Leveraging LLM to identify missed information in patient-physician communication: improving healthcare service quality
Bibliographic record
Abstract
Background and objective: Electronic medical records (EMRs) have significantly changed the dynamics of physician-patient interactions, leading to a shift in communication patterns. Although various studies have developed guidelines for these new dynamics, different EMRs result in different modes of interaction, which can contribute to missed information during clinical encounters. Therefore, this study aims to develop a method that can automate the identification process of missed information to increase patient safety and satisfaction. Methods: A total of 98 transcripts of clinical consultations from two primary care clinics in the United States were used for identifying missed information and patient unsatisfactory factors. We first examine those factors through ordinal logistic regression. Then we leveraged large language model (Phi-3.5) to develop the automation model for identifying missed information of physicians. Results: <0.001) can significantly increase the level of patient satisfaction. And our model has an average accuracy of 90.09% with F1-score of 93.75% on identifying missed information during clinical practices in primary care. Conclusion: This study demonstrates the potential of automated analysis using Phi-3.5 to evaluate the identification of communication gaps in physician-patient interactions, ultimately enhancing patient safety and satisfaction. Further research is needed to refine this approach and explore its application across diverse healthcare settings.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".