Healthcare Informatics: At UTMC, Data-Driven “Meds to Beds” Program Leads to 20 Percent Drop in Readmission Rates
University of Tennessee Medical Center (UTMC), based in Knoxville, Tennessee, is leveraging data analytics to improve medication compliance and patient outcomes. Through its use of a health care data integration and analytics platform, UTMC has reported a reduction in the readmissions rate among patients who were identified to be at high risk for readmission by more than 20 percent.
By improving medication delivery, UTMC’s new “meds to beds” program has resulted in gross margin per patient that was three times as high as that of the prior meds to beds program, according to a press release. These changes took place over four months, after UTMC implemented a data-driven concierge medication program. The system, which was developed in collaboration with AmerisourceBergen, automatically identifies at-risk patients, enabling on-site pharmacy technicians to efficiently engage those individuals one-on-one before discharge to improve medication adherence, a key factor in reducing the likelihood of readmission.
UTMC officials also have reported that the program has resulted in greater than 95 percent enrollment among previous pharmacy customers, with an additional 100 prescriptions filled each month, including an increase in high-value medications.
UTMC Director of Pharmacy Kim Mason said in a statement that the analytical insight provided by the technology tools allows clinicians to focus their efforts on populations who need it most.
Medication adherence during the first 30 days post-discharge is a primary driver of high readmission rates and undesirable patient outcomes, for which hospitals can be penalized as much as 3 percent of their total Medicare reimbursement. Adherence is especially problematic among patients with chronic diseases, with as many as 50 percent not taking their medications as directed.
To address the issue, UTMC’s on-site pharmacy instituted a “meds-to-beds” concierge program upon opening in 2013, in which pharmacy technicians visit patients prior to discharge to discuss the importance of taking their medications, make sure prescriptions are filled and answer any questions. Patients were informed of the “meds-to-beds” program upon admission, and it was up to the patient to decide whether or not to participate.
However, in September of 2016, UTMC leaders began to take a more active approach to the program. The hospital leveraged Dallas-based Loopback Analytics’ data analytics solution to more effectively identify at-risk patients and help pharmacy technicians prioritize those visits.
According to UTMC, the technology platforms helps to identify these high-risk patients by analyzing medication-specific risk factors, such as gaps in medication fill patterns prior to admission, the numbers of concurrent medications, social determinates and flagging of medications that are difficult for patients to manage, such as certain blood thinners. This data, along with existing risk score factors, including clinical markers like comorbidity, clinical encounter data and medical history, as well as demographic markers like age, payer status, and discharge location helped UTMC target both patients who were at high-risk for readmission due to their medication adherence vulnerability.