UCLA

Screen Shot 2014-07-28 at 1.53.01 PM

x axis = time since prostatectomy, the y axis = predicted sexual function level,
scaled by the sexual function level.  Young patients (below 55 years) reporting
poor sexual function prior to treatment 

For all the advances in both medicine and technology, patients still face a bewildering array of advice and information when trying to weigh the possible consequences of certain medical treatments. But a hands-on, data-driven tool I have developed with some colleagues can now help patients obtain personalized predictions for their recovery from surgery. This tool can help patients better manage their expectations about their speed of recovery and long-term effects of the procedure.

People need to be able to fully understand the possible effects of a medical procedure in a realistic and clear way. Seeking to develop a model for recovery curves, we developed a Bayesian modeling approach to recovery curve prediction in order to forecast sexual function levels after prostatectomy, based on the experiences of 300 UCLA clinic patients both before radical prostatectomy surgery and during the four years immediately following surgery. The resulting interactive tool is designed to be used before the patient has a prostatectomy in order to help the patient manage expectations. A central predicted recovery curve shows the patient’s average sexual function over time after the surgery. The tool also displays a range of lighter-colored curves illustrating the broader range of possible outcomes.

This model not only shows people what they can expect about their recovery on average, based on their own specific characteristics, but it also clarifies the uncertainty in the shape of the recovery curve. It shows a range of possible realistic outcomes. We want to help patients who are considering this particular surgery to understand what they could expect. We can’t tell them exactly what their recovery will look like, but at least we can now forecast a personalized recovery curve and show them an informed prediction of their possible outcomes. The model can be used in an interactive way. For example, patients could adjust their reported age or reported sexual function levels to see how their predicted recovery curves change.

Continue reading “Using Big Data to Manage Medical Expectations”

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Thanks to extraordinary advances in medicine, critical care providers can save lives even when the cards are stacked against their patients. However, there are times when no amount of care, however cutting-edge it is, will save a patient. In these instances, when physicians recognize that patients will not be rescued, further critical care is said to be “futile.” In a new study, my RAND and UCLA colleagues and I find that critical care therapies that physicians regard as “futile” are not uncommon in intensive care units, raising some uncomfortable questions.

Of course, we’re fortunate to have such fantastic technology at our disposal — but we must address how to use it appropriately when the patient may not benefit from high-intensity measures. When aggressive critical care is unsuccessful at achieving an acceptable level of health for the patient, treatment should focus on palliative care.

In our study, my colleagues and I quantified the prevalence and cost of “futile” critical care in the journal JAMA Internal Medicine. This can be seen as the first step toward reevaluating the status quo and better optimizing care for critical care patients.

After convening a group of critical care clinicians to determine a consensus definition of “futile treatment,” our research team analyzed nearly 7,000 daily assessments of more than 1,000 patients.

We found that 11 percent received futile treatment, while an additional 9 percent received “probably futile” treatment.

So physician-perceived futile critical care is indeed prevalent. But what about the cost?

Continue reading “What to Do About Futile Critical Care”

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Visit SDIndyACO.com, and you’re greeted by a Hawaiian shirt hanging in an otherwise empty closet. “Future home of something quite cool,” the page’s headline reads.

Forget unicorns, camels and all the other metaphors used to describe accountable care organizations these past few years.

The website — the homepage of the newly formed San Diego Independent ACO, which was one of 106 organizations named last week to Medicare’s Shared Savings Program — could sum up where we stand now on ACOs.

While we’re close enough to see their outline, some ACOs are still just teasing their promise. Many organizations have yet to launch a Web presence (or in San Diego Independent ACO’s case, are waiting to get CMS approval). And more health care providers are rushing to build the ACO structure in hopes of winning federal contracts — and filling out the details later.

Understanding the Medicare ACO Model

The ACO model is loosely defined as having integrated teams of providers share responsibility for caring for a select population of patients. (That isn’t a new idea – and based on that definition, California’s had dozens of physician-led groups and integrated networks essentially operating as ACOs for years.)

Continue reading “Inside Three ACOs: Why California Providers Are Opting for the Model”

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