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Scenarios for Health Care Reform (Part 2 of 2)

Posted on May 18, 2017 I Written By

Andy Oram is an editor at O’Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space.

Andy also writes often for O’Reilly’s Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O’Reilly’s Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article suggested two scenarios that could promote health care reform. We’ll finish off the scenarios in this part of the article.

Capitalism Disrupts Health Care

In the third scenario, reform is stimulated by an intrepid data science firm that takes on health care with greater success than most of its predecessors. After assembling an impressive analytics toolkit from open source software components–thus simplifying licensing–it approaches health care providers and offers them a deal they can’t refuse: analytics demonstrated to save them money and support their growth, all delivered for free. The data science firm asks in return only that they let it use deidentified data from their patients and practices to build an enhanced service that it will offer paying customers.

Some health care providers balk at the requirement to share data, but their legal and marketing teams explain that they have been doing it for years already with companies whose motives are less commendable. Increasingly, the providers are won over. The analytics service appeals particularly to small, rural, and safety-net providers. Hammered by payment cuts and growing needs among their populations, they are on the edge of going out of business and grasp the service as their last chance to stay in the black.

Participating in the program requires the extraction of data from electronic health records, and some EHR vendors try to stand in the way in order to protect their own monopoly on the data. Some even point to clauses in their licenses that prohibit the sharing. But they get a rude message in return: so valuable are the analytics that the providers are ready to jettison the vendors in a minute. The vendors ultimately go along and even compete on the basis of their ability to connect to the analytics.

Once stability and survival are established, the providers can use the analytics for more and more sophisticated benefits. Unlike the inadequate quality measures currently in use, the analytics provide a robust framework for assessing risk, stratifying populations, and determining how much a provider should be rewarded for treating each patient. Fee-for-outcome becomes standard.

Providers make deals to sign up patients for long-term relationships. Unlike the weak Medicare ACO model, which punishes a provider for things their patients do outside their relationship, the emerging system requires a commitment from the patient to stick with a provider. However, if the patient can demonstrate that she was neglected or failed to receive standard of care, she can switch to another provider and even require the misbehaving provider to cover costs. To hold up their end of this deal, providers find it necessary to reveal their practices and prices. Physician organizations develop quality-measurement platforms such as the recent PRIME registry in family medicine. A race to the top ensues.

What If Nothing Changes?

I’ll finish this upbeat article with a fourth scenario in which we muddle along as we have for years.

The ONC and Centers for Medicare & Medicaid Services continue to swat at waste in the health care system by pushing accountable care. But their ratings penalize safety-net providers, and payments fail to correlate with costs as hoped.

Fee-for-outcome flounders, so health care costs continue to rise to intolerable levels. Already, in Massachusetts, the US state that leads in universal health coverage, 40% of the state budget goes to Medicaid, where likely federal cuts will make it impossible to keep up coverage. Many other states and countries are witnessing the same pattern of rising costs.

The same pressures ride like a tidal wave through the rest of the health care system. Private insurers continue to withdraw from markets or lose money by staying. So either explicitly or through complex and inscrutable regulatory changes, the government allows insurers to cut sick people from their rolls and raise the cost burdens on patients and their employers. As patient rolls shrink, more hospitals close. Political rancor grows as the public watches employer money go into their health insurance instead of wages, and more of their own stagnant incomes go to health care costs, and government budgets tied up in health care instead of education and other social benefits.

Chronic diseases creep through the population, mocking crippled efforts at public health. Rampant obesity among children leads to more and earlier diabetes. Dementia also rises as the population ages, and climate change scatters its effects across all demographics.

Furthermore, when patients realize the costs they must take on to ask for health care, they delay doctor visits until their symptoms are unbearable. More people become disabled or perish, with negative impacts that spread through the economy. Output decline and more families become trapped in poverty. Self-medication for pain and mental illness becomes more popular, with predictable impacts on the opiate addiction crisis. Even our security is affected: the military finds it hard to recruit find healthy soldiers, and our foreign policy depends increasingly on drone strikes that kill civilians and inflame negative attitudes toward the US.

I think that, after considering this scenario, most of us would prefer one of the previous three I laid out in this article. If health care continues to be a major political issue for the next election, experts should try to direct discussion away from the current unproductive rhetoric toward advocacy for solutions. Some who read this article will hopefully feel impelled to apply themselves to one of the positive scenarios and bring it to fruition.

Scenarios for Health Care Reform (Part 1 of 2)

Posted on May 16, 2017 I Written By

Andy Oram is an editor at O’Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space.

Andy also writes often for O’Reilly’s Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O’Reilly’s Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

All reformers in health care know what the field needs to do; I laid out four years ago the consensus about patient-supplied data, widespread analytics, mHealth, and transparency. Our frustration comes in when trying to crack the current hide-bound system open and create change. Recent interventions by US Republicans to repeal the Affordable Care Act, whatever their effects on costs and insurance coverage, offer no promise to affect workflows or treatment. So this article suggests three potential scenarios where reform could succeed, along with a vision of what will happen if none of them take hold.

Patients Forge Their Own Way Forward

In the first scenario, a tiny group of selfer-trackers, athletes, and empowered patients start a movement that ultimately wins over hundreds of millions of individuals.

These scattered enthusiasts, driven to overcome debilitating health problems or achieve extraordinary athletic feats, start to pursue self-tracking with fanaticism. Consumer or medical-grade devices provide them with ongoing data about their progress, and an open source platform such as HIE of One gives them a personal health record (PHR).

They also take charge of their interactions with the health care system. They find that most primary care providers aren’t interested in the data and concerns they bring, or don’t have time to process those data and concerns in the depth they need, or don’t know how to. Therefore, while preserving standard relationships with primary care providers and specialists where appropriate, the self-trackers seek out doctors and other providers to provide consultation about their personal health programs. A small number of providers recognize an opportunity here and set up practices around these consultations. The interactions look quite different from standard doctor visits. The customers, instead of just submitting themselves to examination and gathering advice, steer the conversation and set the goals.

Power relationships between doctors and customers also start to change. Although traditional patients can (and often do) walk away and effectively boycott a practice with which they’re not comfortable, the new customers use this power to set the agenda and to sort out the health care providers they find beneficial.

The turning point probably comes when someone–probabaly a research facility, because it puts customer needs above business models–invents a cheap, comfortable, and easy-to-use device that meets the basic needs for monitoring and transmitting vital signs. It may rest on the waist or some other place where it can be hidden, so that there is no stigma to wearing it constantly and no reason to reject its use on fashion grounds. A beneficent foundation invests several million dollars to make the device available to schoolchildren or some other needy population, and suddenly the community of empowered patients leaps from a miniscule pool to a mainstream phenomenon.

Researchers join the community in search of subjects for their experiments, and patients offer data to the researchers in the hope of speeding up cures. At all times, the data is under control of the subjects, who help to direct research based on their needs. Analytics start to turn up findings that inform clinical decision support.

I haven’t mentioned the collection of genetic information so far, because it requires more expensive processes, presents numerous privacy risks, and isn’t usually useful–normally it tells you that you have something like a 2% risk of getting a disease instead of the general population’s 1% risk. But where genetic testing is useful, it can definitely fit into this system.

Ultimately, the market for consultants that started out tiny becomes the dominant model for delivering health care. Specialists and hospitals are brought in only when their specific contributions are needed. The savings that result bring down insurance costs for everyone. And chronic disease goes way down as people get quick feedback on their lifestyle choices.

Government Puts Its Foot Down

After a decade of cajoling health care providers to share data and adopt a fee-for-outcome model, only to witness progress at a snail’s pace, the federal government decides to try a totally different tack in this second scenario. As part of the Precision Medicine initiative (which originally planned to sign up one million volunteers), and leveraging the ever-growing database of Medicare data, the Office of the National Coordinator sets up a consortium and runs analytics on top of its data to be shared with all legitimate researchers. The government also promises to share the benefits of the analytics with anyone in the world who adds their data to the database.

The goals of the analytics are multi-faceted, combining fraud checks, a search for cures, and everyday recommendations about improving interventions to save money and treat patients earlier in the disease cycle. The notorious 17-year gap between research findings and widespread implementation shrinks radically. Now, best practices are available to any patient who chooses to participate.

As with the personal health records in the previous scenario, the government database in this scenario creates a research platform of unprecedented size, both in the number of records and the variety of participating researchers.

To further expand the power of the analytics, the government demands exponentially greater transparency not just in medical settings but in all things that make us sick: the food we eat (reversing the rulings that protect manufacturers and restaurants from revealing what they’re putting in our bodies), the air and water that surrounds us, the effects of climate change (a major public health issue, spreading scourges such as mosquito-borne diseases and heat exhaustion), disparities in food and exercise options among neighborhoods, and more. Public awareness leads to improvements in health that lagged for decades.

In the next section of this article, I’ll present a third scenario that achieves reform from a different angle.

As Patient Engagement Advances, It Raises Questions About Usefulness

Posted on September 26, 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.

Reading ONC’s recent summary of patient engagement capabilities at US hospitals left me feeling both hopeful and wistful. The ONC, as usual, is trying to show off how much progress the field of health IT has made since Meaningful Use started, and the statistics in this dashboard meet those goals. On the other hand, I look at the statistics and wonder when real patient empowerment will emerge from these isolated gains.

The ONC dashboard includes information both on raw data exchange–what Meaningful Use called view, download, and transmit (VDT)–and the uses of that data, which ultimately mean much more than exchange.

I considered at first how important I would find it to download hospital information. I certainly would like my doctors to get the results of tests performed there, and other information related to my status upon discharge, but these supposedly are sent to the primary care physician in a Continuity of Care Document (CCD). If I or a close relative of mine had a difficult or chronic condition, I would certainly benefit from VDT because I would have to be an active advocate and would need the documentation. My point here is that our real goal in health reform is coordinated care, rather than data transfer, and while VDT is an important first step, we must always ask who is using that information.

The ONC did not ask the hospitals how much of their data patients can download. God is in the details, and I am not confident that an affirmative answer to the question of downloading data means patients can get everything that is in their records. For instance, my primary care physician has a patient portal running on eClinicalWorks (not his choice, but the choice of the hospital to which he is affiliated). From this portal I can get only a few pieces of information, such as medications (which I happen to know already, since I am taking them) and lab results. Furthermore, I downloaded the CCD and ran it through a checker provided online by the ONC for a lark, and found that it earned D grades for accurate format. This dismal rating suggests that I couldn’t successfully upload the CCD to another doctor’s EHR.

Still, I don’t want to dismiss the successes in the report. VDT is officially enabled in 7 out of 10 hospitals, a 7-fold growth between 2013 and 2015. Although the dashboard laments that “Critical Access, medium, and small hospitals lag,” the lag is not all that bad. And the dashboard also shows advances in the crucial uses of that data, such as submitting amendments to the data

A critical question in evaluating patient engagement is how the Congress and ONC define it. A summary of the new MACRA law lists several aspects of patient engagement measured under the new system:

  • Viewing, downloading, and transmitting, as defined before. As with the later Meaningful Use requirements, MACRO requires EHRs to offer an API, so that downloading can be done automatically.

  • Secure messaging. Many advances in treating chronic conditions depend on regular communications with patients, and messaging is currently the simplest means toward that goal. Some examples of these advances can be found in my article about a health app challenge. Conventional text messaging is all in plain text, and health care messaging must be secure to meet HIPAA requirements.

  • Educational materials. I discount the impact of static educational materials offered to patients with chronic conditions, whether in the form print brochures or online. But educational materials are part of a coordinated care plan.

  • Incorporating patient-generated data. The MACRA requirements “ask providers to incorporate data contributed by the patient from at least one unique patient.” Lucky little bugger. How will he or she leverage this unprecedented advantage?

That last question is really the nub of the patient engagement issue. In Meaningful Use and MACRA, regulators often require a single instance of some important capability, because they know that once the health care provider has gone through the trouble of setting up that capability, extending it to all patients is less difficult. And it’s heartening to see that 37 percent of hospitals allowed patients to submit patient-generated data in 2015.

Before you accept data from a patient, you need extra infrastructure to make the data useful. For instance:

  • You can check for warning signals that call for intervention, such as an elevated glucose level. This capability suggests a background program running through all the data that comes in and flagging such warning signals.

  • You can evaluate device data to see progress or backsliding in the patient’s treatment program. This requires analytics that understand the meaning of the data (and that can handle noise) so as to produce useful reports.

  • You can create a population health program that incorporates the patient-generated data into activities such as monitoring epidemics. This is also a big analytical capability.

Yes, I’m happy we’ve made progress in using data for patient engagement. A lot of other infrastructure also needs to be created so we can benefit from the big investment these advances required.

What Data Do You Need in Order to Guide Behavioral Change?

Posted on June 2, 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.

This is an exciting time for the health care field, as its aspirations toward value-based payments and behavioral responses to chronic conditions converge on a more and more precise solution. Dr. Joseph Kvedar has called this comprehensive approach connected health and has formed both a conference and a book around it. BaseHealth, a predictive analytics company in healthcare, has teamed up with TriVita to offer a consumer-based service around this approach, which combines access to peer-reviewed research with fine-tuned guidance that taps into personal health and behavioral data and leverages the individual interests of each participant.

I have previously written about BaseHealth’s assessment engine, which asks individuals for information about their activities, family history, and health conditions in order to evaluate their health profile and risk for common diseases. TriVita is a health coaching service with a wide-ranging assessment tool and a number of products, including cutely named supplements such as Joint Complex and Daily Cleanse. TriVita’s nutritionists, exercise coaches, and other staff are overseen by physicians, but their service is not medical: it does not enter the heavily regulated areas where clinicians practice.

I recently talked with BaseHealth’s CEO, Prakash Menon, and Dan Hoemke, its Vice President of Business Development. They describe BaseHealth’s predictive analytics as input that informs TriVita’s coaching service. What I found interesting is the sets of data that seem most useful for coaching and behavioral interventions.

In my earlier article, I wrote, “BaseHealth has trouble integrating EHR data.” Menon tells me that getting this data has become much easier over the past several months, because several companies have entered the market to gather and combine the data from different vendors. Still, BaseHealth focuses on a few sources of medical data, such as lab and biometric data. Overall, they focus on gathering data required to identify disease risk and guide behavior change, which in turn improves preventable conditions such as heart disease and diabetes.

Part of their choice springs from the philosophy driving BaseHealth’s model. Menon says, “BaseHealth wants to work with you before you have a chronic condition.” For instance, the American Diabetes Association estimated in 2012 that 86 million Americans over the age of 20 had prediabetes. Intervening before these people have developed the full condition is when behavioral change is easiest and most effective.

Certainly, BaseHealth wants to know your existing medical conditions. So they ask you about them when you sign up. Other vital signs, such as cholesterol, are also vital to BaseHealth’s analytics. Through a partnership with LabCo, a large diagnostics company in Europe, they are able to tap into lab systems to get these vital signs automatically. But users in the United States can enter them manually with little effort.

BaseHealth is not immune to the industry’s love affair with genetics and personalization, either. They take about 1500 genetic factors into account, helping them to quantify your risk of getting certain chronic conditions. But as a behavioral health service, Menon points out, BaseHealth is not designed to do much with genetic traits signifying a high chance of getting a disease. They deal with problems that you can do something about–preventable conditions. Menon cites a Health 2.0 presentation (see Figure 1) saying that our health can, on average, be attributed 60 percent to lifestyle, 30 percent to genetics, and 10 percent to clinical interventions. But genetics help to show what is achievable. Hoemke says BaseHealth likes to compare each person against the best she can be, whereas many sites just compare a user against the average population with similar health conditions.

Relative importance of health factors

Figure 1. Relative importance of health factors

BaseHealth gets most of its data from conditions known to you, your environment, family history, and more than 75 behavioral factors: your activity, food, over-the-counter meds, sleep activity, alcohol use, smoking, several measures of stress, etc. BaseHealth assessment recommendations and other insights are based on peer-reviewed research. BaseHealth will even point the individual to particular studies to provide the “why” for its recommendations.

So where does TriVita fit in? Hoemke says that BaseHealth has always stressed the importance of human intervention, refusing to fall into the fallacy that health can be achieved just through new technology. He also said that TriVita fits into the current trend of shifting accountability for health to the patient; he calls it a “health empowerment ecosystem.” As an example of the combined power of BaseHealth and TriVita, a patient can send his weight regularly to a coach, and both can view the implications of the changes in weight–such as changes in risk factors for various diseases–on charts. Some users make heavy use of the coaches, whereas others take the information and recommendations and feel they can follow their plan on their own.

As more and more companies enter connected health, we’ll get more data about what works. And even though BaseHealth and TriVita are confident they can achieve meaningful results with mostly patient-generated data, I believe that clinicians will use similar techniques to treat sicker people as well.