Most of these studies are intended to decrease costs as a result hopefully of this analysis. Even well designed studies such as Propublica's are fraught with incorrect assumptions. Statistics should only be taken at face value and individual case studies are imperative. Many cases require a deep dyve to extract information that will improve safety and diminish complications.
Some controversy and a deeper look into the Propublica Study reveal mitigating information about less than optimal statistics for some surgeons.
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When a bad surgeon is the one you want: ProPublica introduces a paradox
Case #1
Morbidity Hunter’s real name is Harjinder Singh. He migrated from Punjab and works in a safety net hospital in North Philadelphia. Singh wanted to work in Beverley Hills, but to convert his J1-visa to a green card, he had to work in an area of need. Once he started working, he liked his job. His daughters liked their school, and his wife liked the house they bought. Singh doesn’t have shiny teeth. He hasn’t appeared on TV, although his daughters tease that he can play Sonny from Exotic Marigold Hotel.
Singh’s colleagues named him Morbidity Hunter because he operates regardless of how sick his patients are. He never says no. Nearly all his patients are obese and diabetic. The school of public health sends students to shadow him to learn about polypharmacy. The hospital went on a spree of hiring hospitalists when Singh started.
His patients, straddling the Federal Poverty Limit, don’t rate him on Yelp. His patients don’t use Yelp. Even if they were informed consumers they would have to choose Singh, because there are very few orthopedic surgeons who are willing to operate on them in that zip code. His patients haven’t heard of Cherry Picker. They don’t ski, ballroom dance or run half marathons.
Singh, too, is good at his craft. Technically excellent, to be precise. You wouldn’t know that from looking at the rates of readmission, infection, and deep vein thrombosis in his patients. But the staff in the operating room know that, as do his colleagues, whom he has often helped out in tough operations. Even Cherry admires him.
Singh is not in for the money. He doesn’t make as much money as Cherry, but makes enough. He doesn’t operate for glory. He operates for professional pride — an ethereal concept that eludes some health economists.
It’s hard to zap the morale of this sturdy lad from the Punjab. But the data transparency movement achieved that. He always knew that operating on the sickest, poorest and most disenfranchised section of society was not going to be lucrative. But he never knew he was going to be made the captain of their ship — he was happy to captain the placement of their total hip — but what happened before or after they entered the operating room was not his fault, he felt.
People began to call Singh an incompetent surgeon. He objected, but he could not understand the logic behind the numbers which were incriminating him. His complication rates were the highest in Philadelphia. Numbers don’t lie, supposedly. This was too much for him to bear. He didn’t mind losing the pitiful bonuses that CMS was withholding from him, but the reason broke his heart: his poor quality.
Singh was puzzled by people who claimed to lose sleep over the poor. The chasm between their sentimentality and actions baffled him. Punjab began to make more sense than Philadelphia. But then Cherry invited Singh to join his practice in New York. Cherry promised Singh that he could operate on technically challenging patients. Grudgingly, Singh accepted the offer, which made his wife very excited about shopping for Indian food in Queens. She insisted, though, that Singh had to see a dentist first.
Homo sapiens have always sought redemption. Today it is through data. Numbers have replaced Yahweh and Indra. But, just like the old gods were, numbers can be moody, arbitrary and, occasionally, downright unfair. Numbers are a human construct, after all.
Case #2
Cherry Picker lives in the Upper East Side of New York. His patients give him great reviews on Yelp. His patients read every comment on Yelp before making any decision. Cherry Picker has a beautiful family. When he smiles, light refracts from his shiny teeth.
Cherry regularly appears on TV. He writes for the sleek, metrosexual publication, FHM. Cherry specializes in knee injuries in weekend warriors. His patients often call him from the ski slopes in Colorado, Whistler ,and Zermatt. Cherry is good at his craft. But his patients are even better at their craft — post-operative recovery. Cherry doesn’t actively seek such patients. His patients are selected for him by his zip code, reputation, long waiting list and Yelp.
Conclusion:
Simpson’s paradox — where the conclusions are actually, and precisely, the opposite of what is inferred from the data. That is, for example, when a study shows the superiority of an inferior treatment, and vice versa. he data release by ProPublica is a reservoir of Simpson’s paradox. This means when the data says “bad surgeon,” the surgeon might, in fact be a Top Gun — a technically-gifted, Morbidity Hunter — the last hope of the poor and sick.
Aren’t you intrigued and perturbed by this paradox? This means that data may not be just telling half-truths, but flat out lying.
The truth is if you have a great outcome, you think your surgeon is the best. If it is less than optimal there is a wide range of reactions. Some surgeons have great bedside manners...sometimes they get away with 'murder' or complications. Some surgeons have no bedside manners. These surgeons may leave patients in doubt, especially if their outcome is less than optimal.
The truth is if you have a great outcome, you think your surgeon is the best. If it is less than optimal there is a wide range of reactions. Some surgeons have great bedside manners...sometimes they get away with 'murder' or complications. Some surgeons have no bedside manners. These surgeons may leave patients in doubt, especially if their outcome is less than optimal.
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