Home 2018 Abstracts Implementation of Criteria to Predict Medication-Related Readmissions within High-Risk Patients

Implementation of Criteria to Predict Medication-Related Readmissions within High-Risk Patients

Belinda Mang (Parkland Health & Hospital System), Kristin S. Alvarez (Parkland Health and Hospital System), Kristy Vo (Parkland Health & Hospital System), Kathryn Johnson (Texas Tech University Health Sciences Center)

Background

The Transitional Care Unit (TCU) at Parkland is a program identifying high-risk patients to improve patient access, promote engagement, and reduce 30-day readmissions. A nurse case manager selects patients utilizing predictive analytics. A pharmacist performs a comprehensive assessment of medications at discharge, reconciles discrepancies or problems, and provides counseling.

Objectives

To balance the value of this service with resource limitations in a large safety-net hospital, a stratification was developed to classify patients as high-, moderate-, or low-risk for medication-related readmissions, which subsequently correlated with the intensity of pharmacist intervention. The aim of this review is to characterize 30-day medication-related readmissions in TCU patients receiving pharmacist intervention.

Methods

We undertook a retrospective chart review of patients who received pharmacist intervention post-implementation between October and December 2017. Readmissions were reviewed for primary diagnosis, contribution of medication-related problems, and readmission setting (inpatient, observation, or emergency department/urgent care).

Results

Three-hundred and sixty-three patients were eligible with most patients categorized as high-risk (68.3%) and receiving intensive pharmacist intervention, followed by moderate- (19.3%) and low-risk (12.4%). Overall, 42 (11.6%) were readmitted within 30 days and 21 were associated with a medication, resulting in a medication-related readmission rate of 5.8%. This was primarily driven by the high-risk (6.9%) group as compared to moderate-risk (4.3%) and low-risk (2.2%) groups, with 66.7% related to noncompliance. No discernible trend was seen in observation and emergency department/urgent care revisits with 2.4%, 1.4%, and 2.2% medication-related revisit rates in the high-, moderate-, and low-risk groups, respectively, despite an all-cause revisit rate of 9.1%.

Conclusion

Our findings suggest that risk categorization may predict medication-related readmissions as observed by down-trending readmission rates. While greater readmissions were observed in the high-risk group, no control group was available to compare the impact of pharmacist intervention.

Implications for the Patient

This data provides a foundation to continue developing more robust stratification processes, ultimately providing targeted pharmaceutical interventions in a resource-appropriate manner to achieve and sustain improved patient outcomes.