The day after NBC releases a story on a ‘ground-breaking’ observational study demonstrating caramel macchiatas reduce the risk of death, everyone expects physicians to be experts on the subject. The truth is that most of us hope John Mandrola has written a smart blog on the topic so we know intelligent things to tell patients and family members.
A minority of physicians actually read the original study, and of those who read the study, even fewer have any real idea of the statistical ingredients used to make the study. Imagine not knowing whether the sausage you just ate contained rat droppings. At least there is some hope the tongue may provide some objective measure of the horror within.
Data that emerges from statistical black boxes typically have no neutral arbiter of truth. The process is designed to reveal from complex data sets, that which cannot be readily seen. The crisis created is self-evident: With no objective way of recognizing reality, it is entirely possible and inevitable for illusions to proliferate.
In this episode of Firing Line, Saurabh Jha (aka @RogueRad), has a conversation with Professor Brian Nosek, a metaresearcher and co-founder of Center for Open Science.
They discuss the implications of this study, which showed that there was a range of analytical methods when interrogating the database to answer a specific hypothesis: are soccer referees more likely to give red cards to dark skinned players? What is the significance of the variation? Does the variation in analysis explain the replication crisis?
In an effort to help women make informed decisions about where to deliver their babies, we set out to collect a comprehensive, nationwide database of hospitals’ C-section rates. Knowing that the federal government mandates surveillance and reporting of vital statistics through the National Vital Statistics System, we contacted all 50 states’ (+Washington D.C.) Departments of Public Health (DPH) asking for access to de-identified birth data from all of their hospitals. What we learned might not surprise you — the lack of transparency in the United States healthcare system extends to quality information, and specifically C-section data. Continue reading…
Among many healthcare providers, it’s been long-standing conventional wisdom (CW) that hoarding patient data is an effective business strategy to lock-in patients — “He who holds the data, wins”. However…we’ve never seen any evidence that this actually works…have you?
We’re here to challenge CW. In this article we’ll explore the rationale of “hoarding as business strategy”, review evidence suggesting it’s still prevalent, and suggest 7 reasons why we believe it’s a lousy business strategy:
Data Hoarding Doesn’t Work — It Doesn’t Lock-In Patients or Build Affinity
Convenience is King in Patient Selection of Providers
Loyalty is Declining, Shopping is Increasing
Providers Have a Decreasingly Small “Share” of Patient Data
Providers Don’t Want to Become a Lightning Rod in the “Techlash” Backlash
Hoarding Works Against Public Policy and the Law
Providers, Don’t Fly Blind with Value-Based Care
In the video below, Dr. Harlan Krumholz of Yale University School of Medicine capsulizes the rationale of hoarding as business strategy.
We encourage you to take a minute to listen to Dr. Krumholz, but if you’re in a hurry we’ve abstracted the most relevant portions of his comments:
“The leader of a very major healthcare system said this to me confidentially on the phone… ‘why would we want to make it easy for people to get their health data…we want to keep the patients with us so why wouldn’t we want to make it just a little more difficult for them to leave.’ …I couldn’t believe it a physician health care provider professional explaining to me the philosophy of that health system.”