Data is not always the path to identifying good medicine. Quality and cost measures should not be perceived as “scores,” because the health care process is neither simplistic nor deterministic; it involves as much art and perception as science—and never is this more the case than in the first step of that process, making a diagnosis.
I share the following story to illustrate this lesson: we should stop behaving as if good quality can be delineated by data alone. Instead, we should be using that data to ask questions. We need to know more about exactly what we are measuring, how we can capture both the physician and patient inputs to care decisions, and how and why there are variations among different physicians.
A Tale of Two Doctors
“As soon as I start swimming, my chest feels heavy and I have trouble breathing. It is a dull pain. It is scary. I swim about a lap of the pool, and, thankfully, the pain goes away. This is happening every time I go to work out in the pool”.
Her primary physician listened intently. With more than 40 years of experience, the physician, a stalwart in the medical community, loved by all, who scored high on the “physician compare” web site listing, stopped the interview after the description and announced, with concern, that she needed to have a cardiac stress test. The stress test would require walking on a “treadmill” to monitor her heart and would include, additionally, an echocardiogram test to see if her heart was being compromised from a lack of blood flow.
“But, I have had three echocardiogram tests in the last year as part of my treatment for breast cancer and each was normal. Why would I need another”?
“Well, I understand your concern about more tests, but the echocardiograms were done without having your heart stressed by exercise. The echo tests may be normal under those circumstances, but be abnormal when you are on the treadmill. You still need the test, unfortunately. I want to order the test today and you should get it done in the next week”.
I don’t know why, but even as a young person I never could make sense of the saying, “seeing is believing”. Seeing, vision, is nothing more than a data collection instrument, not an arbiter of insight. I saw my wife frown at me the other day, for example, after I claimed to have washed the dishes so thoroughly that no spot of grease could be left behind. I have made this claim before and been incorrect, so the frown, the data, triggered an anticipation of being rebuffed. However, nothing of that sort followed. I asked, Why the frown?” She responded, “I just cut my finger”. The frown was obvious, the cause unclear. I believed I was about to be reprimanded and missed the chance to notice her accident. This story suggests that a truer aphorism might be, instead, then, that “believing is seeing”.
“Effectiveness” is at the forefront of the health policy debate. Effectiveness is the assessment of whether any particular medical intervention actually advantages patients when prescribed in practice settings. To be considered effective, the intervention must result in a clinically meaningful improvement for an adequate percentage of patients. Furthermore, it must not result in a clinically important adverse outcome in too many. Clearly, “effectiveness” is a value-laden construct. How is “meaningful improvement” defined and by whom? How is “important adverse outcome” defined and by whom? How are “adequate percentage” and “too many” defined and by whom?
“Cost-effectiveness” is even more value-laden. It is legislated to be off-the-table in the machinations of the Affordable Care Act for reasons that vary from fear of rationing to fear of compromising profit margins. But no one can exclude cost-effectiveness from the patient-doctor dialogue. Considerations of co-pays and deductibles often weigh heavily in the valuation of interventions.
The greatest advance in clinical medicine in my time in the practice, fast approaching 50 years, is that today patients and their doctors can assess effectiveness as collaborators. No longer does an imperious pronouncement by a physician suffice. Rather, the patient should occupy the driver’s seat with the physician as navigator. For each option in intervention, the patient asks, “Based on the available science, what is the best I can expect?”
For nearly 50 years, no prescription drug could be marketed unless the FDA was convinced that it had a tolerable benefit-to-risk ratio based on scientific studies. The bar for devices (like hip replacements) and procedures (like liposuction) is not as high, but there is usually some informative clinical science of this nature. The science is generally designed in the hope of demonstrating a favorable benefit-to-risk ration. Hence, patients and interventions are chosen to measure outcomes in the best case. However, make no mistake; neither the fact of FDA approval nor common practice is an adequate response to “What is the best I can expect?” If the best case falls short in your mind, why would you acquiesce to the intervention?
In an article posted earlier this year on this blog I argued that hospitals have traditionally done a sub-par job of leveraging what has now been dubbed “big data.” Effectively mining and managing the ever rising oceans of data presents both a major challenge – and a significant opportunity – for hospitals.
By doing a better of job connecting the dots of their big data assets, hospital management teams can start to develop the crucial insights that enable them to make the right and timely decisions that are vital to success today. And, better, timelier decisions lead to improved results and a higher level of quality patient care.
That’s the good news. The less than positive story is that hospitals are still way behind in using the mountains of data that are being generated within their institutions every day. Nowhere is this more apparent than in the advanced data management practice of predictive modeling.
At its most basic, predictive modeling is the process by which data models are created and used to try to predict the probability of an outcome. The exciting promise of predictive modeling is that it literally gives hospitals the ability to see into (and predict) the future. Given the massive changes and continuing uncertainty that are buffeting all sectors of the healthcare industry (and especially healthcare providers), having a clearer future view represents an important strategic advantage for any hospital leader.
LeBron James exploded past his defender and raced towards the lane.
Serge Ibaka, the Thunder’s mountainous center, planted his feet and raised his hands straight up into the air. LeBron ducked his left shoulder and plowed right into Ibaka, who went crashing backwards into a nearby cameraman.
Maybe if it had been the first quarter. But given that this was the last minutes of a tightly fought game, the referees chose to restrain themselves, not wanting the game to turn on their actions. Was this even controversial? Not a bit. In such situations, announcers typically applaud the non-call, intoning platitudes like “this game should be decided by the players.”
In their excellent book Scorecasting, Tobias Moskowitz and L. Jon Woertheim explore the psychology of sports through exhaustive and yet entertaining analyses of all kinds of topics that have fueled many a heated bar stool argument.
Are referees biased against your favorite team? According to their analyses, they are biased against your team only if it is playing an away game. Turns out that their unconscious desires to please fans cause referees and umpires to back away from controversial calls that will raise the crowd’s ire.
One of the most fascinating chapters in the book involves what the authors call “whistle swallowing.” All else equal, referees and umpires avoid sins of commission over sins of omission, a preference for inactivity nicely summarized by veteran NBA referee Gary Benson: “It’s late in the game and, let’s say, there’s goal tending and you miss it. That’s an incorrect non-call and that’s bad. But let’s say it’s late in the game and you call goal tending on a play and the re-play shows it was an incorrect call. That’s when you’re in a really deep mess.”Continue reading…
Spend some time with the Society for Medical Decision Making, and “shared decisions” starts to seem less a clinical ideal and more an offshoot of picking a monthly cell phone plan. The fine line between “motivating” and “manipulating” behavior (albeit sometimes unintentionally) starts to blur.
At the group’s recent annual meeting in Chicago, the differing sensibilities of medical and marketplace ethics were in plain view on a panel entitled (with a nod to the Richard Thaler and Cass Sunstein behavioral economics best-seller), “From a Nudge to a Shove: How Big a Role for Shared Decision Making?”
Peter Ubel, a physician and a professor of marketing and public policy at Duke University, told how some free-market theorists have defined away, “overweight.” Since people know what causes them to put on pounds, goes this reasoning, the weight they are must be the weight they rationally decided to be. (Shades of Dr. Pangloss!)
Unfortunately, eating decisions are not purely rational. Eat in a large group, said Ubel, and lingering at lunch could boost your calorie count by 25 percent. Choose the large plate at the buffet table over the small one and bump up calories another 25 percent. Our brains even seek out the bad: give us two identical crackers, but label one as having a more “unhealthy fat,” and we’ll consistently pick it over the healthier-labeled cracker in a taste test.