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OIG Says HHS Needs To Play Health IT Catch-Up

Posted on December 1, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A new analysis by the HHS Office of the Inspector General suggests that the agency still has work to do and appropriately managing health information technology and making sure it performs, according to Health Data Management. And unfortunately, the problems it highlights don’t seem likely to go away anytime soon.

The critique of HHS’s HIT capabilities came as part of an annual report from the OIG, in which the oversight body lists what it sees as the department’s top 10 management and performance issues. The OIG ranked HIT third on its list.

In that critique, auditors from the OIG pointed out that there are still major concerns over the future of health data sharing in the US, not just for HHS but also in the US healthcare system at large. Specifically, the OIG notes that while HHS has spent a great deal on health IT, it hasn’t gotten too far in enabling and supporting the flow of health data between various stakeholders.

In this analysis, the OIG sites several factors which auditors see as a challenge to HHS, including the lack of interoperability between health data sources, barriers imposed by federal and state privacy and security laws, the cost of health IT infrastructure and environmental issues such as information blocking by vendors. Of course, the problems it outlines are the same old pains in the patoot that we’ve been facing for several years, though it doesn’t hurt to point them out again.

In particular, the OIG’s report argues, it’s essential for HHS to improve the flow of up-to-date, accurate and complete electronic information between the agency and providers it serves. After all, it notes, having that data is important to processing Medicare and Medicaid payments, quality improvement efforts and even HHS’s internal program integrity and operations efforts. Given the importance of these activities, the report says, HHS leaders must find ways to better streamline and speed up internal data exchange as well as share that data with Medicare and Medicaid systems.

The OIG also critiqued HHS security and privacy efforts, particularly as the number of healthcare data breaches and potential cyber security threats like ransomware continue to expand. As things stand, HHS cybersecurity shortfalls abound, including inadequacies and access controls, patch management, encryption of data and website security vulnerabilities.  These vulnerabilities, it noted, include not only HHS, but also the states and other entities that do business with the agency, as well as healthcare providers.

Of course, the OIG is doing its job in drawing attention to these issues, which are stubborn and long-lasting. Unfortunately, hammering away at these issues over and over again isn’t likely to get us anywhere. I’m not sure the OIG should have wasted the pixels to remind us of challenges that seem intractable without offering some really nifty solutions, or at least new ideas.

Creating Healthcare Interoperability Bundles

Posted on October 25, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

At this point in the evolution of healthcare data, you’d think it would be easy to at least define interoperability, even if we can’t make it happen. But the truth is that despite the critical importance of the term, we still aren’t as clear as we should be on how to define it. In fact, the range of possible solutions that can be called “interoperable” is all over the map.

For example, a TechTarget site defines interoperability as “the ability of a system or a product to work with other systems or products without special effort on the part of the customer.” When defined down to its most basic elements, even passive methods of pushing data from one to another count is interoperability, even if that data doesn’t get used in clinical care.

Meanwhile, an analysis by research firm KLAS breaks interoperability down into four levels of usefulness, ranked from information being available, to providers having the ability to locate records, to the availability of clinical view to this data having an impact on patient care.

According to a recent survey by the firm, 20% of respondents had access to patient information, 13% could easily locate the data, 8% could access the data via a clinical view and just 6% had interoperable data in hand that could impact patient care.

Clearly, there’s a big gap between these two definitions, and that’s a problem. Why? Because the way we define baseline interoperability will have concrete consequences on how data is organized, transmitted and stored. So I’d argue that until we have a better idea of what true, full interoperability looks like, maybe we should map out interoperability “bundles” that suit a given clinical situation.

A Variety of Interoperabilities

For example, if you’re an ACO addressing population health issues, it would make sense to define a specific level of interoperability needed to support patient self-management and behavioral change. And that would include not only sharing between EMR databases, but also remote monitoring information and even fitness tracking data. After all, there is little value to trying to, say, address chronic health concerns without addressing some data collected outside of clinic or hospital.

On the other hand, when caring for a nursing home-bound patient, coordination of care across hospitals, rehab centers, nurses, pharmacists and other caregivers is vital. So full-fledged interoperability in this setting must be effective horizontally, i.e. between institutions. Without a richly-detailed history of care, it can be quite difficult to help a dependent patient with a low level of physical or mental functioning effectively. (For more background on nursing home data sharing click here.)

Then, consider the case of a healthy married couple with two healthy children. Getting together the right data on these patients may simply be a matter of seeing to it that urgent care visit data is shared with a primary care physician, and that the occasional specialist is looped in as needed. To serve this population, in other words, you don’t need too many bells and whistles interoperability-wise.

Of course, it would be great if we could throw the floodgates open and share data with everyone everywhere the way, say, cellular networks do already. But given that such in event won’t happen anytime in the near future, it probably makes sense to limit our expectations and build some data sharing models that work today.

ZibdyHealth Adapts to Sub-Optimal Data Exchange Standards for a Personal Health Record

Posted on May 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.

Reformers in the health care field, quite properly, emphasize new payment models and culture changes to drive improvements in outcomes. But we can’t ignore the barriers that current technology puts in the way of well-meaning reformers. This article discusses one of the many companies offering a patient health record (PHR) and the ways they’ve adapted to a very flawed model for data storage and exchange.

I had the honor to be contacted by Dr. Hirdey Bhathal, CEO/Founder of ZibdyHealth. Like many companies angling to develop a market for PHRs, ZibdyHealth offers a wide range of services to patients. Unlike, say, Google Health (of blessed memory) or Microsoft HealthVault, ZibdyHealth doesn’t just aspire to store your data, but to offer additional services that make it intensely valuable to you. Charts and visualizations. for instance, will let you see your progress with laboratory and device data over time. They call this a “Smart HIE.” I’ll look a bit at what they offer, and then at the broken model for data exchange that they had to overcome in the health care industry.

The ZibdyHealth application

Setting up an account with ZibdyHealth is as easy as joining Facebook. Once you’re there, you can create health information manually. The company is working with fitness device makers to allow automatic uploads of device data, which can then be saved as a standard Continuity of Care Document (CCD) and offered to doctors.

You can also upload information from your physician via their health care portal–with a degree of ease or difficulty depending on your provider–and share it with other clinicians or family members (Figure 1). You have fine-grained control over which medications, diagnoses, and other information to share, a form of control called segmentation in health care.

Figure 1. Zibdy discharge summary displayed on mobile device

Figure 1. Summary of visit in Zibdy

Dr. Bhathal would like his application to serve whole families and teams, not just individuals. Whether you are caring for your infant or your aging grandmother, they want their platform to meet your needs. In fact, they are planning to deploy their application in some developing nations as an electronic medical record for rural settings, where one healthcare provider will be able to manage the health data for an entire village.

Currently, ZibdyHealth allows speciality clinics to share information with the patient’s regular doctor, helps identify interactions between drugs provided by different doctors, and allows parents to share their children’s health information with schools. This consolidation and quick sharing of medical information will work well with minute clinics or virtual MD visits.

ZibdyHealth is HIPAA-compliant, and support highly secure 256-bit AES encryption for data exchange. Like health care providers, they may share data with partners for operational purposes, but they promise never to sell your data–unlike many popular patient networks. Although they sometimes aggregate anonymized data, they do so to offer you better services, not to sell it on the market or to sell you other services themselves.

In some ways, ZibdyHealth is like a health information exchange (HIE), and as we shall see, they face some of the same problems. But current HIEs connect only health care providers, and are generally limited to large health care systems with ample resources. PHR applications such as ZibdyHealth aim to connect physicians and patients with others, such as family members, therapists, nursing homes, assisted care facilities, and independent living facilities. In addition, most HIEs only work within small states or regions, whereas ZibdyHealth is global. They plan to follow a business model where they provide the application for free to individuals, without advertisements, but charge enterprises who choose the application in order to reach and serve their patients.

Tackling the data dilemma

We’d see a lot more services like ZibdyHealth (and they’d be more popular with patients, providers, and payers) if data exchange worked like it does in the travel industry or other savvy market sectors. Interoperability will enable the “HIE of one” I introduced in an earlier article. In the meantime, ZibdyHealth has carried out a Herculean effort to do the best they can in today’s health exchange setting.

What do they use to get data from patient portals and clinicians’ EHRs? In a phrase, every recourse possible.

  • Many organizations now offer portals that allow patients to download their records in CCD format. ZibdyHealth works with a number of prominent institutions to make uploading easy (Figure 2). Or course, the solution is always a contingent one, because the provider still owns your data. After your next visit, you have to download it again. ZibdyHealth is working on automating this updating process so that providers can feed this information to the patient routinely and, by uploading the discharge CCD as part of a patient’s discharge process, ensure an easy and accurate transition of care.

  • Figure 2. List of electronic records uploaded to Zibdy through their CCD output

    Figure 2. List of uploaded CCDs

  • If providers aren’t on ZibdyHealth’s list of partners, but still offer a CCD, you can download it yourself using whatever mechanism your provider offers, then upload it to ZibdyHealth. ZibdyHealth has invested an enormous amount to parse the various fields of different EHRs and figure out where information is, because the CCD is a very imperfect standard and EHRs differ greatly. I tried the download/upload technique with my own primary care provider and found that ZibdyHealth handled it gracefully.

  • ZibdyHealth also supports Blue Button, the widely adopted XML format that originated at the VA as a text file.

I see ZibdyHealth as one of the early explorers who have to hew a path through the forest to reach their goal. As more individuals come to appreciate the benefits of such services, roads will be paved. Each patient who demands that their doctor make it easy to connect with an application like ZibdyHealth will bring closer the day when we won’t have to contort ourselves to share data.

When Providing a Health Service, the Infrastructure Behind the API is Equally Important

Posted on May 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.

In my ongoing review of application programming interfaces (APIs) as a technical solution for offering rich and flexible services in health care, I recently ran into two companies who showed as much enthusiasm for their internal technologies behind the APIs as for the APIs themselves. APIs are no longer a novelty in health services, as they were just five years ago. As the field gets crowded, maintenance and performance take on more critical roles in offering a successful business–so let’s see how Orion Health and Mana Health back up their very different offerings.

Orion Health

This is a large analytics firm that has staked a a major claim in the White House’s Precision Medicine Initiative. Orion Health’s data platform, Amadeus, addresses population health management as well as “considering how they can better tailor care for each chronically ill individual,” as put by Dave Bennett, executive vice president for Product & Strategy. “We like to say that population health is the who and precision medicine is the how.” Thus, Amadeus can harmonize a huge variety of inputs, such as how many steps a patient takes each day at home, to prevent readmissions.

Orion Health has a cloud service, a capacity for handling huge data sets such as genomes, and a selection of tools for handling such varied sources as clinical, claims, pharmacy, genetic, and consumer device or other patient-generated data. Environmental and social data are currently being added. It has more than 90 million patient records in its systems worldwide.

Patient matching links up data sets from different providers. All this data is ingested, normalized, and made accessible through APIs to authorized parties. Customers can write their own applications, visualizations, and SQL queries. Amadeus is used by the Centers for Disease Control, and many hospitals join the chorus to submit data to the CDC.

So far, Orion Health resembles some other big initiatives that major companies in the health care space are offering. I covered services from Philips in a recent article, and another site talks about GE. Bennett says that Orion Health really distinguishes itself through the computing infrastructure that drives the analytics and data access.

Many companies use conventional relational database as their canonical data store. Relational databases are 1980s-era technology, unmatched in their robustness and sophistication in querying (through the SQL language), but becoming a bottleneck for the data sizes that health analytics deals with.

Over the past decade, every industry that needs to handle enormous, streaming sets of data has turned to a variety of data stores known collectively as NoSQL. Ironically, these are often conceptually simpler than SQL databases and have roots going much farther back in computing history (such as key/value stores). But these data stores let organizations run a critical subset of queries in real time over huge data sets. In addition, analytics are carried out by newer MapReduce algorithms and in-memory services such as Spark. As an added impetus for development, these new technologies are usually free and open source software.

Amadeus itself stores data in Cassandra, one of the most mature NoSQL data stores, and uses Spark for processing. According to Bennett, “Spark enables Amadeus to future proof healthcare organizations for long term innovation. Bringing data and analytics together in the cloud allows our customers to generate deeper insights efficiently and with increased relevancy, due to the rapidity of the analytics engine and the streaming of current data in Amadeus. All this can be done at a lower cost than traditional healthcare analytics that move the data from various data warehouses that are still siloed.” Elastic Search is also used. In short, the third-party tools used within Orion Health are ordinary and commonly found. It is simply modern in the same way as computing facilities in other industries–così fan tutte.

Mana Health

This company integrates device data into EHRs and other data stores. It achieved fame when it was chosen for the New York State patient portal. According to Raj Amin, co-founder and Executive Chairman, the company won over the judges with the convenient and slick tile concept in their user interface. Each tile could be clicked to reveal a deeper level of detail in the data. The company tries to serve clinicians, patients, and data analysts alike. Clients include HIEs, health systems, medical device manufacturers, insurers, and app developers.

Like Orion Health, Mana Health is very conscious of staying on the leading edge of technology. They are mobile-friendly and architect their solutions using microservices, a popular form of modular development that attempts to maximize flexibility in coding and deploying new services. On a lark, they developed a VR engine compatible with the Oculus Rift to showcase what can creatively be built on their API. Although this Rift project has no current uses, the development effort helps them stay flexible so that they can adapt to whatever new technologies come down the pike.

Because Mana Health developed their API some eighteen months ago, they pre-dated some newer approaches and standards. They plan to offer compatibility with emerging standards such as FHIR that see industry adoption. The company recently was announced as a partner in the Commonwell Alliance, a project formed by a wide selection of major EHR vendors to pursue interoperability.

To support machine learning, Mana Health stores data in an open source database called Neo4j. This is a very unusual technology called a graph database, whose history and purposes I described two years ago.

Graphs are familiar to anyone who has seen airline maps showing the flights between cities. Graphs are also common for showing social connections, such as your friends-of-friends on Facebook. In health care, as well, graphs are very useful tools. They show relationships, but in a very different way from relational databases. Graphs are better than relational databases at tracing connections between people or other entities. For instance, a team led by health IT expert Fred Trotter used Neo4J to store and query the data in DocGraph, linking primary care physicians to the specialists to which they refer patients.

In their unique ways, Mana Health and Orion Health follow trends in the computing industry and judiciously choose tools that offer new forms of access to data, while being proven in the field. Although commenters in health IT emphasize the importance of good user interfaces, infrastructure matters too.

Our Uncontrolled Health Care Costs Can Be Traced to Data and Communication Failures (Part 2 of 2)

Posted on April 13, 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 provided whatever detail I could find on the costs of poor communications and data exchange among health care providers. But in truth, it’s hard to imagine the toll taken by communications failures beyond certain obvious consequences, such as repeated tests and avoidable medical errors. One has to think about how the field operates and what we would be capable of with proper use of data.

As patients move from PCP to specialist, from hospital to rehab facility, and from district to district, their providers need not only discharge summaries but intensive coordination to prevent relapses. Our doctors are great at fixing a diabetic episode or heart-related event. Where we fall down is on getting the patient the continued care she needs, ensuring she obtains and ingests her medication, and encouraging her to make the substantial life-style changes that can prevent reoccurrences. Modern health really is all about collaboration–but doctors are decades behind the times.

Clinicians were largely unprepared to handle the new patients brought to them by the Affordable Care Act. Examining the impact of new enrollees, who “have higher rates of disease and received significantly more medical care,” an industry spokesperson said, “The findings underscore the need for all of us in the health care system, and newly insured consumers, to work together to make sure that people get the right health care service in the right care setting and at the right time…Better communication and coordination is needed so that everyone understands how to avoid unnecessary emergency room visits, make full use of primary care and preventive services and learn how to properly adhere to their medications.” Just where the health providers fall short.

All these failures to communicate may explain the disappointing performance of patient centered medical homes and Accountable Care Organizations. While many factors go into the success or failure of such complex practices, a high rate of failure suggests that they’re not really carrying out the coordinated care they were meant to deliver. Naturally, problems persist in getting data from one vendor’s electronic health record to another.

Urgent care clinics, and other alternative treatment facilities offered in places such as pharmacies, can potentially lower costs, but not if the regular health system fails to integrate them.

Successes in coordinated care show how powerful it can be. Even so simple a practice as showing medical records to patients can improve care, but most clinicians still deny patients access to their data.

One care practice drastically lowered ER admissions through a notably low-tech policy–refering their patients to a clinic for follow-up care. This is only the beginning of what we could achieve. If modern communications were in place, hospitals would be linked so that a CDC warning could go to all of them instantly. And if clinicians and their record systems were set up to handle patient-generated data, they could discover a lot more about the patients and monitor behavior change.

How are the hospitals and clinics responding to this crisis and the public pressure to shape up? They push back as if it was not their problem. They claim they are moving toward better information sharing and teamwork, but never get there.

One of their favorite gambits is to ask the government to reward them for achieving interoperability 90 days out of the year. They make this request with no groveling, no tears of shame, no admission that they have failed in their responsibility to meet reasonable goals set seven years ago. If I delivered my projects only 25% of the time, I’d have trouble justifying myself to my employer, especially if I received my compensation plan seven years ago. Could the medical industry imagine that it owes us a modicum of effort?

Robert Schultz, a writer and entrepreneur in health care, says, “Underlying the broken communications model is a lack of empathy for the ultimate person affected–the patient. Health care is one of the few industries where the user is not necessarily the party paying for the product or service. Electronic health records and health information exchanges are designed around the insurance companies, accountable care organizations, or providers, instead of around understanding the challenges and obstacles that patients face on a daily basis. (There are so many!) The innovators who understand the role of the patient in this new accountable care climate will be winners. Those who suffer from the burden of legacy will continue to see the same problems and will become eclipsed by other organizations who can sustain patient engagement and prove value within accountable care contracts.”

Alternative factors

Of course, after such a provocative accusation, I should consider the other contributors that are often blamed for increasing health care costs.

An aging population

Older people have more chronic diseases, a trend that is straining health care systems from Cuba to Japan. This demographic reality makes intelligent data use even more important: remote monitoring for chronic conditions, graceful care transitions, and patient coordination.

The rising cost of drugs

Dramatically increasing drug prices are certainly straining our payment systems. Doctors who took research seriously could be pushing back against patient requests for drugs that work more often in TV ads than in real life. Doctors could look at holistic pain treatments such as yoga and biofeedback, instead of launching the worst opiate addiction crisis America has ever had.

Government bureaucracy

This seems to be a condition of life we need to deal with, like death and taxes. True, the Centers for Medicare & Medicaid Services (CMS) keeps adding requirements for data to report. But much of it could be automated if clinical settings adopted modern programming practices. Furthermore, this data appears to be a burden only because it isn’t exploited. Most of it is quite useful, and it just takes agile organizations to query it.

Intermediaries

Reflecting the Byzantine complexity of our payment systems, a huge number of middlemen–pharmacy benefits managers, medical billing clearinghouses, even the insurers themselves–enter the system, each taking its cut of the profits. Single-payer insurance has long been touted as a solution, but I’d rather push for better and cheaper treatments than attack the politically entrenched payment system.

Under-funded public health

Poverty, pollution, stress, and other external factors have huge impacts on health. This problem isn’t about clinicians, of course, it’s about all of us. But clinicians could be doing more to document these and intervene to improve them.

Clinicians like to point to barriers in their way of adopting information-based reforms, and tell us to tolerate the pace of change. But like the rising seas of climate change, the bite of health care costs will not tolerate complacency. The hard part is that merely wagging fingers and imposing goals–the ONC’s primary interventions–will not produce change. I think that reform will happen in pockets throughout the industry–such as the self-insured employers covered in a recent article–and eventually force incumbents to evolve or die.

The precision medicine initiative, and numerous databases being built up around the country with public health data, may contribute to a breakthrough by showing us the true quality of different types of care, and helping us reward clinicians fairly for treating patients of varying needs and risk. The FHIR standard may bring electronic health records in line. Analytics, currently a luxury available only to major health conglomerates, will become more commoditized and reach other providers.

But clinicians also have to do their part, and start acting like the future is here now. Those who make a priority of data sharing and communication will set themselves up for success long-term.

Our Uncontrolled Health Care Costs Can Be Traced to Data and Communication Failures (Part 1 of 2)

Posted on April 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.

A host of scapegoats, ranging from the Affordable Care Act to unscrupulous pharmaceutical companies, have been blamed for the rise in health care costs that are destroying our financial well-being, our social fabric, and our political balance. In this article I suggest a more appropriate target: the inability of health care providers to collaborate and share information. To some extent, our health care crisis is an IT problem–but with organizational and cultural roots.

It’s well known that large numbers of patients have difficulty with costs, and that employees’ share of the burden is rising. We’re going to have to update the famous Rodney Dangerfield joke:

My doctor said, “You’re going to be sick.” I said I wanted a second opinion. He answered, “OK, you’re going to be poor too.”

Most of us know about the insidious role of health care costs in holding down wages, in the fight by Wisconsin Governor Scott Walker over pensions that tore the country apart, in crippling small businesses, and in narrowing our choice of health care providers. Not all realize, though, that the crisis is leaching through the health care industry as well, causing hospitals to fail, insurers to push costs onto subscribers and abandon the exchanges where low-income people get their insurance, co-ops to close, and governments to throw people off of subsidized care, threatening the very universal coverage that the ACA aimed to achieve.

Lessons from a ground-breaking book by T.R. Reid, The Healing of America, suggests that we’re undergoing a painful transition that every country has traversed to achieve a rational health care system. Like us, other countries started by committing themselves to universal health care access. This then puts on the pressure to control costs, as well as the opportunities for coordination and economies of scale that eventually institute those controls. Solutions will take time, but we need to be smart about where to focus our efforts.

Before even the ACA, the 2009 HITECH act established goals of data exchange and coordinated patient care. But seven years later, doctors still lag in:

  • Coordinating with other providers treating the patients.

  • Sending information that providers need to adequately treat the patients.

  • Basing treatment decisions on evidence from research.

  • Providing patients with their own health care data.

We’ll look next at the reports behind these claims, and at the effects of the problems.

Why doctors don’t work together effectively

A recent report released by the ONC, and covered by me in a recent article, revealed the poor state of data sharing, after decades of Health Information Exchanges and four years of Meaningful Use. Health IT observers expect interoperability to continue being a challenge, even as changes in technology, regulations, and consumer action push providers to do it.

If merely exchanging documents is so hard–and often unachieved–patient-focused, coordinated care is clearly impossible. Integrating behavioral care to address chronic conditions will remain a fantasy.

Evidence-based medicine is also more of an aspiration than a reality. Research is not always trustworthy, but we must have more respect for the science than hospitals were found to have in a recent GAO report. They fail to collect data either on the problems leading to errors or on the efficacy of solutions. There are incentive programs from payers, but no one knows whether they help. Doctors are still ordering far too many unnecessary tests.

Many companies in the health analytics space offer services that can bring more certainty to the practice of medicine, and I often cover them in these postings. Although increasingly cited as a priority, analytical services are still adopted by only a fraction of health care providers.

Patients across the country are suffering from disrupted care as insurers narrow their networks. It may be fair to force patients to seek less expensive providers–but not when all their records get lost during the transition. This is all too likely in the current non-interoperable environment. Of course, redundant testing and treatment errors caused by ignorance could erase the gains of going to low-cost providers.

Some have bravely tallied up the costs of waste and lack of care coordination in health care. Some causes, such as fraud and price manipulation, are not attributable to the health IT failures I describe. But an enormous chunk of costs directly implicate communications and data handling problems, including administrative overhead. The next section of this article will explore what this means in day-to-day health care.

Idiosyncratic Recommendations Based on Widespread Principles: the Health IT Policy Committee Report

Posted on December 21, 2015 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.

Congress received an odd document last week from an advisory committee on Health IT. It takes an unexpectedly new–and demandingly detailed–approach to the perennial problem of health record interoperability. However, if one analyzes the authors’ reasoning, it turns out to be based on unstated principles that are widely accepted in health care:

  1. The market is broken, and the government must intervene either through incentives or through requirements.

  2. The intervention should be based on operational or clinical goals, not dictating the adoption of specific technologies.

  3. Policy-makers should pick off low-hanging fruit through goals that produce potentially large benefits with relative ease.

Read more..

Significant Articles in the Health IT Community in 2015

Posted on December 15, 2015 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.

Have you kept current with changes in device connectivity, Meaningful Use, analytics in healthcare, and other health IT topics during 2015? Here are some of the articles I find significant that came out over the past year.

The year kicked off with an ominous poll about Stage 2 Meaningful Use, with implications that came to a head later with the release of Stage 3 requirements. Out of 1800 physicians polled around the beginning of the year, more than half were throwing in the towel–they were not even going to try to qualify for Stage 2 payments. Negotiations over Stage 3 of Meaningful Use were intense and fierce. A January 2015 letter from medical associations to ONC asked for more certainty around testing and certification, and mentioned the need for better data exchange (which the health field likes to call interoperability) in the C-CDA, the most popular document exchange format.

A number of expert panels asked ONC to cut back on some requirements, including public health measures and patient view-download-transmit. One major industry group asked for a delay of Stage 3 till 2019, essentially tolerating a lack of communication among EHRs. The final rules, absurdly described as a simplification, backed down on nothing from patient data access to quality measure reporting. Beth Israel CIO John Halamka–who has shuttled back and forth between his Massachusetts home and Washington, DC to advise ONC on how to achieve health IT reform–took aim at Meaningful Use and several other federal initiatives.

Another harbinger of emerging issues in health IT came in January with a speech about privacy risks in connected devices by the head of the Federal Trade Commission (not an organization we hear from often in the health IT space). The FTC is concerned about the security of recent trends in what industry analysts like to call the Internet of Things, and medical devices rank high in these risks. The speech was a lead-up to a major report issued by the FTC on protecting devices in the Internet of Things. Articles in WIRED and Bloomberg described serious security flaws. In August, John Halamka wrote own warning about medical devices, which have not yet started taking security really seriously. Smart watches are just as vulnerable as other devices.

Because so much medical innovation is happening in fast-moving software, and low-budget developers are hankering for quick and cheap ways to release their applications, in February, the FDA started to chip away at its bureaucratic gamut by releasing guidelines releasing developers from FDA regulation medical apps without impacts on treatment and apps used just to transfer data or do similarly non-transformative operations. They also released a rule for unique IDs on medical devices, a long-overdue measure that helps hospitals and researchers integrate devices into monitoring systems. Without clear and unambiguous IDs, one cannot trace which safety problems are associated with which devices. Other forms of automation may also now become possible. In September, the FDA announced a public advisory committee on devices.

Another FDA decision with a potential long-range impact was allowing 23andMe to market its genetic testing to consumers.

The Department of Health and Human Services has taken on exceedingly ambitious goals during 2015. In addition to the daunting Stage 3 of Meaningful Use, they announced a substantial increase in the use of fee-for-value, although they would still leave half of providers on the old system of doling out individual payments for individual procedures. In December, National Coordinator Karen DeSalvo announced that Health Information Exchanges (which limit themselves only to a small geographic area, or sometimes one state) would be able to exchange data throughout the country within one year. Observers immediately pointed out that the state of interoperability is not ready for this transition (and they could well have added the need for better analytics as well). HHS’s five-year plan includes the use of patient-generated and non-clinical data.

The poor state of interoperability was highlighted in an article about fees charged by EHR vendors just for setting up a connection and for each data transfer.

In the perennial search for why doctors are not exchanging patient information, attention has turned to rumors of deliberate information blocking. It’s a difficult accusation to pin down. Is information blocked by health care providers or by vendors? Does charging a fee, refusing to support a particular form of information exchange, or using a unique data format constitute information blocking? On the positive side, unnecessary imaging procedures can be reduced through information exchange.

Accountable Care Organizations are also having trouble, both because they are information-poor and because the CMS version of fee-for-value is too timid, along with other financial blows and perhaps an inability to retain patients. An August article analyzed the positives and negatives in a CMS announcement. On a large scale, fee-for-value may work. But a key component of improvement in chronic conditions is behavioral health which EHRs are also unsuited for.

Pricing and consumer choice have become a major battleground in the current health insurance business. The steep rise in health insurance deductibles and copays has been justified (somewhat retroactively) by claiming that patients should have more responsibility to control health care costs. But the reality of health care shopping points in the other direction. A report card on state price transparency laws found the situation “bleak.” Another article shows that efforts to list prices are hampered by interoperability and other problems. One personal account of a billing disaster shows the state of price transparency today, and may be dangerous to read because it could trigger traumatic memories of your own interactions with health providers and insurers. Narrow and confusing insurance networks as well as fragmented delivery of services hamper doctor shopping. You may go to a doctor who your insurance plan assures you is in their network, only to be charged outrageous out-of-network costs. Tools are often out of date overly simplistic.

In regard to the quality ratings that are supposed to allow intelligent choices to patients, A study found that four hospital rating sites have very different ratings for the same hospitals. The criteria used to rate them is inconsistent. Quality measures provided by government databases are marred by incorrect data. The American Medical Association, always disturbed by public ratings of doctors for obvious reasons, recently complained of incorrect numbers from the Centers for Medicare & Medicaid Services. In July, the ProPublica site offered a search service called the Surgeon Scorecard. One article summarized the many positive and negative reactions. The New England Journal of Medicine has called ratings of surgeons unreliable.

2015 was the year of the intensely watched Department of Defense upgrade to its health care system. One long article offered an in-depth examination of DoD options and their implications for the evolution of health care. Another article promoted the advantages of open-source VistA, an argument that was not persuasive enough for the DoD. Still, openness was one of the criteria sought by the DoD.

The remote delivery of information, monitoring, and treatment (which goes by the quaint term “telemedicine”) has been the subject of much discussion. Those concerned with this development can follow the links in a summary article to see the various positions of major industry players. One advocate of patient empowerment interviewed doctors to find that, contrary to common fears, they can offer email access to patients without becoming overwhelmed. In fact, they think it leads to better outcomes. (However, it still isn’t reimbursed.)

Laws permitting reimbursement for telemedicine continued to spread among the states. But a major battle shaped up around a ruling in Texas that doctors have a pre-existing face-to-face meeting with any patient whom they want to treat remotely. The spread of telemedicine depends also on reform of state licensing laws to permit practices across state lines.

Much wailing and tears welled up over the required transition from ICD-9 to ICD-10. The AMA, with some good arguments, suggested just waiting for ICD-11. But the transition cost much less than anticipated, making ICD-10 much less of a hot button, although it may be harmful to diagnosis.

Formal studies of EHR strengths and weaknesses are rare, so I’ll mention this survey finding that EHRs aid with public health but are ungainly for the sophisticated uses required for long-term, accountable patient care. Meanwhile, half of hospitals surveyed are unhappy with their EHRs’ usability and functionality and doctors are increasingly frustrated with EHRs. Nurses complained about technologies’s time demands and the eternal lack of interoperability. A HIMSS survey turned up somewhat more postive feelings.

EHRs are also expensive enough to hurt hospital balance sheets and force them to forgo other important expenditures.

Electronic health records also took a hit from ONC’s Sentinel Events program. To err, it seems, is not only human but now computer-aided. A Sentinel Event Alert indicated that more errors in health IT products should be reported, claiming that many go unreported because patient harm was avoided. The FDA started checking self-reported problems on PatientsLikeMe for adverse drug events.

The ONC reported gains in patient ability to view, download, and transmit their health information online, but found patient portals still limited. Although one article praised patient portals by Epic, Allscripts, and NextGen, an overview of studies found that patient portals are disappointing, partly because elderly patients have trouble with them. A literature review highlighted where patient portals fall short. In contrast, giving patients full access to doctors’ notes increases compliance and reduces errors. HHS’s Office of Civil Rights released rules underlining patients’ rights to access their data.

While we’re wallowing in downers, review a study questioning the value of patient-centered medical homes.

Reuters published a warning about employee wellness programs, which are nowhere near as fair or accurate as they claim to be. They are turning into just another expression of unequal power between employer and employee, with tendencies to punish sick people.

An interesting article questioned the industry narrative about the medical device tax in the Affordable Care Act, saying that the industry is expanding robustly in the face of the tax. However, this tax is still a hot political issue.

Does anyone remember that Republican congressmen published an alternative health care reform plan to replace the ACA? An analysis finds both good and bad points in its approach to mandates, malpractice, and insurance coverage.

Early reports on use of Apple’s open ResearchKit suggested problems with selection bias and diversity.

An in-depth look at the use of devices to enhance mental activity examined where they might be useful or harmful.

A major genetic data mining effort by pharma companies and Britain’s National Health Service was announced. The FDA announced a site called precisionFDA for sharing resources related to genetic testing. A recent site invites people to upload health and fitness data to support research.

As data becomes more liquid and is collected by more entities, patient privacy suffers. An analysis of web sites turned up shocking practices in , even at supposedly reputable sites like WebMD. Lax security in health care networks was addressed in a Forbes article.

Of minor interest to health IT workers, but eagerly awaited by doctors, was Congress’s “doc fix” to Medicare’s sustainable growth rate formula. The bill did contain additional clauses that were called significant by a number of observers, including former National Coordinator Farzad Mostashari no less, for opening up new initiatives in interoperability, telehealth, patient monitoring, and especially fee-for-value.

Connected health took a step forward when CMS issued reimbursement guidelines for patient monitoring in the community.

A wonky but important dispute concerned whether self-insured employers should be required to report public health measures, because public health by definition needs to draw information from as wide a population as possible.

Data breaches always make lurid news, sometimes under surprising circumstances, and not always caused by health care providers. The 2015 security news was dominated by a massive breach at the Anthem health insurer.

Along with great fanfare in Scientific American for “precision medicine,” another Scientific American article covered its privacy risks.

A blog posting promoted early and intensive interactions with end users during app design.

A study found that HIT implementations hamper clinicians, but could not identify the reasons.

Natural language processing was praised for its potential for simplifying data entry, and to discover useful side effects and treatment issues.

CVS’s refusal to stock tobacco products was called “a major sea-change for public health” and part of a general trend of pharmacies toward whole care of the patient.

A long interview with FHIR leader Grahame Grieve described the progress of the project, and its the need for clinicians to take data exchange seriously. A quiet milestone was reached in October with a a production version from Cerner.

Given the frequent invocation of Uber (even more than the Cheesecake Factory) as a model for health IT innovation, it’s worth seeing the reasons that model is inapplicable.

A number of hot new sensors and devices were announced, including a tiny sensor from Intel, a device from Google to measure blood sugar and another for multiple vital signs, enhancements to Microsoft products, a temperature monitor for babies, a headset for detecting epilepsy, cheap cameras from New Zealand and MIT for doing retinal scans, a smart phone app for recognizing respiratory illnesses, a smart-phone connected device for detecting brain injuries and one for detecting cancer, a sleep-tracking ring, bed sensors, ultrasound-guided needle placement, a device for detecting pneumonia, and a pill that can track heartbeats.

The medical field isn’t making extensive use yet of data collection and analysis–or uses analytics for financial gain rather than patient care–the potential is demonstrated by many isolated success stories, including one from Johns Hopkins study using 25 patient measures to study sepsis and another from an Ontario hospital. In an intriguing peek at our possible future, IBM Watson has started to integrate patient data with its base of clinical research studies.

Frustrated enough with 2015? To end on an upbeat note, envision a future made bright by predictive analytics.

Shimmer Addresses Interoperability Headaches in Fitness and Medical Devices

Posted on October 19, 2015 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 promise of device data pervades the health care field. It’s intrinsic to patient-centered medical homes, it beckons clinicians who are enamored with hopes for patient engagement, and it causes data analysts in health care to salivate. This promise also drives the data aggregation services offered by Validic and just recently, the Shimmer integration tool from Open mHealth. But according to David Haddad, Executive Director and Co-Founder of Open mHealth, devices resist attempts to yield up their data to programmers and automated tools.

Every device manufacturer has its own idiosyncratic way of handling data, focused on the particular uses for its own device. According to Haddad, for instance, different manufacturers provide completely different representations for the same data, leave out information like time zones and units, and can provide information as granular as once per second or as vague as once per day.. Even something as basic as secure connectivity is unstandardized. Although most vendors use the OAuth protocol that is widespread on the Web, many alter it in arbitrary ways. This puts barriers in the way of connecting to their APIs.

Validic and Shimmer have to overcome these hurdles one by one, vendor by vendor. The situation is just like the burdens facing applications that work with electronic health records. Haddad reports that the cacophony of standards among device vendors seems to come from lack of attention to the API side of their product, not deliberate obstructionism. With all the things device manufacturers have to worry about–the weight, feel, and attractiveness of the object itself, deals with payers and retailers offering the product, user interface issues, etc.–the API always seems to be an afterthought. (Apple may be an exception.)

So when Shimmer contacts the tool makers at these vendors, most respond and take suggestions in a positive manner. But they may have just one or two programmers working on the API, so progress is slow. It comes down to the old problem in health care: even with government emphasis on data sharing, there is still no strong advocate for interoperability in the field.

Why did Open mHealth take on this snake’s nest and develop Shimmer? Haddad says they figured that the advantages of open source–low cost of adoption and the ease of adding extensions–will open up new possibilities for app developers, clinical settings, and researchers. Most sites are unsure what to do with device data and are just starting to experiment with it. Being able to develop a prototype they can throw away later will foster innovation. Open mHealth has produced a detailed cost analysis in an appeal to researchers and clinicians to give Shimmer a try.

Shimmer, like the rest of the Open mHealth tools, rests on their own schemas for health data. The schemas in themselves can’t revolutionize health care. Every programmer maintains a healthy cynicism about schemas, harking back to xkcd’s cartoon about “one universal standard that covers everyone’s use cases.” But this schema took a broader view than most programs in health care, based on design principles that try to balance simplicity against usefulness and specificity. Of course, every attempt to maintain a balance comes up against complaints the the choices were too complex for some users, too simple for others. The true effects of Open mHealth appear as it is put to use–and that’s where open source tools and community efforts really can make a difference in health care. The schemas are showing value through their community adoption: they are already used by many sites, including some major commercial users, prestigious research sites, and start-ups.

A Pulse app translates between HL7 and the Open mHealth schema. This brings Open mHealth tools within easy reach of EHR vendors trying to support extensions, or users of the EHRs who consume their HL7-formatted data.

The Granola library translates between Apple’s HealthKit and JSON. Built on this library, the hipbone app takes data from an iPhone and puts it in JSON format into a Dropbox file. This makes it easier for researchers to play with HealthKit data.

In short, the walls separating medicine must be beaten down app by app, project by project. As researchers and clinicians release open source tools that tie different systems together, a bridge between products will emerge. Haddad hopes that more widespread adoption of the Open mHealth schema and Shimmer will increase pressure on device vendors to produce standardized data accessible to all.

Could the DoD be SMART to Choose Cerner?

Posted on August 4, 2015 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.

Even before the health IT world could react (with surprise) to the choice of a Cerner EHR (through its lead partner, Leidos Health Solutions Group) by the Department of Defense, rumors have it that Cerner beat out Epic through the perception that it is more open and committed to interoperability. The first roll-out they’ll do at the DoD is certain to be based on HL7 version 2 and more recent version 3 standards (such as the C-CDA) that are in common use today. But the bright shining gems of health exchange–SMART and FHIR–are anticipated for the DoD’s future.

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