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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.

5 Mobile Opportunities in Health Care

Posted on November 10, 2015 I Written By

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

I was recently reading through this whitepaper called Going Mobile: Integrating Mobile to Enhance Patient Care and Practice Efficiency. The concept is lovely, but I’m afraid that most healthcare IT has fallen short of the mobile promise. We see the benefits of mobile in so many other aspects of our lives, but we’ve fallen short in delivering that same benefit in health care.

The good part is that the opportunity is still available for health care to benefit from mobile technology. So, even if the whitepaper might be talking about potential rather than the reality of what’s available today, it’s worth considering why more EHR vendors and other health care IT companies should invest in mobile.

The whitepaper offers 5 opportunities for mobile:

  • Clinical Decision Support – The first iteration of this was Epocrates. It was mostly information, but that’s where clinical decision support starts. Hopefully we’ll see rapid advancement in this area. Mobile makes that clinical decision support easily available at the point of care.
  • Workflow Efficiencies – It’s unfortunate that we haven’t realized this benefit. Mobile can really make things more efficient if we create the right interface. I just have seen so few EHR vendors invest in the right mobile interface to take advantage of these efficiencies.
  • Communication and Coordination – We’re starting to see this happen with services like secure text message. You’d think we’d need something more, but secure text message is a great place to start. It’s easily learned, completely malleable to any workflow, and easily implemented. Over time I’m sure we’ll find even better ways to communicate and coordinate care on mobile.
  • Patient Engagement – One of my favorite stats is that 98% of text messages get read. Plus, they get read almost immediately. Compare that to email and you’ll see why mobile is such an opportunity to engage the patient. We’re seeing more and more of these offerings on the market.
  • Security – Some might consider this a challenge, but I think it’s also an opportunity. Ever heard of 2 factor authentication. Your mobile device is perfect for it and provides a much more secure login. Certainly there are security challenges with mobile devices as well, but it can also be used as a great opportunity to improve how we approach security.

Be sure to check out the whitepaper where they dive a lot deeper into each of these subjects. Like I said, the benefits of mobile have not been really realized in health care, but that opportunity is still available.

Clinical Decision Support Should Be Open Source

Posted on January 26, 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.

Clinical decision support is a long-standing occupant of the medical setting. It got in the door with electronic medical records, and has recently received a facelift under the term “evidence based medicine.” We are told that CDS or EBM is becoming fine-tuned and energized through powerful analytics that pick up the increasing number of patient and public health data sets out in the field. But how does the clinician know that the advice given for a treatment or test is well-founded?

Most experts reaffirm that the final word lies with the physician–that each patient is unique, and thus no canned set of rules can substitute for the care that the physician must give to a patient’s particular conditions (such as a compromised heart or a history of suicidal ideation) and the sustained attention that the physician must give to the effects of treatment. Still, when the industry gives a platform to futurists such as Vinod Khosla who suggest that CDS can become more reliable than a physician’s judgment, we have to start demanding a lot more reliability from the computer.

It’s worth stopping a moment to consider the various inputs to CDS. Traditionally, it was based on the results of randomized, double-blind clinical trials. But these have come under scrutiny in recent years for numerous failings: the questionable validity of extending the results found on selected test subjects to a broader population, problems reproducing results for as many as three quarters of the studies, and of course the bias among pharma companies and journals alike for studies showing positive impacts.

More recently, treatment recommendations are being generated from “big data,” which trawl through real-life patient experiences instead of trying to isolate a phenomenon in the lab. These can turn up excellent nuggets of unexpected impacts–such as Vioxx’s famous fatalities–but suffer also from the biases of the researches designing the algorithms, difficulties collecting accurate data, the risk of making invalid correlations, and the risk of inappropriately attributing causation.

A third kind of computerized intervention has recently been heralded: IBM’s Watson. However, Watson does not constitute CDS (at least not in the demo I saw at HIMSS a couple years ago). Rather, Watson just does the work every clinician would ideally do but doesn’t have time for: it consults thousands of clinical studies to find potential diagnoses relevant to the symptoms and history being reported, and ranks these diagnoses by probability. Both of those activities hijack a bit of the clinician’s human judgment, but they do not actually offer recommendations.

So there are clear and present justifications for demanding that CDS vendors demonstrate its reliability. We don’t really know what goes into CDS and how it works. Meanwhile, doctors are getting sick and tired of bearing the liability for all the tools they use, and the burden of their malpractice insurance is becoming a factor in doctors leaving the field. The doctors deserve some transparency and auditing, and so do the patients who ultimately incorporate the benefits and risks of CDS into their bodies.

CDS, like other aspects of the electronic health records into which it is embedded, has never been regulated or subjected to public safety tests and audits. The argument trotted out by EHR vendors–like every industry–when opposing regulation is that it will slow down innovation. But economic arguments have fuzzy boundaries–one can always find another consideration that can reverse the argument. In an industry that people can’t trust, regulation can provide a firm floor on which a new market can be built, and the assurance that CDS is working properly can open up the space for companies to do more of it and charge for it.

Still, there seems to be a pendulum swing away from regulation at present. The FDA has never regulated electronic health records as it has other medical software, and has been carving out classes of medical devices that require little oversight. When it took up EHR safety last year, the FDA asked merely for vendors to participate voluntarily in a “safety center.”

The prerequisite for gauging CDS’s reliability is transparency. Specifically, two aspects should be open:

  • The vendor must specify which studies, or analytics and data sets, went into the recommendation process.

  • The code carrying out the recommendation process must be openly published.

These fundamentals are just the start of of the medical industry’s responsibilities. Independent researchers must evaluate the sources revealed in the first step and determine whether they are the best available choices. Programmers must check the code in the second step for accuracy. These grueling activities should be funded by the clinical institutions that ultimately use the CDS, so that they are on a firm financial basis and free from bias.

The requirement for transparent studies raises the question of open access to medical journals, which is still rare. But that is a complex issue in the fields of research and publishing that I can’t cover here.

Finally, an independent service has to collect reports of CDS failures and make them public, like the FDA Adverse Event Reporting System (FAERS) for drugs, and the FDA’s Manufacturer and User Facility Device Experience (MAUDE) for medical devices.

These requirements are reasonably light-weight, although instituting them will seem like a major upheaval to industries accustomed to working in the dark. What the requirements can do, though, is put CDS on the scientific basis it never has had, and push forward the industry more than any “big data” can do.

Apervita Creates Health Analytics for the Millions

Posted on January 9, 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.

Health officials are constantly talking up the importance of clinical decision support, more popularly known now as evidence-based medicine. We’re owning up to the awkward little fact–which really should embarrass nobody–that most doctors lack expertise on many of the conditions they encounter and can’t read the thousands of relevant studies published each year. New heuristics are developed all the time for things such as predicting cardiac arrest or preventing readmissions after surgery. But most never make their way into the clinic.

Let’s look at what has to happen before doctors and patients can benefit from a discovery:

  1. The researcher has to write a paper with enough detail to create a working program from the heuristic, and has to publish the paper, probably in an obscure journal.

  2. A clinician or administrator has to find the article and line up staff to write and thoroughly test a program.

  3. If the program is to be used outside the hospital where it was created, it has to be disseminated. The hospital is unlikely to have an organization set up to package and market the program. Even if it is simply put out for free use, other institutions have to learn about it and compile it to work on their systems, in order for it to spread widely. Neither the researcher nor the hospital is likely to be compensated for the development of the program.

  4. The program has to be integrated into the doctor’s workflow, by being put on a menu or generating an alert.

Evidence-based medicine, therefore, is failing to tap a lot of resources that could save lives. A commonly cited observation is that research findings take 17 years to go into widespread practice. That’s 17 years of unnecessary and costly suffering.

I have often advocated for better integration of analytics into everyday medical practice, and I found a company called Apervita (originally named Pervasive Health) that jumps off in the right direction. Apervita, which announced a Series A round of funding on January 7, also has potential users outside of clinical settings. Pharma companies can use it to track adverse drug events, while payers can use it to predict fraud and risks to patients. There is not much public health data in the platform yet, but they’re working on it. For instance, Leapfrog group has published hospital safety info through their platform, and Diameter Health provides an all-cause 30-day readmissions prediction for all non-maternal, non-pediatric hospitalizations.

Here’s how the sequence of events I laid out before would go using Apervita:

  1. The researcher implements her algorithm in Python, chosen because Python is easy for non-programmers to learn and is consequently one of the most popular programming languages, particularly in the sciences. Apervita adds functions to Python to make it easy, such as RangeCompute or tables to let you compute with coefficients, and presents these through an IDE.

  2. The researcher creates an analytic on the Apervita platform that describes and publishes the analytic, along with payment terms. Thus, the researcher derives some income from the research and has more motivation to offer the analytic publicly. Conversely, the provider pays only for usage of the analytic, and does not have to license or implement a new software package.

  3. Clinicians search for relevant analytics and upload data to generate reports at a patient or population level. Data in popular formats such as Excel or comma-separated value (CSV) files can be uploaded manually, while programmers can automate data exchange through a RESTful web service, which is currently the most popular way of exchanging data between cooperating programs. Rick Halton, co-founder and Chief Marketing Officer of Apervita, said they are working on support for HL7’s CCD, and are interested in Blue Button+ button, although they are not ready yet to support it.

  4. Clinicians can also make the results easy to consume through personalized dashboards (web pages showing visualizations and current information) or by triggering alerts. A typical dashboard for a hospital administrator might show a graphical thermometer indicating safety rankings at the hospital, along with numbers indicating safety grades. Each department or user could create a dashboard showing exactly what a clinician cares about at the moment–a patient assessment during an admission, or statistics needed for surgical pre-op, for instance.

  5. Apervita builds in version control, and can automatically update user sites with corrections or new versions.

I got a demo of Apervita and found the administration pretty complex, but this seems to be a result of its focus on security and the many options it offers large enterprises to break staff into groups or teams. The bottom line is that Apervita compresses the difficult processes required to turn research into practice and offers them as steps performed through a Web interface or easy programming. Apervita claims to have shown that one intern can create as many as 50 health analytics in one week on their platform, working just from the articles in journals and web resources.

The platform encrypts web requests and is HIPAA-compliant. It can be displayed off-platform, and has been integrated with at least one EHR (OpenMRS).

Always attuned to the technical difficulties of data use, I asked Halton how the users of Apervita analytics could make sure their data formats and types match the formats and types defined by the people who created the analytics. Halton said that the key was the recognition of different ontolgies, and the ability to translate between them using easy-to-create “codesets.”

An ontology is, in general, a way of representing data and the relationships between pieces of data. SNOMED and ICD are examples of common ontologies in health care. An even simpler ontology might simply be a statement that units of a particular data field are measured in milliliters. Whether simple or complex, standard or custom-built, the ontology is specified by the creator of an analytic. If the user has data in a different ontology, a codeset can translate between the two.

As an example of Apervita’s use, a forward prediction algorithm developed by Dr. Dana Edelson and others from the University of Chicago Medical Center can predict cardiac arrests better than the commonly used VitalPAC Early Warning Score (ViEWS) or Modified Early Warning Score (MEWS). Developed from a dataset of over 250,000 patient admissions across five hospitals, “eCART” (electronic Cardiac Arrest Triage) can identify high-risk hospital ward patients and improve ICU triage decisions, often as much as 48 hours in advance.

The new funding will allow Apervita to make their interface even easier for end-users, and to solicit algorithms from leading researchers such as the Mayo Clinic.

Halton heralds Apervita as a “community” for health care analytics for authors and providers. Not only can the creators of analytics share them, but providers can create dashboards or other tools of value to a wide range of colleagues, and share them. I believe that tools like Apervita can bridge the gap between the rare well-funded health clinic with the resources to develop tools, and the thousands of scattered institutions struggling to get the information that will provide better care.

Ebola Lapse in Dallas Offers Few Lessons, Except About Our Over-reliance on Technology

Posted on October 8, 2014 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.

Of all the EHR problems encountered daily across the country, the only one to hit the major news outlets was a non-story about a missed Ebola diagnosis in Dallas, Texas. Before being retracted, the hospital’s claim of an Epic failure launched a slew of commentary in the health IT field. These swirled through my head last night as I tried to find a lesson in the incident.

The facts seem to be as follows. A 42-year-old man named Thomas Eric Duncan arrived from Liberia and checked in to the emergency room at Texas Health Presbyterian Hospital Dallas complaining of symptoms consistent with an Ebola diagnosis. He told the admitting nurse he had come from Liberia, and the nurse entered the data into the Epic EHR.

The purpose of recording the patient’s travel history, however, seemed to be simply to determine the need for immunizations, so the EHR kept it within a nurse’s section of the data (which the hospital called a “workflow”) and did not display it to the doctor. The doctor sent Duncan home, where he came into contact with about 100 people who were potentially infected. His symptoms worsened and he returned to the hospital two days later, where he was finally diagnosed correctly and admitted.

Late night musing #1: If Texas Health Presbyterian Hospital Dallas can’t diagnose a case of Ebola, why do they think they can treat one? The hospital has won numerous awards, including one for patient safety–I guess you’re safe once you’re admitted.

Meanwhile, the city of Dallas waited several extra days to clean up infected sheets and other belongings from the Duncan home. In Africa, such detritis are recognized as a major source of new Ebola infections.

Late night musing #2: Does this reflect the competence of public health officials in this country? Maybe we should turn the job over to the Secret Service.

It’s really a shame that the national press jumped on the hospital’s announcement that the EHR was the source of the problem. Commenters criticized the hospital right away, asking why the nurse didn’t simply tell the doctor, and why the doctor didn’t ask on his own.

Finally, the hospital backed off from blaming Epic, thus making the hospital look even stupider and more guilty than it already appeared. Nevertheless, EHRs at some hospitals may be designed to flag warning signals.

Clearly, there are many layers to this health care failure. I don’t blame the nurse, or even the doctor. ERs are always busy, and the nurse might never have known who would see the patient or even be in the ER when the doctor finally saw him.

But I do find a small lesson in the brief appearance of the EHR as a pivotal character in the story. The nurse thought he or she was doing their job just by entering the data into the EHR, and the doctor thought he was doing his job by reading it. The EHR had loomed as a magical solution to health care workflow–in the minds of hospital administrators, if not the ER staff.

Maybe if the nurse knew that the travel history was for the purpose of immunizations, he or she would not have relied on the EHR to use that information for diagnosis. Besides showing the need for training, some of my colleagues suggest that this problem calls for FDA regulation of EHR interfaces. They also suggest that systems use good user interface design to highlight important information (which would require a definition of what’s “important”) or at least allow searches for critical elements of the record.

Late night musing #3: Behind this also lies the mindlessness of much data collected by EHRs. I’m sure the nurse knew whether the unfortunate Mr. Duncan was a smoker and whether he suffered from depression, because regulations require these things to be recorded. Travel history became just another one of these automatic requirements to be tossed into the EHR and forgotten.

My story also concerns the musings of other health IT commentators, who suggested that EHRs be better integrated into “workflows”–as if every clinician follows a mechanical path of treatment and the EHR can figure out what it is.

Another thoughtful posting calls for integrating infectious diseaess into clinical decision support. But as my colleague Sandra Raup (R.D., J.D., M.P.H.) points out, CDS depends on a long history of clinical data collection. One can’t instantly add a new disease.

It might have been useful for some international health organization to realize, when the Ebola outbreak began to spread, that it would eventually break out of central Africa, and then to provide an app to hospitals around the world for checking symptoms and travel history. There is certainly a creative role for health IT to play.

I think the messiness of the Texas Health Presbyterian Hospital Dallas story shows why EHR failures, numerous as they are, don’t get reported in the press. There are just too many complicating factors. The EHR is partly configured by the clinic’s staff, who thereby become responsible for some of its decisions. The EHR failure usually comes when the staff is under stress, when they have communication problems, when the patient’s condition is rare. Ascribing blame becomes a tangled mess; one must start designing systems with multiple, redundant points to catch failures that can fall through the cracks.

So one level, this is just another sad story of humanity’s tendency to trust too much in its technology, a story that ranges from the flight of Icarus to the sail of the Titanic and the failure of the Fukushima Daiichi nuclear power plant. On other, it’s a familiar story of a systemic problem leading to what’s sometimes called a “normal failure.” Not much new to learn, but lots of work to do. Clinicians have to evaluate EHRs and know how the data is used, a more open system in all directions.

Where is Clinical Decision Support Heading?

Posted on April 7, 2014 I Written By

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

A few months ago I had a chance to sit down an interview Jonathan Teich, MD, PhD, Elsevier’s Chief Medical Informatics Officer and a physician at Brigham and Women’s Hospital in Boston. In our discussion we dig into the current and future state of clinical decision support. For example, I ask Dr. Teich if you’ll be able to be a doctor in the future without it. If you want to learn more about clinical decision support and where it’s going, you’ll enjoy this video interview:

101 Tips to Make Your EMR and EHR More Useful – EHR Tips 26-30

Posted on October 28, 2011 I Written By

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

Time for the next entry covering Shawn Riley’s list of 101 Tips to Make your EMR and EHR More Useful. I met someone at a conference who commented that they liked this series of posts. I hope you’re all enjoying the series as well.

30. Remember that the EMR is only part of the safety problem
Remember that the EMR is just a tool. How you use that tool still matters. How you manage that tool matters. How you implement that tool matters. Safety is a result of great processes and that doesn’t change when you implement an EMR. In fact, I’d say it’s even more important. The same applies to bad clinical workflows. EMR won’t solve those bad workflows either. You can try to do a redesign of the workflows with the EMR implementation, but that often doesn’t go over well.

29. Errors should be easily reportable
To be honest, I’m not sure exactly which errors Shawn is talking about. I think I’ll take a different spin on it than what he intended and talk about the errors or issues that someone has using an EMR. This is particularly important when you first implement an EMR. You should want to know the errors that are occurring regularly so you can fix them. Make it easy for them to report them and provide proper encouragement and/or rewards for reporting errors they have with the system. Ignorance is not bliss…it always catches up to you eventually.

28. Use data to show both individual and system safety metrics
The key component that Shawn is describing here is the ability to report on various cross sections of data (individual vs system). If you can’t chop up your data to really know what’s going on in your system, then you’re not going to be able to really pinpoint the issues that users are having. Maybe it’s only one person who’s bringing down the average for the entire hospital. You don’t want to make sweeping changes to the system that annoy the majority of users when all you really needed to do was address the issues of an individual or small group of individuals.

27. Record management in the EMR is just as important as in paper
You thought HIM was done when you got the EMR. Wrong! Their role is still very important. Granted, it changes pretty dramatically, but in the clinics I’ve worked in the records management people were able to do a much more effective job improving the patient record in the EMR. Many of the things they did they never had time to do cause they were too busy pulling and filing paper charts.

26. Evaluate decision support tools for a fit to your needs
I believe that the clinical decision support tools are going to be the thing that changes the most over the next 5-10 years. You should definitely see how the clinical decision support tools they have available fit into your environment, but also spend as much time seeing what they’ve implemented and what their road map and method of implementing new clinical decision support tools is so you know where they’re going to be with their tools and product in five years.

If you want to see my analysis of the other 101 EMR and EHR tips, I’ll be updating this page with my 101 EMR and EHR tips analysis. So, click on that link to see the other EMR tips.