Assessing Pharmacists’ Use and Perception of AI Chatbots in Pharmacy Practice: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The use of artificial intelligence (AI)-based large language model chatbots such as ChatGPT has become increasingly popular in many disciplines. However, concerns exist regarding ethics, legal considerations, accuracy, and reproducibility with its use in health care practice, education, and research. OBJECTIVE: This study aimed to assess current perceptions and use of AI chatbots in pharmacy practice from the perspective of pharmacist preceptors and determine factors that may influence the use of AI chatbots in practice. METHODS: A cross-sectional survey of pharmacy practice preceptors from Indiana, Illinois, and Michigan was conducted using the validated Technology Acceptance Model Edited to Assess ChatGPT Adoption (TAME-ChatGPT) survey tool to collect information regarding current use of AI chatbots and factors associated with use, including ease of use, perceived risk, technology or social influences, anxiety, and perceived usefulness. RESULTS: A total of 194 responses (194/1877, 10.34% response rate) were received. Approximately one-third (n=59, 30.4%) of respondents reported having used an AI chatbot, with 51.5% (n=100) indicating that they planned to start or would continue using chatbots in the future. In practice, common uses for AI chatbots included summarizing information (n=90, 46.4%), letter of recommendation writing (n=64, 32.9%), and obtaining disease state information (n=63, 32.5%). The 2 main constructs associated with the use of chatbots identified from the TAME-ChatGPT tool included perceived risk of using AI and attitude toward AI. Factors that predicted pharmacists' current use of AI chatbots included positive attitude toward technology (odds ratio [OR] 3.64, 95% CI 2.08-6.36), coworker use of AI (OR 7.41, 95% CI 2.64-20.8), and working in academia (OR 5.62, 95% CI 1.30-24.23). CONCLUSIONS: Most pharmacist respondents had not used an AI chatbot and were unlikely to make patient care decisions based on information from a chatbot. The TAME-ChatGPT survey is validated for assessing chatbot use and attitudes among pharmacists, and future studies using this survey tool can guide the implementation of chatbots into pharmacy practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".