Clinical decision-making autonomy of pharmacists in decentralized models of healthcare administration and risk management

Abstract

As healthcare systems globally transition toward patient-centered and decentralized care models, the role of pharmacists has evolved from traditional dispensing functions to more autonomous, clinically integrated responsibilities. This paradigm shift, driven by the need for efficiency, accessibility, and personalized care, positions pharmacists as critical stakeholders in therapeutic decision-making, especially within primary and community-based health systems. In decentralized healthcare structures, such as those found in integrated care networks and rural outreach programs, pharmacists are increasingly responsible for clinical judgment, medication optimization, patient education, and adverse drug reaction monitoring—functions traditionally reserved for physicians. This study explores the extent and determinants of clinical decision-making autonomy among pharmacists within decentralized healthcare models. It examines how organizational structure, regulatory frameworks, risk management policies, and interprofessional collaboration impact pharmacists’ ability to make independent clinical decisions. Particular attention is paid to the balance between autonomy and accountability, highlighting potential risks such as therapeutic errors and liability concerns, alongside opportunities for improving medication adherence and reducing hospital readmissions. Using a mixed-methods approach involving policy analysis, structured interviews, and clinical case reviews, the study uncovers significant variation in autonomy across regions and care settings. It proposes a framework for risk-informed autonomy, whereby pharmacists operate with expanded clinical responsibility under well-defined governance and support systems. Ultimately, this research underscores the importance of redefining pharmacists’ roles in modern health systems and offers strategic recommendations for empowering them within decentralized models without compromising patient safety or care quality.