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 been skilled in medicine for 37 years, the last seven as a hospital CMIO, I can’t help but feel accountable for both these issues. The very fact that the majority folks try to deal with the movement of drugs from “volume to value", and lots of folks have a foot in each world and try to not fail! a number of us also are preparing for risk-sharing and if we are getting to do this, we must tackle the difficulty of Clinical Variation, or prepare to fail! during this article, I will be able to share how we, at Flagler Hospital, are reducing clinical variation to reinforce patient safety, improve our quality, while reducing the value of the care we offer.
Hospitals are trying to deal with this for a real while, usually with energetic clinical staff. Optimistic administrations have done the simplest they might, with chart reviews and data extractions. But these efforts are biased by the alternatives of what they decided to watch, and therefore the results are usually months old by the time the info is analyzed.
Over the past few years, we've had enormous advances within the hardware and software wont to store our patient data. Advances in AI have made it possible for us to seem in the least the info, allowing it to inform us what we've done well and where we've fallen short. It can now do that during a matter of minutes, not months or years.
At Flagler Hospital we've chosen to use AI software advanced by Ayasdi, which uses a particular branch of mathematics called Topology. This software allows us to seem in the least our patient data chronologically, creating a separately flagged event for each order, medication administration, lab result, and every one patient care tasks. In other words, everything that occurred to the patient. It then groups the patients into “treatment groups” that supported their similar care. Next, it shows us the Direct Variable Cost, length of stay, and co-morbid conditions, allowing us to look at and identify any statistical differences in these groups.