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Vendor Study Says Wearables Can Promote Healthy Behavior Change

Posted on November 28, 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.

A new study backed by a company that makes an enterprise health benefits platform has concluded that wearables can encourage healthy behavior change, and also, serve as an effective tool to engage employees in their health.

The data from the study, which was sponsored by Mountain View, CA-based Jiff, comes from a two-year research project on employer-sponsored wearables. Rajiv Leventhal, who wrote about the study for Healthcare Informatics, argues that these findings challenge common employer beliefs about these type of programs, including that participation is typically limited to young and healthy employees, and that engagement with these rules can’t be sustained over time.

The data, which was drawn from 14 large employers with 240,000 employees, apparently suggests that wearable adoption and long-term engagement is possible for employees of all ages. The company reported that among the employers offered the wearables program via its enterprise health platform, 53% of employees under 40 years old participated, and 36% of employees over 50 years participated as well.

Jiff researchers also found that employee engagement had not measurably fallen for more than nine months following the program rollout, and that for one employer, levels of engagement have been progressively increasing for more than 18 months, the company reported.

According to Jiff, they have helped sustain employee engagement by employing three tactics:  Using “challenges,” time-bound immersive and social games that encourage healthy actions, “device credits,” subsidies that offset the cost of purchasing wearables and “behavioral incentives,” rewards for taking healthy actions such as walking a minimum number of steps per day.

The thing is, as interesting as these numbers might be — and they do, if nothing else, underscore the role of engaging consumers rather than waiting for them to engage with healthier behaviors on their own — the story doesn’t address one absolutely crucial issue, to wit, what concrete health impact are companies seeing from employee use of these devices.

I don’t think I’m asking for too much here when I demand some quantitative data suggesting that the setup can actually achieve measurable health results. Everything I’ve read about employee wellness initiatives to date suggests that they’ve been a giant bust, with few if any accomplishing anything measurable.

And here we have Jiff, a venture-backed hotshot company, which I’m guessing had the resources to report on results if it found any. After all, if I understand the study right, with their researchers had access to 540,000 employees for significant amount of time.  So where are the health conclusions that can be drawn from this population?

And by the way, no, I don’t accept that patient engagement (no matter how genuine) can be used as a proxy or predictive factor for health improvement. It’s a promising step in the right direction but it isn’t the real thing yet.

So, I shared the study with you because I thought you might find it interesting. I did. But I wouldn’t take it too seriously when it comes to signs of real change — either for wearables used for employee wellness initiatives. At this point both are more smoke than substance.

What Would A Community Care Plan Look Like?

Posted on November 16, 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.

Recently, I wrote an article about the benefits of a longitudinal patient record and community care plan to patient care. I picked up the idea from a piece by an Orion Health exec touting the benefits of these models. Interestingly, I couldn’t find a specific definition for a community care plan in the article — nor could I dig anything up after doing a Google search — but I think the idea is worth exploring nonetheless.

Presumably, if we had a community care plan in place for each patient, it would have interlocking patient-specific and population health-level elements to it. (To my knowledge, current population health models don’t do this.) Rather than simply handing patients off from one provider to another, in the hope that the rare patient-centered medical home could manage their care effectively on its own, it might set care goals for each patient as part of the larger community strategy.

With such a community care strategy, groups of providers would have a better idea where to allocate resources. It would simultaneously meet the goals of traditional medical referral patterns, in which clinicians consult with one another on strategy, and help them decide who to hire (such as a nurse-practitioner to serve patient clusters with higher levels of need).

As I envision it, a community care plan would raise the stakes for everyone involved in the care process. Right now, for example, if a primary care doctor refers a patient to a podiatrist, on a practical level the issue of whether the patient can walk pain-free is not the PCP’s problem. But in a community-based care plan, which help all of the individual actors be accountable, that podiatrist couldn’t just examine the patient, do whatever they did and punt. They might even be held to quantitative goals, if the they were appropriate to the situation.

I also envision a community care plan as involving a higher level of direct collaboration between providers. Sure, providers and specialists coordinate care across the community, minimally, but they rarely talk to each other, and unless they work for the same practice or health system virtually never collaborate beyond sharing care documentation. And to be fair, why should they? As the system exists today, they have little practical or even clinical incentive to get in the weeds with complex individual patients and look at their future. But if they had the right kind of community care plan in place for the population, this would become more necessary.

Of course, I’ve left the trickiest part of this for last. This system I’ve outlined, basically a slight twist on existing population health models, won’t work unless we develop new methods for sharing data collaboratively — and for reasons I be glad to go into elsewhere, I’m not bullish about anything I’ve seen. But as our understanding of what we need to get done evolves, perhaps the technology will follow. A girl can hope.

HealthTap Announces a Comprehensive Health App Platform

Posted on November 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.

For the past five years, HealthTap has been building a network of doctors and patients who exchange information and advice through information forums, messaging, video teleconferencing, and other integrated services. According to CEO Ron Gutman, all that platform building has taught them a lot about what health app developers need–knowledge that they’ve expanded by listening to hospitals and third-party app developers over the years. On Tuesday, November 1, HealthTap announced a comprehensive cloud platform pulling together all these ideas. The features in the press release read like a wish list from health app developers:

  • Text, voice, and video messaging

  • Telemedicine

  • Population health

  • Predictive modeling

  • Device input and other patient-generated data

  • Handling clinical data from electronic health records

  • Aggregated data on patient groups, such as the frequency of concepts in the population

  • The ability to view timelines on patients

  • Searchable content from the huge library of clinical advice posted to HealthTap by its roster of more than 100,000 doctors

  • Identity management, so that patients and clinicians can verify who they are and connect securely

  • Customer relationship management through messaging

Many of the APIs covering these topics are covered in the developer documentation, and others are available by application from qualified developers.

Gutman told me that three to four years of work went into this platform, and that he hopes it can reduce the multi-year developments efforts his team had to deal with to just weeks for other developers hoping to innovate in the health care field. Transparency is promoted as a key value, because the developer terms required developers to “Clearly inform users what data you collect (with their consent) as well as and how you use the data you collect or that we (HealthTap) provides to you.” Even so, some items are restricted even more, such as adherence data and health goals.

In addition to RESTful APIs, the platform has SDKs for iOS, Android, and JavasScript. CTO Sastry Nanduri says that these SDKs permit apps to incorporate some workflows, such as making virtual appointments. His philosophy is that, “We do the work and make it easy for the developers.”

HealthTap has created its own formats and APIs instead of using existing standards such as the Open mHealth defined for medical devices (described in another article). A diversity of formats may make adoption harder. But the platform does harmonize diverse data from different sources into predictable formats, so that things such as blood glucose and body weight are shown in fixed units. Nanduri points out that most of their work has not been done by other organizations in an open, API format.

In any case, central to HealthTap’s goals and efforts is the sharing of data among organizations. If Partners Healthcare or Kaiser Permanente can open their data through HealthTap’s APIs, it can all be combined with the aggregated data from millions of records HealthTap has built up over time.

Offering this platform in HealthTap’s cloud gives it many advantages. Foremost is the enormous data repository of both patients and content served up by the platform. Second, identity management is automatically provided through the secure and robust platform HealthTap has always used for signing up patients and clinicians. Clinicians are carefully validated. Theoretically, a developer could also use an independent means of authenticating patients, so that someone can use apps built on the platform without a HealthTap account.

They are also exploring a blockchain solution for tracking permissions and contracts.

The proof of this huge undertaking will be in its adoption. I’m sure HealthTap’s partners and many other organizations will play with the platform and try to bring apps to life through it, either for internal use or for widespread distribution. Nanduri says that they are ramping up carefully, reviewing applications one by one, and will talk to each of their early developers to find out their goals and offer guidance to creating a successful app. Time will tell whether HealthTap has, as Gutman says, created the platform their developers wish they had when they started the company.

Health Plans Need Big Data Smarts To Prove Their Value

Posted on November 2, 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.

Recently, Aetna cut a deal which suggests a new role for health insurers in big data analytics and population health management. In partnership with Merck, the health insurer is launching a new program using predictive analytics to identify target populations and provide them with health and wellness services. AetnaCare will start by targeting patients with diabetes and hypertension in the mid-Atlantic U.S., but it seems likely to go national soon.

In its press release on the matter, Aetna says the goal of the program is to “proactively curate various health and wellness services… to support treatment adherence, ensure that critical social support needs are met, and reinforce healthy lifestyle behaviors.” That in and of itself isn’t a big deal. We all know that these are goals shared by providers, employers and health plans, and that most of the efforts health plans make on this front are pie in the sky, half-baked initiatives featuring cutesy graphics and little substance.

But then, Aetna’s chief medical officer gives away the real goal here — to power this effort by analyzing patient data being spun out by patients in varied care settings.  In the release, Dr. Harold Paz notes that patients are getting care in a wide variety of settings, including retail clinics, healthcare devices, pharmaceutical services, behavioral health, and social services, and that these services are seldom coordinated well, and implies that this is the real problem Aetna must solve.

If you listen to this with the ears of a health IT chick like myself, you hear Aetna (and Merck, actually) admitting that they must engage in predictive analytics across all of these encounters – and eventually, use these insights to help patients make good healthcare choices. In other words, they have to think like providers and even offer provider-like services fulfill their mission. And that means competing with or even beating providers at the big data game.

The truth is, health plans are in the same boat as providers, in that they’re at the center of a hailstorm of data and struggling with how to make use of it. Also, like providers they’re facing pressure from health purchasers to slow healthcare cost growth and boost patient wellness. But I’d argue that they’re even less prepared, technically and culturally, to improve health or coordinate care. So jumping in now is critically important.

In fact, I’d argue that health insurers are under greater pressure to improve population health than even sophisticated health systems or ACOs. Why? Because while health systems and ACOs can point to what they do – they make people better, for heaven’s sake — insurance companies are the eternal middleman who must continue to prove that they add value to the healthcare equation.

It remains to be seen whether programs like AetnaCare succeed at helping patients find the resources they need to improve and maintain their health. But even if this concept doesn’t work out, others will follow. Health plans need to leverage their unique data set to boost quality and reduce costs. Otherwise, as providers learn to work under value-based payments and accept risk, employers will have increasingly good reasons to contract directly — and leave the insurance industry out of the game entirely.

E-Patient Update: The Patient Data Engagement Leader

Posted on October 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.

As healthcare delivery models shift responsibility for patient health to the patients themselves, it’s becoming more important to give them tools to help them get and stay healthy. Increasingly, digital health tools are filling the bill.

For example, portals are moving from largely billing and scheduling apps to exchanging of patient data, holding two-way conversations between patient and doctor and even tracking key indicators like blood glucose levels. Wearables are slowly becoming capable of helping doctors improve diagnoses, and patterns revealed by big data should soon be used to create personalized treatment plants.

The ultimate goal of all this, of course, is to push as much data power as possible into the hands of consumers. After all, for patients to be engaged with their health, it helps to make them feel in control, and the more sophisticated information they get, the better choices they can make. Or at least that’s how the traditional script reads.

Now, as an e-patient, the above is certainly true for me. Every incremental improvement in the data I get me brings me closer to taking on otherwise overwhelming health challenges. That’s true, in part, because I’m comfortable reading charts, extrapolating conclusions from data points and visualizing ways to make use of the information. But if you want less tech-friendly patients to get on board, they’re going to need help.

The patient engagement leader

And where will that help come from? I’d argue that hospitals and clinics need to create a new position dedicated to helping engage patients, including though not limited to helping them make their health data their own. This position would cut across several disciplines, ranging from patient health education clinical medicine to data analytics.

The person owning this position would need to be current in patient engagement goals across the population and by disease/condition type, understand the preferred usage patterns established by the hospital, ACO, delivery network or clinic and understand trends in health behavior well enough to help steer patients in the right direction.

It also wouldn’t hurt if such a person had a healthy dose of marketing skills under their belt, as part of the patient engagement process is simply selling consumers on the idea that they can and should take more responsibility for their health outcomes. Speaking from personal experience, a good marketer can wheedle, nudge and empower people by turns, and this will be very necessary to boost your engagement.

While this could be a middle management position, it would at least need to have the full support of the C-suite. After all, you can’t promote population-wide improvements in health by nibbling around the edges of the problem. Such measures need to be comprehensive and strategic to the mission of the healthcare organization as a whole, and the person behind the needs to have the authority to see them through.

Patients in control

If things go right, establishing this position would lead to the creation of a better-educated, more-confident patient population with a greater sense of self efficacy regarding their health. While specific goals would vary from one healthcare organization to the other, such an initiative would ideally lead to improvements in key metrics such as A1c levels population-wide, drops in hospital admission and readmission rates and simultaneously, lower spending on more intense modes of care.

Not only that, you could very well see patient satisfaction increase as well. After all, patients may not feel capable of making important health changes on their own, and if you help them do that it stands to reason that they’ll appreciate it.

Ultimately, engaging patients with their health calls for participation by everyone who touches the patient, from techs to the physician, nurses to the billing department. But if you put a patient engagement officer in place, it’s more likely that these efforts will have a focus.

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?

Engaging Patients With Health Data Cuts Louisiana ED Overuse

Posted on September 15, 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.

Maybe I’m misreading things, but it seems to me that few health IT pros really believe we can get patients to leverage their own health data successfully. And I understand why. After all, we don’t even have clear evidence that patient portals improve outcomes, and portals are probably the most successful engagement tool the industry has come up with to date.

And not to be a jerk about it, but I bet you’d be hard-pressed to find HIT gurus who believed the state of Louisiana would lead the way, as the achingly poor southern state isn’t exactly known for being a healthcare thought leader.  As it so happens, though, the state has actually succeeded where highfalutin’ health systems have failed.

Over one year, the state has managed to generate a 23% increase in health IT use among at-risk patients, and also, a 10.2% decrease in non-emergent use of emergency departments by Medicaid managed care organization members, thank you very much.

So how did Louisiana’s top healthcare brass accomplish this feat? Among other things, they launched a HIE-enabled ED data registry, along with a direct-to-consumer patient engagement campaign. These efforts were done in partnership with the Louisiana Health Care Quality Forum, which developed statewide marketing plans for the effort (See John’s interview with the Louisiana Health Care Quality Forum for more details).

They must have created some snazzy marketing copy. As Healthcare IT News noted, between August 2015 and May 2016, patient portal use shot up 31%, consumer EHR awareness rose 23% and opt-in to the state’s HIE grew by 3%, Quality Forum marketer Jamie Martin told HIN.

Not only that, the number of patients asking for access to or copies of electronic health data increased by 12%, and the number of patients with current copies of their health information grew by 9%, Martin said.

This is great news for those who want to see patients buy in to the digital health paradigm. Though it’s hard to tell whether the state will be able to maintain the benefits it gained in its initial effort, it clearly succeeded in getting a substantial number of patients to rethink how they manage their care.

But (and I’m sorry to be a bit of a Debbie Downer), I was a bit disappointed when I saw none of the gains cited related to changing health behaviors, such as, say, an increase in diabetics getting retinal exams.

I know that I should probably be focused on the project’s commendable successes, and believe it or not, I do find them to be exciting. I’m just not sure that these kinds of metrics can be used as proxies for health improvement measures, and let’s face it, that’s what we need, right?

OCHIN Shows That Messy Data Should Not Hold Back Health Care

Posted on September 12, 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 health care industry loves to complain about patient data. It’s full of errors, which can be equally the fault of patients or staff. And hanging over the whole system is lack of interoperability, which hampers research.

Well, it’s not as if the rest of the universe is a pristine source of well-formed statistics. Every field has to deal with messy data. And somehow retailers, financial managers, and even political campaign staff manage to extract useful information from the data soup. This doesn’t mean that predictions are infallible–after all, when I check a news site about the Mideast conflicts, why does the publisher think I’m interested in celebs from ten years ago whose bodies look awful now? But there is still no doubt that messy data can transform industry.

I’m all for standards and for more reliable means of collecting and vetting patient data. But for the foreseeable future, health care institutions are going to have to deal with suboptimal data. And OCHIN is one of the companies that shows how it can be done.

I recently had a chance to talk and see a demo of OCHIN’s analytical tool, Acuere, with CEO Abby Sears and the Vice President of Data Services and Integration, Clayton Gillett. Their basic offering is a no-nonsense interface that lets clinicians and administrator do predictions and hot-spotting.

Acuere is part of a trend in health care analytics that goes beyond clinical decision support and marshalls large amounts of data to help with planning (see an example screen in Figure 1). For instance, a doctor can rank her patients by the number of alerts the system generates (a patient with diabetes whose glucose is getting out of control, or a smoker who hasn’t received counseling for smoking cessation). An administrator can rank a doctor against others in the practice. This summary just gives a flavor of the many services Acuere can perform; my real thrust in this article is to talk about how OCHIN obtains and processes its data. Sears and Gillett talked about the following challenges and how they’re dealing with them.

Acuere Provider Report Card

Figure 1. Acuere Report Card in Acuere

Patient identification
Difficulties in identifying patients and matching their records has repeatedly surfaced as the biggest barrier to information exchange and use in the US health care system. A 2014 ONC report cites it as a major problem (on pages 13 and 20). An article I cited earlier also blames patient identification for many of the problems of health care analytics. But the American public and Congress have been hostile to unique identifiers for some time, so health care institutions just have to get by without them.

OCHIN handles patient matching as other institutions, such as Health Information Exchanges, do. They compare numerous fields of records–not just obvious identifiers such as name and social security number, but address, demographic information, and perhaps a dozen other things. Sears and Gillett said it’s also hard to knowing which patients to attribute to each health care provider.

Data sources
The recent Precision Medicine initiatives seeks to build “a national research cohort of one million or more U.S. participants.” But OCHIN already has a database on 7.6 million people and has signed more contracts to reach 10 million this Fall. Certainly, there will be advantages to the Precision Medicine database. First, it will contain genetic information, which OCHIN’s data suppliers don’t have. Second, all the information on each person will be integrated, whereas OCHIN has to take de-identified records from many different suppliers and try to integrate them using the techniques described in the previous section, plus check for differences and errors in order to produce clean data.

Nevertheless, OCHIN’s data is impressive, and it took a lot of effort to accumulate it. They get not only medical data but information about the patient’s behavior and environment. Along with 200 different vital signs, they can map the patient’s location to elements of the neighborhood, such as income levels and whether healthy food is sold in local stores.

They get Medicare data from qualified entities who were granted access to it by CMS, Medicaid data from the states, patient data from commercial payers, and even data on the uninsured (a population that is luckily shrinking) from providers who treat them. Each institution exports data in a different way.

How do they harmonize the data from these different sources? Sears and Gillett said it takes a lot of manual translation. Data is divided into seven areas, such as medications and lab results. OCHIN uses standards whenever possible and participates in groups that set standards. There are still labs that don’t use LOINC codes to report results, as well as pharmacies and doctors who don’t use RxNorm for medications. Even ICD-10 changes yearly, as codes come and go.

Data handling
OCHIN isn’t like a public health agency that may be happy sharing data 18 months after it’s collected (as I was told at a conference). OCHIN wants physicians and their institutions to have the latest data on patients, so they carry out millions of transactions each day to keep their database updated as soon as data comes in. Their analytics run multiple times every day, to provide the fast results that users get from queries.

They are also exploring the popular “big data” forms of analytics that are sweeping other industries: machine learning, using feedback to improve algorithms, and so on. Currently, the guidance they offer clinicians is based on traditional clinical recommendations from randomized trials. But they are seeking to expand those sources with other insights from light-weight methods of data analysis.

So data can be useful in health care. Modern analytics should be available to every clinician. After all, OCHIN has made it work. And they don’t even serve up ads for chronic indigestion or 24-hour asthma relief.

A Consulting Firm Attempts a Transition to Open Source Health Software (Part 2 of 2)

Posted on September 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 covered the history of HLN’s open source offerings. How can it benefit from this far-thinking practice to build a sustainable business?

The obvious place to turn for funding is the Centers for Disease Control, which lies behind many of the contracts signed by public health agencies. One way or another, a public health agency has to step up and pay for development. This practice is called custom-developed code in the open source policy memorandum of the federal Office of Management and Budget (p. 14 of the PDF).

The free rider problem is acute in health care. In particular, the problems faced by a now-defunct organization, Open Health Tools, were covered in another article of mine. I examined why the potential users of the software felt little inclination to pay for its development.

The best hope for sustaining HLN as an open source vendor is the customization model: when an agency needs a new feature or a customized clinical decision support rule, it contracts with HLN to develop it. Naturally, the agency could contract with anyone it wants to upgrade open source software, but HLN would be the first place to look because they are familiar with software they built originally.

Other popular models include offering support as a paid service, and building proprietary tools on top of the basic open source version (“open core”). The temptation to skim off the cream of the product and profit by it is so compelling that one of the most vocal stalwarts of the open source process, MariaDB (based on the popular MySQL database) recently broke radically from its tradition and announced a proprietary license for its primary distinguishing extension.

Support has never scaled as a business model; it’s very labor-intensive. Furthermore, it might have made sense to offer support decades ago when each piece of software posed unique integration problems. But if you create good, modern interfaces–as Arzt claims to do–you use standards that are familiar and require little guidance.

The “open core” model has also proven historically to be a weak business model. Those that use it may stay afloat, but they don’t grow the way popular open source software such as Linux or Python do. The usual explanation for this is that users don’t find the open part of the software useful enough on its own, and don’t want to contribute to it because they feel they are just helping a company build its proprietary business.

Wonks to the Rescue
It may be that Arzt–and others who want to emulate his model in health care–have to foster a policy change in governments. This is certainly starting to happen, as seen in a series of policy announcements by the US government regarding open source software. But this is a long road, and direction could easily be reversed or allowed to falter. We have already seen false starts to open source software in various Latin American governments–the decade of the 2000s saw many flowery promises these, but hardly any follow-through.

I don’t like to be cynical, but hope may lie in the crushing failures of proprietary vendors to produce usable and accurate software for health care settings. The EHR Incentive Programs under Meaningful Use poured about 28 billion dollars into moving clinicians onto electronic records, almost all of it spent on proprietary products (of course, there were also administration costs for things such as Regional Extension Centers), with little to show in quality improvements or data exchange. The government’s open source initiatives, CONNECT and Direct, got lost in the muddle of non-functional proprietary EHRs.

So the health care industry will have to try something radically new, and the institutions willing to be innovate have their fingers on the pulse of cutting-edge trends. This includes open source software. HLN may be able to ride a coming wave.

A Consulting Firm Attempts a Transition to Open Source Health Software (Part 1 of 2)

Posted on September 6, 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.

Open source is increasingly understood to be the future of software, because communities working together on shared needs can produce code that is at least as good as proprietary products, while representing user interests more effectively and interoperating without friction. But running an open source project is a complex task, and keeping a business going on it is absolutely perilous. In his 2001 book The Cathedral & the Bazaar, Eric S. Raymond listed half a dozen ways for businesses to profit on open source software, but today only one or two are visible in the field (and they differ from his list).

An Enduring Commitment
Noam H Arzt, president and founder of HLN Consulting, is trying to make the leap. After getting his PhD from the University of Pennsylvania and working there in various computer-related positions for 20 years, he got the health care bug–like many readers of this article–and decided to devote his career to software for public health. He first encountered the field while working on a public health project among the famous “hot spotters” of depressed Camden, New Jersey, and was inspired by the accomplishments of people in a bad area with minimal resources. Many of his company’s later projects come from the Department of Health and Mental Hygiene in New York City.

Founded in 1997, HLN Consulting has released code under an open source license for some time. It makes sense, because its clients have no reason to compete with anybody, because IT plays a crucial role in public health, and because the needs of different public health agencies overlap a great deal. Furthermore, they’re all strapped for funds. So Arzt tells me that the agency leadership is usually enthusiastic about making the software open source. It just may take a few months to persuade the agency’s lawyers, who are clueless about open source licenses, to put one in the contract.

A few agencies outside of HLN’s clients have picked up the software, though–particularly as the developers adopt modern software practices such as more modular systems and a service-oriented architecture using open, published APIs–but none have yet contributed anything back.

HLN did, however, rack up a recent win over the Immunization Calculation Engine (ICE), software that calculates and alerts clinicians about the vaccinations patients need. The software is normally used by immunization registries that serve states or large municipalities. But eClinicalWorks has also incorporated ICE into its EHR. And the Veterans Health Administration (VHA) chose ICE this year to integrate with its renowned VistA health record. HLN has invested a fair amount of its own time into preparing ICE for integration. Arzt estimates that since HLN developed ICE for a client, the company has invested at least five person-years in upgrading the software, and has received no money directly for doing so. HLN hopes to generate revenue from assisting organizations in configuring and using ICE and its clinical decision support rules, and a new support contract with VHA is the first big step.

Can You Get There From Here?
Arzt is trying now to build on the success of ICE and make a transition from a consulting firm to an open source software firm. A consulting firm typically creates products for a single customer, and has “fight and claw for every contract,” in Arzt’s words. Maintaining a steady stream of work in such firms is always challenging. In contrast, successful open source software is widely used, and the work put into the software by each contributor is repaid by all the contributions made by others. There is no doubt that HLN is developing products with broad applicability. It all makes economic sense–except that somebody actually has to foot the bill. We’ll look at possibilities in the next section of this article.