Arthur C. Clark and Stanley Kubrick predicted supercomputers more intelligent than humans. In 2001: A Space Odyssey, the HAL states, with typical human immodesty, “The 9000 series is the most reliable computer ever made… We are all, by any practical definition of the words, foolproof and incapable of error.” Forty years later, IBM’s Watson pummeled humans in Jeopardy – a distinctly human game.
Watson is a big shot oncology fellow at MD Anderson – he is already impressing nurses and the attendings. The supercomputer presented patients in the morning rounds, parsed data within seconds, and made few mistakes. The real oncology fellow, the human I mean, flabbergasted by the efficiency of his binary colleague, relayed to the Washington Post, “Even if you work all night, it would be impossible to be able to put this much information together like that.” Watson doesn’t have to worry about duty hour restrictions.
CEO of IBM, Ginni Rometty, claims that Watson 2.0 will interpret medical imaging like a radiologist. In its third iteration, the supercomputer will “debate and reason.” Why hire radiologists who sap productivity with lunch breaks and sleep? Watson will never complain about the dearth of vegan food in the cafeteria, never get tired, and – best of all – never whine about Medicare reimbursement cuts.
But forgive me for snoring at night without fear of the Robo-Radiologist. The reasons are simple.
Doctors are human. Their talents and skills differ. They make mistakes. And as with every other area of human endeavor: some doctors are really good; some are pretty bad; most are average. If you are over age 50, you’ve likely met an example of all three.
In the past decade there’s more open recognition of this reality and the need to address the failures it creates in medicine and the delivery of care. There’s more willingness now to say out loud that it’s not just poor system dynamics or gaps in planning, knowledge or training leading to poor care and bad results; it’s also the differential skills and ability of the people delivering care.
In an age where the importance of data, statistics and predictive modeling win big games for baseball teams and make fat money for high-frequency traders, we are at the dawn of a new age of transparency in healthcare It behooves every actor, in every sector, to use this new perspective to constructively illuminate best practices and design an infrastructure for true operational, clinical and logistic efficiencies at large scale and the local level – all in the spirit of getting the patient the best outcome.
Every modern industry uses ‘big data’ to understand the dynamics of their market landscape. This in turn, enables them to make decisions and develop strategies for gaining market share and building their brands. Fortress medicine has received a shot over the bow regarding the power of this new data perspective and needs to craft visionary, courageous yet mindful strategies that includes the bright light of outcomes into their private practices, clinics and large institutions. Propublica, in a seminal article, Making the Cut, shows us the power of transparency in complications rates during surgery. Doctors and their patients, since the dawn of medicine, have existed in a world without clarity around outcomes – there was not way to meaningfully collect it and analyze it. What Yelp has done for small business and Zagat has done for fine restaurants, CMS just did for the medical profession….and it just might be a needed dose of datacillin to start an honest conversation about what this all means.Continue reading…
Although Pennsylvania is the sixth most populous and ninth most densely populated state in the Union, based on information from the United States Census Bureau from 2010 and 2013, it also is home to a significant amount of rural areas. According to the Pennsylvania Rural Health Association, 48 of the 67 counties in the state are classified as rural, and all but two counties have rural areas. Approximately 27 percent of Pennsylvanians lived in rural counties in 2010, The Center for Rural Pennsylvania reports.Continue reading…
How do you plan? Obviously, you have to. Obviously, you can’t.
For your organization, and for you as a health care leader, the rapid and, at times, chaotic changes in the payment systems, the purchasers’ strategies, your population base, new technological possibilities, and the competitive landscape mean that you must plan for the future and act vigorously to make that future happen — or you fail. At the same time, those very same factors render traditional planning methods irrelevant, impossible, even deadly.
The movie line that comes to mind is, “Forget it, Jake. It’s Chinatown.” But we can’t just forget it. We must figure this out.
Let’s step through it: the shape of the complexity we are dealing with, how the process must change to deal with it. Then we get to a core issue that often gets overlooked: What kind of mind do we need for this new thinking, and how do we cultivate it?
Some measures of health care quality and patient safety should be taken with a grain of salt. A few need a spoonful.
In April, a team of Johns Hopkins researchers published an article examining how well a state of Maryland pay-for-performance program measure for dangerous blood clots identified cases that were potentially preventable. In reviewing the clinical records of 157 hospital patients deemed by the state program to have developed these clots — known as deep vein thrombosis and pulmonary embolism — they found that more than 40 percent had been misclassified. The vast majority of these patients had clots that were not truly preventable, such as those associated with central catheters, for which the efficacy of prophylaxis remains unproven.
These misclassified cases of blood clots resulted in potentially $200,000 in lost reimbursement from the state, which penalizes hospitals when the additional treatment costs related to more than 60 preventable harms exceeds established benchmarks.
Why the discrepancies? The state identified cases of these clots using billing data, which utilize the diagnosis codes that medical billing specialists enter on claims. These data, also known as administrative data, lack the detail that would be available in the actual clinical record, considered by many to be the most trusted source for safety and quality measures.
The proposal involves a five-year bundled payment model across 75 geographic areas whereby hospitals would be eligible for a bonus if their costs and outcomes were optimal or be penalized if not based on results 90 days post-discharge. The agency noted that in 2013, it spent more than $7 billion on hospitalization for these procedures with the payments for hospitalization and recovery ranging widely from 16,500 to $33,000. Comments about the proposal will be received by CMS through September 8, 2015, aiming for implementation January 1, 2016.
Their rationale, according to Secretary of Health and Human Services Sylvia Burwell, in the HHS statement announcing the proposal: “By focusing on episodes of care, rather than a piecemeal system, hospitals and physicians have an incentive to work together to deliver more effective and efficient care. This model will incentivize providing patients with the right care the first time and finding better ways to help them recover successfully. It will reward providers and doctors for helping patients get and stay healthy.”Continue reading…
The aging of populations worldwide is leading to many healthcare challenges, such as an increase in dementia patients. One recent estimate suggests that 13.9% of people above age 70 currently suffer from some form of dementia like Alzheimer’s or dementia associated with Parkinson’s disease. The Alzheimer’s Association predicts that by 2050, 135 million people globally will suffer from Alzheimer’s disease.
While these are daunting numbers, some forms of cognitive diseases can be slowed if caught early enough. The key is early detection. In a recent study, my colleague and I found that machine learning can offer significantly better tools for early detection than what is traditionally used by physicians.
One of the more common traditional methods for screening and diagnosing cognitive decline is called the Clock Drawing Test. Used for over 50 years, this well-accepted tool asks subjects to draw a clock on a blank sheet of paper showing a specified time. Then they are asked to copy a pre-drawn clock showing that time. This paper and pencil test is quick and easy to administer, noninvasive, and inexpensive. However, the results are based on the subjective judgment of clinicians who score the tests. For instance, doctors must determine whether the clock circle has “only minor distortion” and whether the hour hand is “clearly shorter” than the minute hand.
Research is a critical part of creating a learning health system that routinely incorporates the latest treatment guidelines into its clinical care. The efficacy of new treatments and guidelines are studied by researchers who recommend better, more personalized treatment protocols. Increasingly, researchers have been tasked with not only identifying new interventions to create better clinical outcomes, but also with partnering with healthcare delivery systems to implement those new discoveries.
Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.
Date: Wednesday, July 22, 2015
Time: 1:00–2:00 PM ET
Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.
Attendees will be presented with:
- A summary of some of the challenges in using data and analytics for research
- A research analytics adoption framework for all organizations interested in using clinical data for research
- What is needed from a workflow and organizational perspective to power research with data
We look forward to you joining us.
In my work with hundreds of over stressed and burned out physicians, one thing is constant. Documentation is always one of their biggest sources of stress.
In fact, if you ask the average working doctor to make a list of their top five stresses, documentation chores will take up three of the five slots.
1. EMR – especially if you use multiple EMR software programs that don’t talk to each other
2. Dealing with lab reports and refill requests
3. Returning patient and consultant calls and documenting them adequately and all the other places information streams have to be forced together by the sweat of your brow.
The average doc is walking the cliff edge of overload on a significant number of office days in any given month. Now comes ICD-10 and my biggest fear is the extra work of the new coding system will push many physicians over the edge into burnout.
How much more time will ICD-10 take?