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Using Technology to Better Inform Consumers about Privacy Decisions

Apr 30, 2014• 1

By Cameron F. Kerry

At the first White House public workshop on Big Data, Latanya Sweeney, a leading privacy researcher at Carnegie Mellon and Harvard who is now the chief technologist for the Federal Trade Commission, was quoted as asking about privacy and big data, “computer science got us into this mess; can computer science get us out of it?”

There is a lot computer science and other technology can do to help consumers in this area. Some examples:

• The same predictive analytics and machine learning used to understand and manage preferences for products or content and improve user experience can be applied to privacy preferences. This would take some of the burden off individuals to manage their privacy preferences actively and enable providers to adjust disclosures and consent for differing contexts that raise different privacy sensitivities.

Computer science has done a lot to improve user interfaces and user experience by making them context-sensitive, and the same can be done to improve users’ privacy experience.

• Tagging and tracking privacy metadata would strengthen accountability by making it easier to ensure that use, retention, and sharing of data is consistent with expectations when the data was first provided.

• Developing features and platforms that enable consumers to see what data is collected about them, employ visualizations to increase interpretability of data, and make data about consumers more available to them in ways that will allow consumers to get more of the benefit of data that they themselves generate would provide much more dynamic and meaningful transparency than static privacy policies that few consumers read and only experts can interpret usefully.

In a recent speech to MIT’s industrial partners, I presented examples of research on privacy-protecting technologies.

The Gift of Cancer

Apr 30, 2014• 4

By Brett Hendel-Paterson, MD

After my last post about “the gift of cancer” I must say that CLL has felt much less like a gift this month.

Joining the ranks of those with “a diagnosis” has given me a some insight into what our patients face all the time.

Recently, I received my second dose of humility. I capped off a truly exhausting week in the hospital with a routine lab follow-up.

The last day of my 85-hour week I had my CBC checked, and my platelets dropped from the 100s to the 30s.

My first reaction was denial. Lab error.

Unfortunately, they dropped further the next day and I realized that the little red bumps on my legs weren’t some skin reaction, but petechiae. Bummer. Turns out that in addition to the 2% of people diagnosed with CLL under age 40, I also joined the 20% who develop idiopathic thrombocytopenic purpura (ITP).

The treatment of choice for ITP is prednisone 1mg/kg. So after a visit with my oncologist, I started 80mg of prednisone.

I realized with more than a little chagrin that I have a double standard about therapeutics. I was surprised at how much I despise being on prednisone.

I had never taken it before, and I would guess that I prescribe it every week, if not every day, that I work in the hospital. I have always felt that prednisone is fine for my patients to take.

Steroids work to help clear up that asthma flare, quickly improve that gout pain, or even help with a burst of energy in the last days or weeks of life for a terminal patient.

But for me? No thank you.

Should Medical Schools Teach to the Boards?

Apr 29, 2014• 13

By Abraar Karan

In the 2012 National Residency Match Program Survey, which is sent out to residency program directors around the country by the NRMP, the factor that was ranked highest with regards to criteria considered for receiving an interview—higher than honors in clinical clerkships, higher than extracurricular experiences or AOA election, and even higher than evidence of professionalism, interpersonal skills, and humanistic qualities—was the USMLE Step 1 score.

When considering where to rank an interviewed applicant, the Step 1 score took a backseat to some of the aforementioned criteria that are perhaps more telling of what kind of person the interviewee is, although it was still one of the highest considered criteria for ranking applicants as well.

When a single exam is given this level of importance in determining a future physician’s most critical period in career development—their residency—we have to look carefully at our system.

Two points of consideration come to mind. First, is it wise to weigh a test score so heavily? Many students and faculty could easily point out that student performance on exams by no means always reflects their clinical acumen and social skills when seeing patients.

Medicine is, after all, an art far more than a science.

Nonetheless, it would be foolish to assume that scores have no worth—a high score on an exam, particularly a behemoth such as the USMLE Step 1, points out many qualities in an individual: hard work, persistence, discipline, and frankly, an understanding of textbook medicine.

And thus, we are left somewhere in the middle—perhaps we should weigh scores less than we do, but when you have to sort through thousands of applications, the only standardized metric to quickly compare is, in the end, a number somewhere between 192 and 300.

Traditional Chinese Herbalism at the Cleveland Clinic? What Happened to Science-Based Medicine?

Apr 29, 2014• 34

By David Gorski, MD

I don’t recall if I’ve ever mentioned my connection with the Cleveland Clinic Foundation (CCF). I probably have, but just don’t remember it.

Long-time readers might recall that I did my general surgery training at Case Western Reserve University at University Hospitals of Cleveland. Indeed, I did my PhD there as well in the Department of Physiology and Biophysics.

Up the road less than a mile from UH is the Cleveland Clinic. As it turns out, during my stint in Physiology and Biophysics at CWRU, I happened to do a research rotation in a lab at the CCF, which lasted a few months.

OK, so it’s not much of a connection. It was over 20 years ago and only lasted a few months, but it’s something that gives me an obvious and blatant hook to start out this post, particularly given the number of cardiac patients I delivered to the CCF back in the early 1990s when I moonlighted as a flight physician forMetro LifeFlight.

Obvious and clunky introduction aside (hey, they can’t all be brilliant; so I’ll settle for nauseatingly self-deprecating), several of my readers have been sending me a link to a story that appeared in the Wall Street Journal the other day: A Top Hospital Opens Up to Chinese Herbs as Medicines: Evidence is lacking that herbs are effective.

I also noticed that Steve Novella blogged about it and was tempted to let it pass, given that I had seemingly lost my window, but then I realized that there’s always something I can add to a post, even after the topic’s been blogged by Steve Novella.

Whether that something is of value or not, I leave to the reader. So here we go. Besides, if this article truly indicates a new trend in academic medical centers, it’s—if you’ll excuse the term—quantum leap in the infiltration of quackademic medicine into formerly reputable medical centers.

It’s a depressing thing, and it needs to be publicized.

Hacking the Hospital: Medical Devices Have Terrible Default Security

Apr 29, 2014• 3

By Cory Doctorow

Scott Erven is head of information security for a healthcare provider called Essentia Health, and his Friday presentation at Chicago’s Thotcon, “Just What The Doctor Ordered?” is a terrifying tour through the disastrous state of medical device security.

Wired’s Kim Zetter summarizes Erven’s research, which ranges from the security of implanted insulin pumps and defibrillators to surgical robots and MRIs. Erven and his team discovered that hospitals are full of fundamentally insecure devices, and that these insecurities are not the result of obscure bugs buried deep in their codebase (as was the case with the disastrous Heartbleed vulnerability), but rather these are incredibly stupid, incredibly easy to discover mistakes, such as hardcoded easy default passwords.

For example: Surgical robots have their own internal firewall. If you run a vulnerability scanner against that firewall, it just crashes, and leaves the robot wide open.

The backups for image repositories for X-rays and other scanning equipment have no passwords. Drug-pumps can be reprogrammed over the Internet with ease. Defibrillators can be made to deliver shocks — or to withhold them when needed.

Doctors’ instructions to administer therapies can be intercepted and replayed, adding them to other patients’ records.

You can turn off the blood fridge, crash life-support equipment and reset it to factory defaults. The devices themselves are all available on the whole hospital network, so once you compromise an employee’s laptop with a trojan, you can roam free.

You can change CT scanner parameters and cause them to over-irradiate patients.Continue reading…

Why a Majority of Readmission Risk Tools Fail in Practice

Apr 28, 2014• 8

By Eric Heil

Potentially preventable readmissions are a scourge on the US healthcare system.

Each year millions of patients are discharged from the hospital, only to return within 30, 60, or 90 days.

Not only do patients, their families, and their caregivers suffer as a result, but hospitals, insurers, and the government waste billions of dollars that could be spent on other public health priorities. Many if not most of these readmissions could have been avoided if clinicians had effective, scalable, and timely methods for identifying not only which patients were the highest risk, but what steps should have been taken to mitigate that risk.

In recent years there has been a proliferation of readmission risk assessment models, yet readmission rates have barely budged. Fundamental flaws exist in most approaches in the areas of Data, Model Adaptability and Clinical Workflow Integration.

Many tools rely solely on historical patient data mined from the EHR or are disease-specific models that cannot be scaled to address all readmissions challenges. Models that rely on data collected at discharge are not timely enough to enable clinicians to take meaningful action, and ones that are not well-integrated into clinical workflow are not easily adopted.

For a readmission risk assessment tool to achieve a meaningful and long-lasting impact, these common pitfalls must be avoided at all costs. Today, I’m going to address some of the many data challenges faced when trying to risk assess patients.

Historical Data does not Predict Future Readmissions

Anybody who has ever invested in the stock market, rooted for a local sports team, or stuck with a television show past its tenth season knows that past performance gives you no guarantee on future returns. Factors beyond our control and beyond our ability to predict may cause our fortunes to turn on a dime.

Consider the Dow Jones Industrial Average: Those who had any investments around July of 2007 remember the feelings of unabashed optimism and certainty inspired by the great bull run of the early 2000s. Unfortunately, those same investors also most assuredly remember what happened shortly thereafter, when the financial crisis of 2008 erased trillions of dollars’ worth of wealth.

A recent systematic review of readmission risk models concluded that many hospitals still model their approach to identifying high-risk patients based on historical admissions, claims data, and outdated information on patient populations [1].

Using these old data to model and predict readmissions is dangerous. And with increasing pressure on hospitals to reduce readmissions, this approach also runs the risk of becoming extremely costly. Just ask the guy who splurged on Brooklyn Dodgers tickets in 1958, or the guy who put all his money into 8-track cassettes in 1979, or the guy who started a Hummer dealership in 2005.

Any of these folks will tell you that past performance data can not only betray you, but it may also prevent you from recognizing the obsolescence of your sources. As a result, this data may cost you a fortune.

Health Reform and the Mission of Nonprofit Hospitals

Apr 28, 2014• 1

By Mary Crossley

Ever since 1969, when the IRS established the “community benefit” standard for hospital tax exemption, nonprofit hospitals have been able to achieve federal tax exemption without any precise accountability for the benefits they provided.

The ACA’s passage, however, ushered in significant changes to federal tax-exemption standards for hospitals.

The new § 501(r) of the Internal Revenue Code requires hospitals to take numerous measures, including establishing written financial assistance policies, limiting the amount charged to patients eligible for financial assistance, and limiting their use of “extraordinary collection actions” against patients.

These requirements responded to concerns about how some purportedly “charitable” hospitals treated uninsured patients and, more generally, hospitals’ lack of transparency regarding indigent care.

They stop well short, however, of requiring hospitals to provide any particular quantum of free care to patients unable to pay.

Section 501(r) also incorporates a different tack, requiring that at least once every three years, a hospital conduct a “community health needs assessment” (CHNA) and adopt an “implementation strategy” to respond to the needs identified by the assessment.

The needs assessment requirement is novel as a matter of federal tax policy, but is similar to mandates previously existing in a number of states.

As announced in the statute and fleshed out in Proposed Regulations issued by the IRS in April 2013, the CHNA requirement entails a series of steps.

In identifying and prioritizing community health needs, a hospital must take into account “input from persons who represent the broad interests of the community served by the hospital facility, including those with special knowledge of or expertise in public health.”

Once the assessment is completed, the hospital must make a report on it “widely available to the public” and adopt an “implementation strategy” to meet the community health needs it identified.

Doctors Should Be Paid for Outcomes. But Which Outcomes?

Apr 28, 2014• 7

By Hans Duvefelt, MD

Should we be paid for outcomes?

This is often proposed, but I have trouble understanding it. Real outcomes are not blood pressure or blood sugar numbers; they are deaths, strokes, heart attacks, amputations, hospital-acquired infections and the like.

In today’s medicine-as-manufacturing paradigm, such events are seen as preventable and punishable.

Ironically, the U.S. insurance industry has no trouble recognizing “Acts of God” or “force majeure” as events beyond human control in spheres other than healthcare.

There is too little discussion about patients’ free choice or responsibility. Both in medical malpractice cases and in the healthcare debate, it appears that it is the doctor’s fault if the patient doesn’t get well.

If my diabetic patient doesn’t follow my advice, I must not have tried hard enough, the logic goes, so I should be penalized with a smaller paycheck.

The dark side of such a system is that doctors might cull such patients from their practices in self defense and not accept new ones.

I read about some practices not accepting new patients taking more than three medications. In the example I read, the explanation was not having time for complicated patients, but such a policy would also reduce the number of patients exposing the doctor to the risk of bad outcomes.

A few comparisons illustrate the dilemma of paying for outcomes:

Do firefighters not get paid if the house they’re dousing to the best of their ability still burns down?

Does the detective investigating a homicide not get a paycheck if the crime remains unsolved?

Does the military get less money if we lose a war?

Even if we were to accept and embrace outcomes-based reimbursement in health care, how would we measure outcomes?

What We’ve Learned from Horizontal and Vertical Integration of Physicians

Apr 25, 2014• 10

By Lawton R. Burns & Aditi Sen

As health care reform rolls out, there is a growing focus on restructuring the health service delivery system in the hope of improving health care quality and “bending the cost curve.”

A key part of this focus has been on physician organization and, in particular, moving toward large, multispecialty physician groups or hospital-physician systems that can provide integrated, coordinated patient care (e.g., through “Accountable Care Organizations”).

In a recent chapter in Advances in Health Care Management’s Annual Review of Health Care Management, however, we and our co-author Jeff Goldsmith find that there is little evidence for the superiority of these integrated models in terms of patient care quality or cost-savings, and that the trends toward physician consolidation has been much less dramatic than is often thought.

Using data from a variety of sources, we find there are two separate phenomena at work in physician organization. At one end of the spectrum (bottom tail of the size distribution of physician groups), the majority of physicians continue to practice in small groups, although there has been some movement from really small practices (one to three or four physicians) to slightly larger groups (five to nine physicians).

Still, nearly two-thirds of office-based physicians continue to practice in solo settings, two-person partnerships, and small (usually single specialty) groups with five or fewer physicians.

At the other end of the spectrum (upper tail of the distribution), however, is a smaller number of very large and rapidly growing multispecialty physician groups, which are often owned by hospitals, health plans, private equity firms, or other non-physician sponsors.

These two stories of what is happening in the distribution of physician group size are described as “a tale of two tails.”

The Medical Chart: Ground Zero for the Deterioration of Patient Care

Apr 25, 2014• 32

By Val Jones, MD

EMR Alert – Featuring radiologist note in illegible font color

For the past couple of years I’ve been working as a traveling physician in 13 states across the U.S.

I chose to adopt the “locum tenens lifestyle” because I enjoy the challenge of working with diverse teams of peers and patient populations.

I believe that this kind of work makes me a better doctor, as I am exposed to the widest possible array of technology, specialist experience, and diagnostic (and logistical) conundrums. During my down times I like to think about what I’ve learned so that I can try to make things better for my next group of patients.

This week I’ve been considering how in-patient doctoring has changed since I was in medical school. Unfortunately, my experience is that most of the changes have been for the worse.

While we may have a larger variety of treatment options and better diagnostic capabilities, it seems that we have pursued them at the expense of the fundamentals of good patient care.

What use is a radio-isotope-tagged red blood cell nuclear scan if we forget to stop giving aspirin to someone with a gastrointestinal bleed?