This post originally appeared on HIStalk
Earlier today I had a discussion with a critical access hospital. I discovered that every doctor at the hospital had developed his or her own insulin sliding scale. The sliding scale paper forms were actually titled "Smith’s Sliding Scale" and "Johnson’s Sliding Scale" (names changed to maintain anonymity). All on-staff physicians completed medical school within 10 years of one another, but none of them could agree on a standard sliding scale.
The hospital deployment team encouraged the hospital to standardize. So we Googled insulin sliding scales. We found that virtually no one on the Internet agrees what the standard insulin sliding scale should be. We did find a sliding scale on CMS’s website, but there was so much other (mis)information on the web, I could hardly consider CMS’s sliding scale "official."
We herded sheep and got all the doctors in a room together to agree upon a standard sliding scale for the hospital. I was curious to learn why they preferred different sliding scales, and what evidence each had to support their claims. Not a single doctor tried to claim their sliding scale was a best practice, or that it was even backed up by any clinical evidence. Each doctor simply stated that they had been taught their respective sliding scales long ago and that everyone else’s was inferior to their own.
Everyone in the industry knows that EHR deployments bring about major workflow changes. Depending on the hospital’s previous policies and the flexibility of the EHR being installed, these changes can be dramatic. One of the most frequent change management challenges I’ve witnessed has been standardization where it didn’t previously exist. EHRs can enforce organization-wide standardization where it was previously impossible to enforce. Before, physicians could say, "I don’t care about the other doctors, my template / order / sliding scale / frequency / security / whatever needs to be different." Now it’s the other way around. "But the system doesn’t support that, so you need to come to an agreement with the others." It’s pretty hard for doctors to argue against that. In response, many doctors have quit or retired rather than conform.
EHRs present opportunities to standardize care within and across healthcare organizations and providers. This is not necessarily a good thing. We need to continually experiment to figure out what really is the best solution, because most of the time, we don’t actually know what the best solution is. It takes 20 years to standardize medical best practices because we need to experiment and collect huge amounts of data before we can be certain in medicine. How can we expedite that process?
We’ll experiment, collect data, and analyze it in literally every conceivable way. Everyday there are hundreds of thousands of clinical encounters that each generate dozens of data points. With enough data and the right tools, we will correlate every data point. Through raw statistical analysis, we’ll be able to predict diagnoses and outcomes with high levels of confidence. We’ll suggest diagnoses with confidence intervals to doctors as they enter data into EHRs.
When every doctor is presented a probability distribution of diagnoses backed up by billions of data points, they will usually agree with the computer. In time, opinions and practices may slowly converge.