June - 202019treatment 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". It demonstrates the best combination of lower direct variable cost, length of stay, readmission rate, and mortality. We use Ayasdi's unsupervised learning algorhithm4to generate a CarePath from this treatment group. It tells us, "if you want future patients to be in this group, here are the things you need to do, and the timing and sequence with which to do them". We then edit the CarePath and deploy it, use it to make changes to corresponding admission and treatment order sets and then monitor adherence of our physician to the CarePath.While all of this is music to our CFO's ears, it is meaningless unless we can operationalize it. We, at Flagler, have been fortunate. Seven years ago, we formed our "PIT Crew" (Physician IT Crew). It is comprised of 20 physicians, one or two from every department in the hospital, who were given authority by the hospital board to make decisions concerning EMR workflow, order sets and orders. They meet regularly and review all things "EMR" and can make decisions or recommendations to individual departments or the medical staff in general. This team has been vital to our EMR success and forms the "cornerstone" of our project to reduce clinical variation. Two years ago, when I discovered Ayasdi, I brought a plan to my CEO who gave me the go-ahead for the Pilot. The PIT Crew was brought in at the very beginning of the process, as were several Informatics staff members with excellent SQL skills. Here is how we have operationalized our use of Ayasdi:1. We created 2,500 lines of SQL code to extract patient data from our EMR (Allscripts), our Analytics Platform (CPM), our surgical system (SIS), our Enterprise Data Warehouse and our financial system. These queries were parameterized so we can easily move from one diagnosis to another2. Once the data is extracted, one of our DBA's uploads the data to Ayasdi3. We perform several rounds of semantic and syntactic validation4. We run the "unsupervised learning" algorithm of Ayasdi to generate the treatment groups5. We compare the groups statistically using P-values and K-S test (Kolmogorov-Smirnov)6. We present the groups to the PIT Crew who review the data and decide on the "Goldilocks" treatment group7. Ayasdi is then used to generate the CarePath from the Goldilocks treatment group8. A PIT Crew member from the appropriate medical specialty and I edit the CarePath9. Admission and treatment order sets are changed to comply with the CarePath10. Education goes out to the medical staff11. Order Sets and CarePath are deployed12. Monitoring begins with the physician adherence report generated by Ayasdi13. Quarterly reports go the Administration, the Hospital Board, the ACO and physician staff showing changes in Direct Variable Cost, Length of Stay, Readmissions and MortalityWe completed our first CarePath, involving Pneumonia patients from 1/1/14 to 6/30/18, in nine weeks. We completed our second, Sepsis, in two weeks. We went on to complete COPD, Heart Failure and are now on our 5th, Total Hip. We plan to roll out 18 CarePaths in 18 months.We went live with our first CarePath, Pneumonia in August of 2018. Our results show a reduction in Direct Variable Cost of $1,073 per patient, a reduction in Length of Stay by 1.38 days and a reduction in Mortality from 1.5 percent to 0.0 percent. With our efforts, we appear to be on the right path to do our part in reducing clinical variation, as well as reducing unnecessary costs and eliminating medical errors. I believe that all hospitals, even sole community hospitals like ours, can do this well. Together we can improve the way healthcare is delivered for our patients, our communities and our nation! Michael C. Sanders
< Page 9 | Page 11 >