DALLAS, May 23, 2017 /PRNewswire/ — Loopback Analytics’ health care data integration and analytics platform has enabled the University of Tennessee Medical Center (UTMC) to reduce readmission rate among patients who were identified to be at high risk for readmission by more than 20 percent. The proactive approach to medication delivery also resulted in a gross margin per patient that was three times as high as that of the prior meds to beds program. These changes took place in just four months, after UTMC implemented a data-driven, proactive 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.

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. In September of 2016, however, UTMC began to take a more active approach to the program. The hospital leveraged Loopback Analytics’ data analytics solution to proactively identify at-risk patients and help pharmacy technicians prioritize those visits.

These high risk patients are identified 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.

Loopback also provides a workflow system and tablet-based interface that technicians use to engage with patients at bedside, as well as a mechanism for post-discharge tracking that can issue alerts to the pharmacy if high risk patients do not fill their medications in a hospital or community pharmacy, in order to facilitate follow up calls. Built-in analytics and metrics dashboards allow UTMC to measure the economic and clinical impact of their efforts.
In addition to the dramatic reduction in readmission rate for high risk patients and 3X increase in gross margins as opposed to the more passive “meds-to-beds” program, UTMC saw greater than 95 percent enrollment among previous pharmacy customers, with an additional 100 prescriptions filled each month, including an increase in high-value medications.
“We are extremely pleased with the results of this program, and I would absolutely recommend this solution to other hospitals,” said UTMC Director of Pharmacy Kim Mason. “The analytical insight Loopback allows us to focus our efforts on populations who need it most.”

“A data-driven meds to beds program can improve both pharmacy economics and clinical outcomes,” said Loopback CEO Neil Smiley. “By leveraging data and technology, UTMC has positioned their onsite pharmacy to be a strategic resource in improving medication adherence for high risk patients.”

To learn more about Loopback’s health care data analytics solutions, visit www.loopbackanalytics.com.