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What is Quality in Health Care? (Part 2 of 2)

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

The first part of this article described different approaches to quality–and in fact to different qualities. In this part, I’ll look at the problems with quality measures, and at emerging solutions.

Difficulties of assessing quality

The Methods chapter of a book from the National Center for Biotechnology Information at NIH lays out many of the hurdles that researchers and providers face when judging the quality of clinical care. I’ll summarize a few of the points from the Methods chapter here, but the chapter is well worth a read. The review showed how hard it is to measure accurately many of the things we’d like to know about.

For instance, if variations within a hospital approach (or exceed) the variations between hospitals, there is little benefit to comparing hospitals using that measure. If the same physician gets wildly different scores from year to year, the validity of the measure is suspect. When care is given by multiple doctors and care teams, it is unjust to ascribe the outcome to patient’s principal caretaker. If random variations outweigh everything, the measure is of no use at all. One must also keep in mind practical considerations, such as making sure the process of collecting data would not cost too much.

Many measures apply to a narrow range of patients (for instance, those with pneumonia) and therefore may be skewed for doctors with a relatively small sample of those patients. And a severe winter could elevate mortality from pneumonia, particularly if patients have trouble getting adequate shelter and heat. In general, “For most outcomes, the impacts of random variation and patient factors beyond providers’ control often overwhelm differences attributable to provider quality.” ACMQ quality measures “most likely cannot definitively distinguish poor quality providers from high quality providers, but rather may illuminate potential quality problems for consideration of further investigation.”

The chapter helps explain why many researchers fall back on standard of care. Providers don’t trust outcome-based measures because of random variations and factors beyond their control, including poverty and other demographics. It’s hard even to know what contributed to a death, because in the final months it may not have been feasible to complete the diagnoses of a patient. Thus, doctors prefer “process measures.”

Among the criteria for evaluating quality indicators we see, “Does the indicator capture an aspect of quality that is widely regarded as important?” and more subtly, “subject to provider or public health system control?” The latter criterion heed physicians who say, “We don’t want to be blamed for bad habits or other reasons for noncompliance on the part of our patients, or for environmental factors such as poverty that resist quick fixes.”

The book’s authors are certainly aware of the bias created by gaming the reimbursement system: “systematic biases in documentation and coding practices introduced by awareness that risk-adjustment and reimbursement are related to the presence of particular complications.” The paper points out that diagnosis data is more trustworthy when it is informed by clinical information, not just billing information.

One of the most sensitive–and important–factors in quality assessment is risk adjustment, which means recognizing which patients have extra problems making their care more difficult and their recovery less certain. I have heard elsewhere the claim that CMS doesn’t cut physicians enough slack when they take on more risky patients. Although CMS tries to take poverty into account, hospital administrators suspect that institutions serving low-income populations–and safety-net hospitals in particular–are penalized for doing so.

Risk adjustment criteria are sometimes unpublished. But the most perverse distortion in the quality system comes when hospitals fail to distinguish iatrogenic complications (those introduced by medical intervention, such as infections incurred in the hospital) from the original diseases that the patient brought. CMS recognizes this risk in efforts such as penalties for hospital-acquired conditions. Unless these are flagged correctly, hospitals can end up being rewarded for treating sicker patients–patients that they themselves made sicker.

Distinguishing layers of quality

Theresa Cullen,associate director of the Regenstrief Institute’s Global Health Informatics Program, suggests that we think of quality measures as a stack, like those offered by software platforms:

  1. The bottom of the stack might simply measure whether a patient receive the proper treatment for a diagnosed condition. For instance, is the hemoglobin A1C of each diabetic patient taken regularly?

  2. The next step up is to measure the progress of the first measure. How many patients’ A1C was under control for their stage of the disease?

  3. Next we can move to measuring outcomes: improvements in diabetic status, for instance, or prevention of complications from diabetes

  4. Finally, we can look at the quality of the patient’s life–quality-adjusted life years.

Ultimately, to judge whether a quality measure is valid, one has to compare it to some other quality measure that is supposedly trustworthy. We are still searching for measures that we can rely on to prove quality–and as I have already indicated, there may be too many different “qualities” to find ironclad measures. McCallum offers the optimistic view that the US is just beginning to collect the outcomes data that will hopefully give us robust quality measures, Patient ratings serve as a proxy in the interim.

When organizations claim to use quality measures for accountable care, ratings, or other purposes, they should have their eyes open about the validity of the validation measures, and how applicable they are. Better data collection and analysis over time should allow more refined and useful quality measures. We can celebrate each advance in the choices we have for measures and their meanings.

What is Quality in Health Care? (Part 1 of 2)

Posted on February 9, 2016 I Written By

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

Assessing the quality of medical care is one of the biggest analytical challenges in health today. Every patient expects–and deserves–treatment that meets the highest standards. Moreover, it is hard to find an aspect of health care reform that does not depend on accurate quality measurement. Without a firm basis for assessing quality, how can the government pay Accountable Care Organizations properly? How can consumer choice (the great hope of many reformers) become viable? How can hospitals and larger bodies of researchers become “learning health systems” and implement continuous improvement?

Ensuring quality, of course, is crucial in a fee-for-value system to ensure that physicians don’t cut costs just by withholding necessary care. But a lot of people worry that quality-based reimbursement plans won’t work. As this article will show, determining what works and who is performing well are daunting tasks.

A recent op-ed claims that quality measures are adding unacceptable stress to doctors, that the metrics don’t make a difference to ultimate outcomes, that the variability of individual patients can’t be reflected in the measures, that the assessments don’t take external factors adequately into account, and that the essential element of quality is unmeasurable.

Precision medicine may eventually allow us to tailor treatments to individual patients with unique genetic prints. But in the meantime, we’re guessing a lot of the time we prescribe drugs.

The term quality originally just distinguished things of different kinds, like the Latin word qualis from which it is derived. So there are innumerable different qualities (as in “The quality of mercy is not strained”). It took a while for quality to be seen as a single continuum, as in an NIH book I’ll cite later, which reduces all quality measures to a single number by weighting different measures and combining them. Given the lack of precision in individual measures and the subjective definitions of quality, it may be a fool’s quest to seek a single definition of quality in health care.

Many qualities in play
Some of the ways to measure quality and outcomes include:

  • Longitudinal research: this tracks a group of patients over many years, like the famous Framingham Heart Study that changed medical care. Modern “big data” research carries on this tradition, using data about patients in the field to supplement or validate conventional clinical research. In theory, direct measurement is the most reliable source of data about what works in public health and treatment. Obvious drawbacks include:

    • the time such studies take to produce reliable results

    • the large numbers of participants needed (although technology makes it more feasible to contact and monitor subjects)

    • the risk that unknown variations in populations will produce invalid results

    • inaccuracies introduced by the devices used to gather patient information

  • Standard of care: this is rooted in clinical research, which in turn tries to ensure rigor through double-blind randomized trials. Clinical trials, although the gold standard in research, are hampered by numerous problems of their own, which I have explored in another article. Reproducibility is currently being challenged in health care, as in many other areas of science.

  • Patient ratings: these are among the least meaningful quality indicators, as I recently explored. Patients can offer valuable insights into doctor/patient interactions and other subjective elements of their experience moving through the health care system–insights to which I paid homage in another article–but they can’t dissect the elements of quality care that went into producing their particular outcome, which in any case may require months or years to find out. Although the patient’s experience determines her perception of quality, it does not necessarily reflect the overall quality of care. The most dangerous aspect of patient ratings, as Health IT business consultant Janice McCallum points out, comes when patients’ views of quality depart from best practices. Many patients are looking for a quick fix, whether through pain-killers, antibiotics, or psychotropic medications, when other interventions are called for on the basis of both cost and outcome. So the popularity of ratings among patients just underscores how little we actually know about clinical quality.

Quality measures by organizations such as the American College of Medical Quality (ACMQ) and National Committee for Quality Assurance (NCQA) depend on a combination of the factors just listed. I’ll look more closely at these in the next part of this article.

Meaningful Use Holdover Could Be Good News For Healthcare

Posted on January 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.

I know all of us are a flutter about the pending regulatory changes which will phase out Meaningful Use as we know it. And yes, without a doubt, the changes underway will have an impact that extends well beyond the HIT world. But while big shifts are underway in federal incentives programs, it’s worth noting that it could be a while before these changes actually fall into place.

As readers may know, the healthcare industry will be transitioning to working under value-based payment under the Medicare Access and CHIP Reauthorization Act, which passed last year. But as ONC’s Karen DeSalvo noted last week, the transition could take a while In fact, proposed draft regulations for MACRA rollout will be released this spring for public comment. When you toss in the time needed for those comments to be submitted, and for the feds to digest those comments and respond, my guess is that MACRA regs won’t go live until late this year at the earliest.

The truth is, this is probably a very good thing. While I don’t have to tell you folks that everyone and their cousin has a Meaningful Use gripe, the truth is that the industry has largely adapted to the MU mindset. Maybe Meaningful Use Stage 3 wouldn’t have provided a lot of jollies, but on the whole, arguably, most providers have come to terms with the level of process documentation required — and have bought their big-bucks EMRs, committing once and for all to the use of digital health records.

Value-based payment, on the other hand, is another thing entirely. From what I’ve read and researched to date, few health organizations have really sunk their teeth into VBP, though many are dabbling. When MACRA regs finally combine the Physician Quality Reporting System, the Value-based Payment Modifier and the Medicare EHR incentive program into a single entity, providers will face some serious new challenges.

Sure, on the surface the idea of providers being paid for the quality and efficiency they deliver sounds good. Rather than using a strict set of performance measures as proxies for quality, the new MACRA-based programs will focus on a mix of quality, resource use and clinical practice use measures, along with measuring meaningful use of certified EHR technology. Under these terms, health systems could conceivably enjoy both greater freedom and better payoffs.

However, given health systems’ experiences to date, particularly with ACOs, I’m skeptical that they’ll be able to pick up the ball and run with the new incentives off the bat. For example, health systems have been abandoning CMS’s value-based Pioneer ACO model at a brisk clip, after finding it financially unworkable. One recent case comes from Dartmouth-Hitchcock Medical Center, which dropped out of the program in October of last year after losing more than $3 million over the previous two years.

I’m not suggesting that health systems can afford to ignore VBP models, or that sticking to MU incentives as previously structured would make sense. But if the process of implementing MACRA gives the industry a chance to do more preparing for value-based payment, it’s probably a good thing.

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://radar.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.

We’re Just Getting Started with an Internet of Healthy Things (Part 2 of 3)

Posted on November 25, 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://radar.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 described the dire condition of health care today. So where does Kvedar’s book, The Internet of Healthy Things,fit into all this? It encapsulates all those years of learning at his Center for Connected Health, set up by the Boston-area giant Partners HealthCare and now renamed Partners Connected Health. From these insights, the book pinpoints the areas where innovators can make headway. He shows the gap between how we approach chronic health conditions now–even among the companies experimenting with mobile health and patient engagement–and the ideal for which the Partners Connected Health is striving. In reviewing his suggestions, I’ll try also to shine lights into passageways he did not explore.

Lessons from the field

Kvedar divides the evolution of connected health into three broad phases. Most companies are now in the first phase of simply reporting statistics back to patients and doctors. You can find out from a mobile app what your blood sugar is, and from your fitness bracelet how far you’ve walked during the day. This phase can have some benefits on athletes and the small set of Quantified Selfers who love data, but has absolutely no appeal for the vast mass of people who most need support.

Partners Connected Health has entered the second phase and has its own data to show the great strides it has made. In this phase, you engage the patients by connecting him to his providers, family, and friends, making him feel watched (the Sentinel Effect) and therefore extracting healthier behavior. This starts to achieve the changes we want, but is still limited in the people we reach.

The third stage is to fit the intervention directly to the lifestyle and needs of the individual, a process Kvedar calls “hyperpersonalization.” If walking your dog is an important part of your life, the system should feed you messages encouraging you to do things that improve your endurance and walking ability. If you want to fit into smaller clothing for an upcoming wedding, focus on everything that can get your waistline down.

Kvedar’s vision does not seem to be the automated-intelligence utopia laid out by Vinod Khosla and others, where patients get automated diagnoses and treatment recommendations from the “cloud” and avoid physicians for most ailments. Rather, technology for Kvedar supports a strong relationship between patient and clinician. At the same time, the technology extends the clinician’s reach–and allows her to treat many more people with greater effectiveness–by bringing the treatment plan into the patient’s everyday life, throughout the day.

The first chapter of the book lays out a fantasy scenario for an automated coach that follows the individual around and sends messages right before he reaches for a cookie or is about to stay up too late at night. Kvedar unveiled the same scenario, which was quite amusing, in his introduction to the Connected Health conference. I covered the major aspects of this hyperpersonalization–automated, contextual, motivational, empowering, and incentivizing–in another article. It has to be done very careful in order not to appear intrusive and annoying, but it offers a greater promise to change behavior than anything else we know.

I already see one difficulty with organizations aiming at this vision of health care. Kvedar talks a great deal about apps–the little agents you download from the Apple Store or Google Play. But hyperpersonalization is not an app. It’s a whole environment for dealing with personal lifestyle–aided by apps, to be sure, but requiring a deep investigation into the patients’ needs and interests. What Kvedar is really calling for is not a prize-winning app, but a reconfiguration of our health system.

In the face of such a challenge, several organizations are stepping up. Among their ranks are scattered a few traditional health care organizations (providers such as Kaiser Permanente and Kvedar’s own Partners HealthCare, insurers such as Aetna) but most come from the outside. Kvedar concentrates on the clinics and wellness programs set up by Walgreens pharmacy. Their integration of convenience and support for ongoing behavior change is much more thorough than most people realize.

Another example of an integrated strategy is provided by a single teenager whose caretakers are monitoring his diabetes remotely. The process brings the teen’s doctor and mother into the picture with technologies that include an unusual skin sensor, Apple HealthKit, and an Epic health record. The solution is not an open one.

It’s great for Walgreen’s to fix sore throats and minor cuts, and even to start offering primary care. But people with serious health needs will eventually need to interact with a traditional clinic or hospital. If these institutions still can’t accept data from the urgent health clinic (some already can), the same old inefficiencies and errors will re-emerge. And this failure to evolve with the times is a danger even though, as Dr. Kvedar repeatedly warns, it threatens the continued existence of the traditional hospitals.

The final section of this article will look at the gap between where we are now and where The Internet of Healthy Things would like us to be.

How Complicated Is It to Simplify Medication Adherence?

Posted on November 17, 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://radar.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 things that irrationally inflate health costs, one of the top concerns is people who just don’t take their prescribed medications. Medication adherence doesn’t sound like a high-tech issue, but a lot of interesting technology is being thrown at the problem.

One pharmacist (obviously harboring an interest in increasing orders) estimated that we’d save 290 billion dollars a year if everybody took the medications prescribed for them. But don’t dismiss their claim as self-serving–the Centers for Disease Control suggests they may be right. It also says that half of all medications are discontinued too early. As the “fee for value” movement starts extending to the performance of medications, concerns that patients actually follow through on prescriptions will increase.

At the recent Connected Health Conference I talked to several companies taking on the difficult adherence problem from different angles. Medisafe aids patients in self-monitoring, Insightfil creates convenient packaging that groups pills the ways patients take them, and Dose doles out medication at prescribed times.

Medisafe is one of a wave of firms that address medication adherence, representing an advance over jotting down daily practices in a paper journal. These services share a good deal in common with other solutions in the marketplace that carry out patient monitoring, care planning, and the patient-centered medical home. In all these areas, services boast of tracking behavior, providing feedback to both patients and clinicians, promoting communication, and similar aspects of the connected health vision.

Medisafe handles patients’ nonadherence in multiple ways, including importing the patient’s medication list, along with vital signs such as blood pressure. Visualizations help both the patient and the doctor see the relationship between taking medication and the relevant vital signs. Patients can manage their doctor office visits or when they have been assigned a change in medication, and monitor the effects of such events on adherence through Medisafe. Finally, doctors will be able to compare data on patients within their practices, grouping them by condition, by medication taken, by demographics, or by behavior traits.

Other medication solutions try to reduce the burden of compliance that falls on the patient–or to look at it in another way, reduce the patient’s discretion. At something of an extreme, Proteus inserts a tiny radio device into each pill and makes the patient wear a patch that can detect the presence of the pill in the body. People have suggested one or two use cases for this intrusive system (for instance, during a drug trial, to guarantee accuracy) but in general, treating patients like criminals doesn’t encourage healthy behavior.

A lot of people, especially the elderly and those with the most severe medical conditions, need so many pills and capsules that it’s hard to remember which ones to take, and when. I’ve seen relatives loading little pillboxes every Sunday morning with the pills for the upcoming week.

Insightfil hopes to take all the manual labor, and consequent chances for error, out of this process. It ships each person a customized blister pack with a week’s worth of medications, offering up to four compartments per day to cover different times. This may seem like a simple problem, but it’s actually a major logistical feat.

First, according to founder and CEO Ted Acworth, his company had to develop a robot that could recognize different pills and accurately load them into the blister packs. Then they had to find a pharmacy with nationwide reach and room in its warehouse for the robot.

Dose solves the problem a different way, through a dispenser into which a patient or caregiver can pour bottles of pills. The dispenser, which has been configured to know the patient’s medication regimen, can automatically separate the pills and release them at the right time.

Once the pills are in the box, control can be removed from the patient. This can be important for doling out opiates or other drugs that can be dangerous or that patients have a tendency to abuse.

Dose’s dispenser is a very smart machine, supporting some of other goals of connected health I mentioned. Clinicians, caregivers, and patients can get alerts about doses taken or missed. The device has bi-directional programming capabilities with a web portal and mobile app, and clinicians can change regimens over the Internet. Biometric devices can be attached to let users map medication adherence to vital signs, or to report a user’s exercise and eating habits. The device’s forward facing camera can be used for scanning the barcode of a pill bottle, as well as for video consultations with a clinician. Along with these features, the device is integrated with an FDA Drug Database and therefore an accurate drug list, along with information about potential drug interactions is readily available.

On many levels, then, advanced technology can help patients with the apparently simple problem of opening a bottle at the right time and popping a pill in their mouths. This article has been a limited look at the problem–I haven’t dealt with over-prescription or side effects, but just the question of how to get patients to take the drugs that are understood to improve their health. We’ll see over time which of these solutions–perhaps all of them at different times–can help of hundreds of millions who regularly take prescription drugs.

Combating Physician Burnout – Let’s Stop Treating Physicians Like Factory Workers

Posted on July 1, 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.

Have you ever seen the Love Medicine Again website? I hadn’t either until yesterday, but it says something that someone would need to create a website like that in the first place. Many doctors are quite unhappy with their life as a doctor and where medicine is heading. I actually came across this website thanks to their article titled “5 Ways to Use Creativity to Combat Physician Burnout.” Here’s the 5 suggestions they make to put creativity to work to reduce physician burnout:

  1. Allow your staff to help you with your schedule
  2. Voice your concerns about something that isn’t working to your colleagues
  3. Give credit where credit is due
  4. Engage your patients to help you help them
  5. Think outside of the box about how to bring improved satisfaction to your job

I wasn’t that impressed with the list. It seems to gloss over the core of what’s burning physicians out. I do love the idea of providing opportunities for physicians to be creative. That has definitely been pushed to the side in many offices.

One of the most common complaints I hear from doctors is “Stop making me be a data entry clerk.” I think this was largely true in the paper world, but it has been made even more so in the world of EHR thanks to things like meaningful use that require a lot of hoop jumping and box clicking to comply. We could certainly do more to make the data entry work easier, but that still just masks the problems.

I think that doctors saying they don’t want to be a data entry clerk is really a proxy for “I want to be creative and thoughtful in how I approach medicine. I don’t have time to be creative.”

The 15 minute (or less) per appointment hits this same pain point. Doctors want to get paid and so they feel the economic reality is that they need to see as many patients as possible. This economic reality leaves no time for doctors to be creative. I think they feel more like factory workers than highly educated leaders.

If we want to solve the physician burnout problem we need to find ways to tap into a physician’s creativity. We need to free them up for churning out patients so they can leverage all their education and experience to solve the larger problems of healthcare. It’s not very often that overworked factory workers are able to solve massive problems. They’re too busy working to think about the larger context. We need to stop treating our doctors like factory workers to solve healthcare’s larger problems.

Do We Want a Relationship With Our Doctor?

Posted on June 22, 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.

As is often the case, this weekend I was browsing Twitter. Many of the people and hashtags I follow are healthcare and health IT related. Many of the tweets related to the need to change the healthcare system. You know the usual themes: We pay too much for healthcare. We deserve better quality healthcare. We need to change the current healthcare system to be focused on the patient. Etc etc etc.

This wave of tweets ended with one that said “It’s all about the relationships.” I actually think the tweet had more to do with how a company was run, but in the beautiful world of Twitter you get to mesh ideas from multiple disciplines in the same Twitter stream (assuming you follow a good mix of people). I took the tweet and asked the question, “Do We Want a Relationship With Our Doctor?

If you’d asked me a year ago, I would have said, no! Why would I want a relationship with my doctor? I don’t want any relationship with my doctor, because that means that I’m sick and need him to fix something that’s wrong with me. I hope to never see my doctor. Doctor = Bad. Don’t even get me started with hospitals. If Doctor = Bad then Hospital > Doctor.

I’m personally still battling through a change in mindset. It’s not an easy change. It’s really hard to change culture. We have a hard core culture in America of healthcare being sick care. We all want to be healthy, but none of us want to be sick. Going to the doctor admits that we are sick and we don’t want anything to do with that. If we have an actual relationship with our doctor, then we must be really sick.

From the other perspective, do doctors want relationships with their patients? I’ve met some really jaded doctors who probably don’t, but most of the doctors I’ve met would love an actual, deep relationship with their patients. However, they all are asking the question, “How?” They still have to pay the bills, pay off their debts, etc. I don’t know many doctors who have reconciled these practical needs with the desire to have a relationship with their patients.

The closest I’ve seen is the direct primary care and concierge models. It’s still not clear to me that these options will scale across healthcare. Plus, what’s the solution for specialists? Will ACOs and Value Based Reimbursement get us there. I hear a lot of talk in this regard which scares me. Lots of talk without a clear path to results really scares me in healthcare.

What do you think? Do you want a relationship with your doctor? Do doctors want a relationship with their patients? What’s the path to making this a practical reality? Are you already practicing medicine where you have a deep, meaningful relationship with your patient? We’d love to hear your experience.

Patients Can Squawk, But We Have Little To Crow About Open Data

Posted on June 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://radar.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.

One of the biggest disappointments at this year’s Health Datapalooza (which I found disappointing overall) was the continued impasse presented to patients who, bolstered by the best thinking in health care as well as Federal laws and regulations, ask for health data stored about them by doctors and other institutions.

Activists such as Regina Holliday and e-Patient Dave proved years ago that giving patients information and involving them in decisions will save lives. The Society for Participatory Medicine enshrines the principle. But the best witnesses for patient empowerment are the thousands of anonymous patients, spouses, parents, and children quietly trundling folders with their own records through the halls of hospitals, building up more knowledge of their chronic conditions than any professional clinician possesses, and calmly but inflexibly insisting on being equal partners with those who treat them.

There were plenty of high-minded words at the Datapalooza about patient rights to data. It was recognized as a key element of patient empowerment (or “activation,” as the more timid speakers liked to say) as well as an aid to better care. An online petition backed by an impressive array of health reformers is collecting signatures (whom someone will presumably look at) and encourages activists to speak up about this topic on July 4. HHS announced that anyone denied access to data to which the law gives her a right can submit an informal report to noinformationblocking@cms.hhs.gov.

Although occasional mention was made of personal health records (PHRs), most of the constant discussion about interoperability stayed on the safe topic of provider-to-provider data exchange. Keeping data with health care providers leads to all sorts of contorted practices. For instance, patient matching and obtaining consent are some of the most difficult challenges facing health IT in the U.S., all caused by keeping data with providers instead of the patients themselves.

The industry’s slowness to appreciate patient-generated data is also frustrating. Certainly, the health IT field needs to do a lot more to prepare data for use: consumer device manufacturers must assure clinicians of the devices’ accuracy, and researchers need to provide useful analytics that clinicians can plug in to their electronic systems. Still, doctors are demonstrating a disappointing lack of creativity in the face of this revolutionary source of information. It’s all to easy to carp about accuracy (after all, lab tests have limited accuracy as well) or just to state that you don’t know what to do with the data.

I heard about recent progress at the UK’s National Health Service from Brian Ahier, who is the only person I know who can explain the nuances of extensions to FHIR resources while actively using both his laptop and his cell phone at the same time. Ahier heard at a UK-US Bootcamp before the Datapalooza that the NHS has given 97% of its patients access to their records.

But there’s a bit of a caution around that statistic: only one-fifth of the patients have taken advantage of this right. This doesn’t bother me. First of all, one-fifth of the population with access to their personal records would be a dizzying accomplishment for most countries, including the U.S. Second, few people need access to records until some major problem arises, such as the need to see a specialist. They probably feel relieved to know the records will be there when needed.

Another aspect of patient control over data is research. The standard researcher-centered model is seen as increasingly paternalistic, driving patients away. They’re not impressed with being told that some study will benefit people like them–they want to tell researchers what really matters to them as sufferers, and hear more about the study as it goes along. Researchers are frantic to reverse a situation where most studies fail simply because they can’t sign up enough subjects.

The Patient-Centered Outcomes Research Institute (PCORI) is one of the progressive institutions in health care who understand that giving patients more of a say will be increasingly important for signing up patients in the first place, as well doing research of value to them. Its PCORnet combines traditional research databases with databases maintained by patient advocacy groups. Each member network can create its own policies for getting consent, which allows researchers to bend with the needs of their research subjects.

OpenClinica, the open source clinical research platform, just announced the release of an app that may contribute to the goals of taking input from patients and binding them closer to the research endeavor.

Public health officials also recognize the sensibilities of the people they monitor. At a panel on data about low-income people, speakers stressed the importance of collecting data in a respectful way that doesn’t make people feel they’re being spied on or could be punished for their behavior.

Let’s talk a minute about health care costs, if only because doctors and insurers don’t want to. (Some doctors are prohibited by their employers from telling patients how much a recommended procedure will cost, supposedly because they don’t want costs to intrude on what should ideally be a clinical decision. This is changing with the increase in deductibles, but often the doctors don’t even know what the final cost will be after insurance.)

One app so admired by the Datapalooza team that they allowed the company to demonstrate its product on the main stage during keynote time was Sensentia. This product everybody is so impressed with takes in information from health plans to allow patients as well as the staff at health care providers to quickly find the health plan benefits for a procedure. (I recently covered another company doing similar work with insurance and costs.)

Sensentia is a neat product, I am willing to aver. It accepts natural language queries, crunches the data about health plans and insurers, and returns the actual health plan benefits for a treatment. Of course, I know the cost of flying from Boston to San Francisco after six clicks in my browser, even though the calculations that go into offering me a price are at least as complicated as those run by health plans. One may be shocked to hear that that current phone calls to an insurer cost $3-$10. This is the state of health care–it costs more than five bucks on average for a doctor just to find out how much it will cost to offer his own service.

A panel on patient-generated data reported more barriers than successes in getting doctors to work with data from patient devices and reports from everyday life. Another panel about improving quality measures culminated in the moderator admitting that more patients use Yelp than anything else to choose providers–and that it works pretty well for them.

For me that was the conference’s low point, and a moment of despairing cynicism that doesn’t reflect the mood of the conference or the health care field as a whole. Truly, if Yelp could solve our quality problems, we wouldn’t need a Datapalooza or the richness of data analysis it highlights. But I think reformers need more strategies to leap the hurdles we’re facing and implement the vision we all share.

Early Warnings Demonstrate an Early Advance in the Use of Analytics to Improve Health Care

Posted on May 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://radar.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.

Early warning systems–such as the popular Modified Early Warning System (MEWS) used in many hospitals–form one of the first waves in the ocean of analytics we need to wash over our health care system. Eventually, health care will elegantly integrate medical device output, electronic patient records, research findings, software algorithms, and–yes, let us not forget–the clinician’s expertise in a timely intervention into patient care. Because early warning systems are more mature than many of the analytics that researchers are currently trying out, it’s useful to look at advances in early warning to see trends that can benefit the rest of health care as well.

I talked this week to Susan Niemeier, Chief Nursing Officer at CapsuleTech, a provider of medical device integration solutions. They sell (among other things) a bedside mobile clinical computer called the Neuron that collects, displays, and sends to the electronic medical record vital signs from medical devices: temperature, pulse, respiration, pulse oximetry, and so on. A recent enhancement called the Early Warning Scoring System (EWSS) adds an extra level of analytics that, according to Niemeier, can identify subtle signs of patient deterioration well before a critical event. It’s part of Capsule’s overarching aim to enable hospitals to do more with the massive amount of data generated by devices.

For more than 18 years, CapsuleTech provided bedside medical device connectivity products and services that captured patient vital signs and communicated that data to the hospital EMR. Rudimentary as this functionality may appear to people using automated systems in other industries, it was a welcome advance for nurses and doctors in hospitals. Formerly, according to Niemeier, nurses would scribble down on a scrap of paper or a napkin the vital signs they saw on the monitors. It might be a few hours before they could enter these into the record–and lots could go wrong in that time. Furthermore, the record was a simple repository, with no software observing trends or drawing conclusions.

Neuron 2 running Early Warning Scoring System

Neuron 2 running Early Warning Scoring System

So in addition to relieving the nurse of clerical work (along with likely errors that it entails), and enhancing workflow, the Neuron could make sure the record immediately reflected vital signs. Now the Neuron performs an even more important function: it can run a kind of clinical support to warn of patients whose conditions are deteriorating.

The Neuron EWSS application assigns a numerical score to each vital sign parameter. The total early warning score is then calculated on the basis of the algorithm implemented. The higher the score, the greater the likelihood of deterioration. The score is displayed on the Neuron along with actionable steps for immediate intervention. These might include more monitoring, or even calling the rapid response team right away.

The software algorithm is configured in a secure management tool accessible through a web browser and sent wirelessly to the Neuron at a scheduled time. The management tool is password protected and administered by a trained designee at the hospital, allowing for greater flexibility and complete ownership of the solution.

Naturally, the key to making this simple system effective is to choose the right algorithm for combining vital signs. The United Kingdom is out in front in this area. They developed a variety of algorithms in the late 1990s, whereas US hospitals started doing so only 5 years ago. The US cannot simply adopt the UK algorithms, though, because our care delivery and nursing model is different. Furthermore, each hospital has different patient demographics, priorities, and practices.

On the other hand, according to Niemeier, assigning different algorithms to different patients (young gun-shot victims versus elderly cardiac patients, for instance) would be impractical because mobile Neuron computers are used across the entire hospital facility. If you tune an algorithm for one patient demographic, a nurse might inadvertently use it on a different kind of patient as the computer moves from unit to unit. Better, then, to create a single algorithm that does its best to reflect the average patient. The algorithm should use vital signs and observations that are consistently collected, not vitals that are intermittently measured and documented.

Furthermore, algorithms can be tuned over time. Not only do patient populations evolve, but hospitals can learn from the data they collect. CapsuleTech advises a retrospective chart review of rapid response events prior to selecting an algorithm. What vital signs did the patient have during the eight hours before the urgent event? Retrospectively apply the EWSS to the vital signs to determine the right algorithm and trends in that data to recognize deterioration earlier.

Without help such as the Early Warning Scoring System, rapid response teams have to be called when a clear crisis emerges or when a nurse’s intuition suggests they are needed. Now the nurse can check his intuition against the number generated by the system.

I think clinicians are open to the value of analytics in early warning systems because they dramatically heighten chances for avoiding disaster (and the resulting expense). The successes in early warning systems give us a glimpse of what data can do for more mundane aspects of health care as well. Naturally, effective use of data takes a lot more research: we need to know the best ways to collect the data, what standards allow us to aggregate it, and ultimately what the data can tell us. Advances in this research, along with rich new data sources, can put information at the center of medicine.