Pharmaceutical Analysis of Inpatient Prescriptions: Systematic Observation of Hospital Pharmacists' Practices in the Early User-Centered Design Phase.

Background: The health care sector's digital transformation has accelerated, yet adverse drug events continue to rise, posing significant clinical and economic challenges. Clinical decision support systems (CDSSs), particularly those related to medication, are crucial for improving patient care, identifying drug-related problems, and reducing adverse drug events. Hospital pharmacists play a key role in using CDSSs for patient management and safety. Human factors and ergonomics (HFE) methods are essential for designing effective, human-centered CDSSs. HFE involves 3 phases-exploration, design, and evaluation-with exploration being critical yet often overlooked in the literature. For medication-related CDSSs, understanding hospital pharmacists' tasks and challenges is vital for creating user-centered solutions.

Objective: This study aimed to explore the actual practices and identify the needs of hospital pharmacists analyzing electronic prescriptions. This study focused on the preliminary stage of the user-centered design of a pharmacist-centered CDSS.

Methods: The study involved observing 16 pharmacists across 5 hospitals in mainland France (a university hospital, 2 large general hospitals, a smaller general hospital, and a specialized clinic). Pharmacists were selected regardless of expertise. The observation method-systematic in situ observation with shadowing posture-involved following pharmacists as they analyzed prescriptions. Researchers recorded activities, tools used, verbalizations, behaviors, and interruptions, using an observation grid. Data analysis focused on modeling pharmacists' cognitive work, categorizing activities by action type, specificity, and information source. Sequential time data analysis and distance matrices were used to generate hierarchical clustering and identify similarity groups among the pharmacists' analyses. Each group was described using its typical sequences of analysis and related covariates.

Results: In total, 16 pharmacists analyzed and validated electronic prescriptions for 140 patients, averaging 5.48 minutes per patient. They spend 91% of their time searching for information rather than transmitting it. Most information comes from the list of prescriptions, but it is the time spent in electronic medical records (EMRs) that dominates at the heart of the analysis. Pharmaceutical interventions are most frequently transmitted in the last third of the sequence. The pharmaceutical analyses were grouped into 4 clusters: (cluster A, 22%) interventionist clinical analysis with extensive crossing of various sources of information and almost systematic pharmaceutical interventions; (cluster B, 52%) most common clinical analysis focusing on EMRs and biology results; (cluster C, 13%) logistical analysis, focusing on the pharmacy workflow and the medication circuit; and (cluster D, 13%) quick, trivial analyses based exclusively on the list of prescriptions.

Conclusions: The pharmaceutical analysis process is complex and multifaceted. Pharmacists are detectives, accessing a wealth of information to discriminate drug-related problems and respond accordingly. They also carry out different types of analysis, which lead to different needs and require different solutions from CDSSs. This exploratory study is an essential prerequisite for meeting the challenge of designing tools to support pharmaceutical analysis and pharmacists.