Optimizing Pharmacist-Led Medication Therapy Management Using Predictive Analytics: A U.S. Real- World Study on Chronic Disease Outcome

Abstract

Pharmacist-led Medication Therapy Management (MTM) plays a crucial role in improving patient outcomes
for chronic conditions such as diabetes, hypertension, and hyperlipidemia. Integrating predictive analytics into
MTM can greatly enhance its effectiveness by identifying high-risk patients, improving medication adherence,
and enabling more targeted interventions. (Watanabe et al., 2018). This is a paper that examines a predictive
model study and how it is applied under MTM programs in the U.S. health care system using actual patient
data to evaluate how it has influenced clinical indicators and healthcare utilization. According to research
findings, predictive analytics help pharmacists provide more personalized care, which reduces hospitalizations
as well as better control of chronic disease indicators such as HbA1c and systolic blood pressure (Farley
et al., 2017; Grizzle et al., 2020). The addition of data-enabled functionality to MTM processes facilitating more
proactive interventions is what allows population health approaches to evolve towards a more preventive and
precision care model (Choudhry et al., 2022). These findings denote the transformative prospects of integrating
the expertise of pharmacists with machine learning-based tools in the management of chronic illness and the
necessity of expanding adoption of predictive analytics in the framework of pharmacist-administered care.