Leveraging LLM to identify missed information in patient-physician communication: improving healthcare service quality

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 We show that showing care and empathy to patients ( β =1.283, OR = 3.609 [95% CI: 1.836, 7.091], p <0.001) and explaining things clearly to patients ( β =1.620, OR = 5.051 [95% CI: 2.138, 11.938], p <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.