A practical look at redesigning tasks and integrating artificial intelligence to improve efficiency and patient outcomes
Workflow optimization is increasingly recognized as essential for improving efficiency, worker satisfaction, and the effectiveness of service delivery and patient outcomes. Workflow is defined as a system in which a set of tasks is performed in a specific order while accounting for resources to achieve specific goals.1 Its principal goals are to ensure operational efficiency and safety of employees and patients. With emerging technologies like artificial intelligence (AI) and machine learning models, there is an opportunity to optimize patient safety, inventory management, administrative workload, and even clinical decision-making.
Workflow optimization can be achieved through various approaches, including restructuring of personnel or role delegation and the integration of technologies. Opportunities to optimize a pharmacy’s workflow are not limited to software or mechanical solutions. Strategic reallocation of a technician's or a pharmacist's duties, particularly in an administrative setting, will significantly improve efficiency and quality without altering regulatory requirements. By reducing pharmacists’ nonclinical and redundant tasks, more time can be dedicated to clinical decision-making, patient counseling, and business- or systems-level responsibilities in community and hospital settings.2
In a specific workflow redesign described by Sparkman et al, redesigning technician responsibilities across multiple states enabled pharmacists and technicians to contribute more meaningfully to patient care.2,3 In states permitting advanced technicians to perform tasks such as accepting verbal orders from providers for previously prescribed medications, administering immunizations, and managing refill authorizations, workflow efficiency improved while maintaining patient safety. These responsibilities reduce task redundancy, allowing pharmacists to focus on medication therapy management, clinical interventions, and interdisciplinary collaboration.
The effectiveness of workflow optimization depends on pharmacy-specific factors, including the pharmacy's physical layout, service volume, processes, and capacity. The ability to shift within workflow design is vital, particularly when unexpected changes occur. A common example is inventory management. In many pharmacies, the weekly orders arrive on predictable schedules (eg, Mondays and Thursdays), requiring a dedicated technician staff to manage receiving, stocking, and reconciliation of the order. If a scheduled technician is unavailable due to illness or other contingency, the management team would need to reorganize the schedule to prevent delays in patient care. Management would need to establish command intent before any delay in duties to reinforce timely completion of higher-priority tasks.4 This concept keeps the business workflow at an optimal pace to prioritize patient care.
However, as times change, a business’s operations must also adapt. These factors evoke the need for careful job analysis in an organization. A job analysis examines all the work that needs to be accomplished on a daily basis, then considers the labor capital available to perform all of those tasks. This enables the creation of job descriptions that prioritize responsibilities for each person, thus strengthening role clarity and mitigating ambiguities (eg, whose turn is it to do what and when).
Implementing new technology, such as AI, is going to happen in pharmacy, whether for good or bad. The potential time savings and workflow optimization that AI can provide in inventory management are among the most impactful aspects of workflow optimization. Through predictive modeling, AI can help forecast the optimal amount of inventory that should be stocked.5 Other nonclinical tasks, such as staff scheduling, email communications, and chatbots, can also be implemented for greater efficiency.6 On the other hand, AI tools could potentially alienate some customers who prefer human interaction in their pharmacy experience.
Shawna Casias and Sukhvinder Nijjar are PharmD candidates at the Touro University California College of Pharmacy
Shane Desselle, RPh, PhD, is a professor of social and behavioral pharmacy at the Touro University California College of Pharmacy.
Clinically, AI is still not widely adopted, offering opportunities for future optimization. AI’s current utilization focuses more on predictive outcomes than on clinical decision-making, which is appropriate considering safety concerns. For example, AI algorithms have been utilized in the emergency departments of the UC San Diego Health System to predict sepsis and have demonstrated significant reduction in mortality.7 Without even considering the potential new services that machine learning technologies can help develop and implement, the reduction in operational cost and time alone is already a draw for innovative pharmacies.
Workflow optimization is a multifaceted process with significant impacts on the continued operations of pharmacy organizations. From small changes to organization-altering technologies, there are many ways to optimize workflow. Increasing and expanding technician duties will enable better patient care in all pharmacy practices, as well as increase optimization of workflow. AI can assist in various aspects, although it is no panacea. The effective pharmacy manager or pharmacist appreciates the value of workflow optimization and recognizes the need for traditional strategies like job analysis, assisted by emerging technologies.
