Let’s start with acknowledging that Medical Errors are now the 3rd leading cause of death in the USA, having surpassed COPD. In addition, studies have shown us that 30 percent of all medical care costs are unnecessary. While clinical variation is not the only reason for these two issues, it is a significant contributor. If eliminating medical errors and unnecessary medical care do not form the basis for a “Just Cause Initiative”, I don’t know what does.
As someone who has practiced medicine for 37 years, the last seven as a hospital CMIO, I can’t help but feel responsible for both these issues. Couple this with the fact that most of us are trying to cope with the movement of medicine from “volume to value", and many of us have a foot in each world and are trying not to fail! Some of us are also preparing for risk sharing and if we are going to do that, we must tackle the issue of Clinical Variation, or prepare to fail! In this article, I will share how we, at Flagler Hospital, are reducing clinical variation to enhance patient safety, improve our quality, while reducing the cost of the care we provide.
Hospitals have been trying to address this for a very long time, usually with energetic clinical staff. Optimistic administrations have done the best they could, with chart reviews and data extractions. But these efforts are biased by the choices of what they decided to observe, and the results are usually months old by the time the data is analyzed.
Over the past few years, we have had enormous advances in the hardware and software used to store our patient data. Advances in Artificial Intelligence have made it possible for us to look at all the data, allowing it tell us what we have done well and where we have fallen short. It can now do this in a matter of minutes, not months or years.
At Flagler Hospital we have chosen to use AI software developed by Ayasdi, which uses a particular branch of mathematics called Topology. This software allows us to look at all our patient data chronologically, creating a separately flagged event for every order, medication administration, lab result, and all patient care tasks. In other words, everything that happened to the patient. It then groups the patients into “treatment groups” based on their similar care. Next, it shows us the Direct Variable Cost, length of stay, and co-morbid conditions, allowing us to examine and identify any statistical differences in these groups. Ayasdi also allows us to create our own “User Defined Variables” for additional, customized analysis. With these, we can further analyze our treatment groups based on factors such as age, gender, lab value result or whether the patient came from home, a SNF or another medical facility.
We select the best treatment group, known as our “Goldilocks Group”.