to miss,” he writes.

According to the author, there are already machine learning models in place which help predict the appearance of genetically-influenced diseases such as diabetes and heart disease. Other possibilities for machine learning in healthcare – which he doesn’t mention but are referenced elsewhere – include getting a handle on population health. After all, an iterative learning technology could be a great choice for making predictions about population trends. You can probably think of several other possibilities.

Now, like many other industries, healthcare suffers from a data silo problem, and we’ll have to address that issue before we create the kind of multi-source, multi-format data pool that Mauricio envisions. Leveraging big data effectively will also require people to cooperate across departmental and even organizational boundaries, as John Lynn noted in a post from last year.

Even so, it’s good to identify tools and models that can help get the technical work done, and machine learning seems promising. Have any of you experimented with it?

How Precision Medicine Can Save More Lives and Waste Less Money (Part 2 of 2)

Posted on August 10, 2016 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 section of this article looked at how little help we get from genetic testing. Admittedly, when treatments have been associated with genetic factors, testing has often been the difference between life and death. Sometimes doctors can hone in with laser accuracy on a treatment that works for someone because a genetic test shows that he or she will respond to that treatment. Hopefully, the number of treatments that we can associate with tests will grow over time.

So genetics holds promise, but behavioral and environmental data are what we can use right now. One sees stories in the trade press all the time such as these:

These studies usually depend on straightforward combinations of data that are easy to get, either from the health care system (clinical or billing data) or from the patient (reports of medication adherence, pain level, etc.).

And we’ve only scratched the surface of the data available to us. Fitness devices, sensors in our neighborhoods, and other input will give us much more. We can also find new applications for data: for instance, to determine whether one institution is overprescribing certain high-cost drugs, or whether an asthma victim is using an inhaler too often, meaning the medication isn’t strong enough. We know that social factors, notably poverty (LGBTQ status is not mentioned in the article, but is another a huge contributor to negative health outcomes, due to discrimination and clinician ignorance) must be incorporated into models for diagnosis, prediction, and care.

President Obama promises that Precision Medicine features both genetics and personal information. One million volunteers are sought for DNA samples and information on age, race, income, education, sexual orientation, and gender identity.

There are other issues that critics have brought up with the Precision Medicine initiative. For instance, its focus on cure instead of prevention weakens its value for long-term public health improvements. We must also remember the large chasm between knowing what’s good for you and doing it. People don’t change notoriously unhealthy behaviors, such as smoking, even when told they are at increased risk. Some experts think people shouldn’t be told their DNA results.

Meanwhile, those genetic database can be used against you. But let’s consider our context, once again, in order to assess the situation responsibly. The data is being mined by police, but it’s probably not very useful because the DNA segments collected are different from what the police are looking for. Behavioral data, if abused, is probably more damning than genetic data.

Just as there are powerful economic forces biasing us toward genetics, social and political considerations weigh against behavioral and environmental data. We all know the weaknesses in the government’s dietary guidelines, heavily skewed by the food industry. And the water disaster in Flint, Michigan showed how cowardice and resistance by the guardians of public health to admitting changes raised the costs in public health measures. Industry lobbying and bureaucratic inertia work together to undermine the simplest and most effective ways of improving health. But let’s get behavioral and environmental measures on the right track before splurging on genetic testing.

How Precision Medicine Can Save More Lives and Waste Less Money (Part 1 of 2)

Posted on August 9, 2016 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.

We all have by now seen the hype around the Obama Administration’s high-profile Precision Medicine Initiative and the related Cancer Moonshot, both of which plan to cull behavioral and genomic data on huge numbers of people in a secure manner for health research. Major companies have rushed to take advantage of the funds and spotlight what these initiatives offer. I think they’re a good idea so long as they focus on behavioral and environmental factors. (Scandalously, the Moonshot avoids environmental factors, which are probably the strongest contributors to cancer) . What I see is an unadvised over-emphasis on the genetic aspect of health analytics. This can be seen in announcements health IT vendors, incubators, and the trade press.

I can see why the big analytics firms are excited about increasing the health care field’s reliance on genomics: that’s where the big bucks are. Sequencing (especially full sequencing) is still expensive, despite dramatic cost reductions over the past decade. And after sequencing, analysis requires highly specialized expertise that relatively few firms possess. I wouldn’t say that genomics is the F-35 of health care, but is definitely an expensive path to our ultimate goals: reducing the incidence of disease and improving life quality.

Genomics offer incredible promise, but we’re still waiting to see just how it will help us. The problems that testing turns up, such as Huntington’s, usually lack solutions. One study states, “Despite the success of genome-wide association and whole-exome and whole-genome sequencing (WES/WGS) studies in revealing the DNA variants that underlie the genetic basis of disease, the development of effective treatments for most diseases has remained a challenge.” Another says, “Despite much progress in defining the genetic basis of asthma and atopy [predisposition to getting asthma] in the last decade, further research is required.”

When we think about the value of knowing a gene or a genetic deviation, we are asking: “How much does this help predict the likelihood that I’ll get the disease, or that a particular treatment will work on me?” The most impressive “yes” is probably in this regard to the famous BRCA1 and BRCA2 genes. If you are unlucky enough to have certain mutations of these gene, you have a 70% lifetime risk for developing breast or ovarian cancer. This is why testing for the gene is so popular (as well as contentious from an intellectual property standpoint), and why so may women act on the results.

However–this is my key point–only a small percentage of women who get these cancers have these genetic mutations. Most are not helped by testing for the genes, and a negative result on such a test gives them only a slight extra feeling of relief that they might not get cancer. Still, because the incidence of cancer is so high among the unfortunate women with the mutations, testing is worthwhile. Most of the time, though, testing is not worth much, because the genetic component of the disease is small in relation to lifestyle choices, environmental factors, or other things we might know nothing about.

So, although it’s hard enough already to say with any assurance that a particular gene or combination of genes is associated with a disease, it’s even harder to say that testing will make a big difference. Maybe, as with breast or ovarian cancer, a lot of people will get the disease for reasons unrelated to the gene.

In short, several factors go into determining the value of testing: how often a positive test guarantees a result, how often a negative test guarantees a result, how common the disease is, and more. Is there some way to wrap all these factors up into a single number? Yes, there is: it’s called the odds ratio. The higher an odds ratio, the more helpful (using all the criteria I mentioned) an association is between gene and disease, or gene and treatment. For instance, one study found that certain genes have a significant association with asthma. But the odds ratios were modest: 3.203 and 5.328. One would want something an order of magnitude higher to show running a test for the genes would have a really strong value.

This reality check can explain why doctors don’t tend to recommend genetic testing. Many sense that the tests can’t help or aren’t good at predicting most things.

The next section of this article will turn to behavioral and environmental factors.