Free EMR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to EMR and EHR for FREE!

Why We Store Data in an EHR

Posted on April 27, 2016 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.

Shereese Maynard offered this interesting stat about the data inside an EHR and how that data is used.


I then made up this statistic which isn’t validated, but I believe is directionally accurate:


Colin Hung then validated my tweet with his comment:

It’s a tricky world we live in, but the above discussion is not surprising. EHRs were created to make an office more efficient (many have largely failed at that goal) and to help a practice bill at the highest level. In the US, you get paid based on how you document. It’s safe to say that EHR software has made it easier to document at a higher level and get paid more.

Notice that the goals of EHR software weren’t to improve health outcomes or patient care. Those goals might have been desired by many, but it wasn’t the bill of goods sold to the practice. Now we’re trying to back all this EHR data into health outcomes and improved patient care. Is it any wonder it’s a challenge for us to accomplish these goals?

When was the last time a doctor chose an EHR based on how it could improve patient care? I think most were fine purchasing an EHR that they believed wouldn’t hurt patient care. Sadly, I can’t remember ever seeing a section of a RFP that talks about an EHRs ability to improve patient care and clinical outcomes.

No, we store data in an EHR so we can improve our billing. We store data in the EHR to avoid liability. We store data in the EHR because we need appropriate documentation of the visit. Can and should that data be used to improve health outcomes and improve the quality of care provided? Yes, and most are heading that way. Although, it’s trailing since customers never demanded it. Plus, customers don’t really see an improvement in their business by focusing on it (we’ll see if that changes in a value based and high deductible plan world).

In my previous post about medical practice innovation, Dr. Nieder commented on the need for doctors to have “margin in their lives” which allows them to explore innovation. Medical billing documentation is one of the things that sucks the margins out of a doctor’s life. We need to simplify the billing requirements. That would provide doctors more margins to innovate and explore ways EHR and other technology can improve patient care and clinical outcomes.

In response to yesterday’s post about Virtual ACO’s, Randall Oates, MD and Founder of SOAPware (and a few other companies), commented “Additional complexity will not solve healthcare crises in spite of intents.” He, like I, fear that all of this value based reimbursement and ACO movement is just adding more billing complexity as opposed to simplifying things so that doctors have more margin in their lives to improve healthcare. More complexity is not the answer. More room to innovate is the answer.

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://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 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://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.

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.

Harvard Law Conference Surveys Troubles With Health Care

Posted on March 30, 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.

It is salubrious to stretch oneself and regularly attend a conference in a related field. At the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, one can bask in the wisdom of experts who are truly interdisciplinary (as opposed to people like me, who is simply undisciplined). Their Tenth Anniversary Conference drew about 120 participants. The many topics–which included effects of the Supreme Court rulings on the Affordable Care Act and other cases, reasons that accountable care and other efforts haven’t lowered costs, stresses on the pharmaceutical industry, and directions in FDA regulation–contained several insights for health IT professionals.

From my perspective, the center of the conference was the panel titled “Health Innovation Policy and Regulating Novel Technology.” A better title might have been “How to Make Pharma Profitable Again,” because most of the panelists specialized in pharmaceuticals or patents. They spun out long answers to questions about how well patents can protect innovation (recognizing a controversy); the good, the bad, and the ugly of pricing; and how to streamline clinical trials, possibly adding risk. Their pulses really rose when they were asked a question about off-label drug use. But they touched on health IT and suggested many observations that could apply to it as well.

It is well known that drug development and regulatory approval take years–perhaps up to 20 years–and that high-tech companies developing fitness devices or software apps have a radically different product cycle. As one panelist pointed out, it would kill innovation to require renewed regulatory approval for each software upgrade. He suggested that the FDA define different tiers of changes, and that minor ones with little risk of disrupting care be allowed automatically.

I look even farther. It is well known also that disruptive inventions displace established technologies. Just as people with mobile devices get along without desktop computers and even TV sets, medicines have displaced many surgical procedures. Now the medicines themselves (particularly, controversial mental health medicines) can sometimes be replaced by interactive apps and online services. Although rigorous testing is still lacking for most of these alternatives, the biggest barrier to their adoption is lack of reimbursement in our antiquated health payment system.

Instead of trying to individually fix each distortion in payment, value-based care is the reformer’s solution to the field’s inefficient use of treatment options. Value-based care requires more accurate information on quality and effectiveness, as I recently pointed out. And this in turn may lead to the more flexible regulations suggested by the panelist, with a risk that is either unchanged or raised by an amount we can tolerate.

Comparisons between information and other medical materials can be revealing. For instance, as the public found out in the Henrietta Lacks controversy, biospecimens are treated as freely tradable information (so long as the specimen is de-identified) with no patient consent required. It’s assumed that we should treat de-identified patient information the same way, but in fact there’s a crucial difference. No one would expect the average patient to share and copy his own biospecimens, but doing so with information is trivially easy. Therefore, patients should have more of a say about how their information is used, even if biospecimens are owned by the clinician.

Some other insights I picked up from this conference were:

  • Regulations and policies by payers drive research more than we usually think. Companies definitely respond to what payers are interested in, not just to the needs of the patients. One panelist pointed out that the launch of Medicare Part D, covering drugs for the first time, led to big new investments in pharma.

  • Hotels and other service-oriented industries can provide a positive experience efficiently because they tightly control the activities of all the people they employ. Accountable Care Organizations, in contrast, contain loose affiliations and do not force their staff to coordinate care (even though that was the ideal behind their formation), and therefore cannot control costs.

  • Patents, which the pharma companies consider so important to their business model, are not normally available to diagnostic tests. (The attempt by Myriad Genetics to patent the BRACA1 gene in order to maintain a monopoly over testing proves this point: the Supreme Court overturned the patent.) However, as tests get more complex, the FDA has started regulating them. This has the side effect of boosting the value of tests that receive approval, an advantage over competitors.

Thanks to Petrie-Flom for generously letting the public in on events with such heft. Perhaps IT can make its way deeper into next year’s conference.

Another Quality Initiative Ahead of Its Time, From California

Posted on March 21, 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.

When people go to get health care–or any other activity–we evaluate it for both cost and quality. But health care regulators have to recognize when the ingredients for quality assessment are missing. Otherwise, assessing quality becomes like the drunk who famously looked for his key under the lamplight instead of where the key actually lay. And sadly, as I read a March 4 draft of a California initiative to rate health care insurance, I find that once again the foundations for assessing quality are not in place, and we are chasing lamplights rather than the keys that will unlock better care.

The initiative I’ll discuss in this article comes out of Covered California, one of the Unites States’ 13 state-based marketplaces for health insurance mandated by the ACA. (All the other states use a federal marketplace or some hybrid solution.) As the country’s biggest state–and one known for progressive experiments–California is worth following to see how adept they are at promoting the universally acknowledged Triple Aim of health care.

An overview of health care quality

There’s no dearth of quality measurement efforts in health care–I gave a partial overview in another article. The Covered California draft cites many of these efforts and advises insurers to hook up with them.

Alas–there are problems with all the quality control efforts:

  • Problems with gathering accurate data (and as we’ll see in California’s case, problems with the overhead and bureaucracy created by this gathering)

  • Problems finding measures that reflect actual improvements in outcomes

  • Problems separating things doctors can control (such as follow-up phone calls) with things they can’t (lack of social supports or means of getting treatment)

  • Problems turning insights into programs that improve care.

But the biggest problem in health care quality, I believe, is the intractable variety of patients. How can you say that a particular patient with a particular combination of congestive heart failure, high blood pressure, and diabetes should improve by a certain amount over a certain period of time? How can you guess how many office visits it will take to achieve a change, how many pills, how many hospitalizations? How much should an insurer pay for this treatment?

The more sophisticated payers stratify patients, classifying them by the seriousness of their conditions. And of course, doctors have learned how to game that system. A cleverly designed study by the prestigious National Bureau of Economic Research has uncovered upcoding in the U.S.’s largest quality-based reimbursement program, Medicare Advantage. They demonstrate that doctors are gaming the system in two ways. First, as the use of Medicare Advantage goes up, so do the diagnosed risk levels of patients. Second, patients who transition from private insurance into Medicare Advantage show higher risk not seen in fee-for-service Medicare.

I don’t see any fixes in the Covered California draft to the problem of upcoding. Probably, like most government reimbursement programs, California will slap on some weighting factor that rewards hospitals with higher numbers of poor and underprivileged patients. But this is a crude measure and is often suspected of underestimating the extra costs these patients bring.

A look at the Covered California draft

Covered California certainly understands what the health care field needs, and one has to be impressed with the sheer reach and comprehensiveness of their quality plan. Among other things, they take on:

  • Patient involvement and access to records (how the providers hated that in the federal Meaningful Use requirements!)

  • Racial, ethnic, and gender disparities

  • Electronic record interoperability

  • Preventive health and wellness services

  • Mental and behavioral health

  • Pharmaceutical costs

  • Telemedicine

If there are any pet initiatives of healthcare reformers that didn’t make it into the Covered California plan, I certainly am having trouble finding them.

Being so extensive, the plan suffers from two more burdens. First, the reporting requirements are enormous–I would imagine that insurers and providers would balk simply at that. The requirements are burdensome partly because Covered California doesn’t seem to trust that the major thrust of health reform–paying for outcomes instead of for individual services–will provide an incentive for providers to do other good things. They haven’t forgotten value-based reimbursement (it’s in section 8.02, page 33), but they also insist on detailed reporting about patient engagement, identifying high-risk patients, and reducing overuse through choosing treatments wisely. All those things should happen on their own if insurers and clinicians adopt payments for outcomes.

Second, many of the mandates are vague. It’s not always clear what Covered California is looking for–let alone how the reporting requirements will contribute to positive change. For instance, how will insurers be evaluated in their use of behavioral health, and how will that use be mapped to meeting the goals of the Triple Aim?

Is rescue on the horizon?

According to a news report, the Covered California plan is “drawing heavy fire from medical providers and insurers.” I’m not surprised, given all the weaknesses I found, but I’m disappointed that their objections (as stated in the article) come from the worst possible motivation: they don’t like its call for transparent pricing. Hiding the padding of costs by major hospitals, the cozy payer/provider deals, and the widespread disparities unrelated to quality doesn’t put providers and insurers on the moral high ground.

To me, the true problem is that the health care field has not learned yet how to measure quality and cost effectiveness. There’s hope, though, with the Precision Medicine initiative that recently celebrated its first anniversary. Although analytical firms seem to be focusing on processing genomic information from patients–a high-tech and lucrative undertaking, but one that offers small gains–the real benefit would come if we “correlate activity, physiological measures and environmental exposures with health outcomes.” Those sources of patient variation account for most of the variability in care and in outcomes. Capture that, and quality will be measurable.

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.

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 1 of 3)

Posted on November 24, 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 release of Joseph Kvedar’s book The Internet of Healthy Thingscoincided with the 15th annual symposium on Connected Health, which he runs every year and which I reported on earlier. Now, more than ever, a health field in crisis needs his pointed insights into the vision widely shared by all observers: collaborative, data-rich, technology-enabled, transparent, and patient-centered.

The promise and the imminent threat

A big part of Dr. Kvedar’s observations concern cost savings and “scaling” clinicians’ efforts to allow a smaller team to treat a larger community of patients with more intensive attention. As I review this book, shock waves about costs are threatening the very foundations of the Affordable Care Act. Massive losses by insurers and providers alike have led to the abandonment of Accountable Care Organizations by many who tried them. The recent bail-out by UnitedHealth was an ominous warning, eagerly jumped on by Fox News. Although other insurers issued assurances that they stay with the basic ACA program, most are reacting to the increased burden of caring for newly signed up patients by imposing insufferably high deductibles as well as extremely narrow networks of available providers. This turns the very people who should benefit from the ACA against the system.

There is nothing surprising about this development, which I have labeled a typical scam against consumers. If you sign up very sick people for insurance and don’t actually make them better, your costs will go up. T.R. Reid averred in his book The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care that this is the sequence all countries have to follow: first commit to universal healthcare, then institute the efficiencies that keep costs under control. So why hasn’t that happened here?

Essentially, the health care system has failed us. Hospitals have failed to adopt the basic efficiency mechanisms used in other industries and still have trouble exchanging records or offering patients access to their data. A recent study finds that only 40% of physicians shared data within their own networks, and a measly 5% share data with providers outside their networks.

This is partly because electronic health records still make data exchange difficult, particularly with the all-important behavioral health clinics that can creat lifestyle changes in patients. Robust standards were never set up, leading to poor implementations. On top of that, usability is poor.

The federal government is well aware of the problem and has been pushing the industry toward more interoperability and patient engagement for years. But as health IT leader John Halamka explains, organizations are not ready for the necessary organizational and technological changes.

Although video interviews and home monitoring are finding footholds, the health industry is still characterized by hours of reading People magazine in doctors’ waiting rooms. The good news is that patients are open to mobile health innovations–the bad news is that most doctors are not.

The next section of this article will continue with lessons learned–and applied–both by Dr. Kvedar’s organization, Partners Connected Health, and by other fresh actors in the health care space.

We’re Hosting the #KareoChat and Discussing Value Based Care and ACOs – Join Us!

Posted on June 23, 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.

ACO and Value Based Reimbursement Twitter Chat
We’re excited to be hosting this week’s #KareoChat on Thursday, 6/25 at 9 AM PT (Noon ET) where we’ll be diving into the details around Value Based Care and ACOs. We’ll be hosting the chat from @ehrandhit and chiming in on occasion from @techguy and @healthcarescene as well.

The topic of value based care and ACOs is extremely important to small practice physicians since understanding and participating in it will be key to their survival. At least that’s my take. I look forward to hearing other people’s thoughts on these changes on Thursday’s Twitter chat. Here are the questions we’ll be discussing over the hour:

  1. What’s the latest trends in value based reimbursement that we should know or watch? #KareoChat
  2. Why or why aren’t you participating in an ACO? #KareoChat
  3. Describe the pros and cons you see with the change to value based reimbursement. #KareoChat
  4. What are you doing to prepare your practice for value based reimbursement and ACOs? #KareoChat
  5. Which technologies and applications will we need in a value based reimbursement and ACO world? #KareoChat
  6. What’s the role of small practices in a value based reimbursement world? Can they survive? #KareoChat

For those of you not familiar with a Twitter chat, you can follow the discussion on Twitter by watching the hashtag #KareoChat. You can also take part in the Twitter chat by including the #KareoChat hashtag in any tweets you send.

I look forward to “seeing” and learning from many of you on Twitter on Thursday. Feel free to start the conversation in the comments below as well.

Full Disclosure: Kareo is a sponsor of EMR and EHR.