Enhancing doctor-patient communication using large language models for pathology report interpretation
Abstract Background Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to c...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02838-z |
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| author | Xiongwen Yang Yi Xiao Di Liu Yun Zhang Huiyin Deng Jian Huang Huiyou Shi Dan Liu Maoli Liang Xing Jin Yongpan Sun Jing Yao XiaoJiang Zhou Wankai Guo Yang He WeiJuan Tang Chuan Xu |
| author_facet | Xiongwen Yang Yi Xiao Di Liu Yun Zhang Huiyin Deng Jian Huang Huiyou Shi Dan Liu Maoli Liang Xing Jin Yongpan Sun Jing Yao XiaoJiang Zhou Wankai Guo Yang He WeiJuan Tang Chuan Xu |
| author_sort | Xiongwen Yang |
| collection | DOAJ |
| description | Abstract Background Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients about their diagnosis and treatment options, potentially impacting patient outcomes such as understanding of their condition, treatment adherence, and overall satisfaction. Materials and methods This study analyzed text pathology reports from four hospitals between October and December 2023, focusing on malignant tumors. Using GPT-4, we developed templates for interpretive pathology reports (IPRs) to simplify medical terminology for non-professionals. We randomly selected 70 reports to generate these templates and evaluated the remaining 628 reports for consistency and readability. Patient understanding was measured using a custom-designed pathology report understanding level assessment scale, scored by volunteers with no medical background. The study also recorded doctor-patient communication time and patient comprehension levels before and after using IPRs. Results Among 698 pathology reports analyzed, the interpretation through LLMs significantly improved readability and patient understanding. The average communication time between doctors and patients decreased by over 70%, from 35 to 10 min (P < 0.001), with the use of IPRs. The study also found that patients scored higher on understanding levels when provided with AI-generated reports, from 5.23 points to 7.98 points (P < 0.001), with the use of IPRs. indicating an effective translation of complex medical information. Consistency between original pathology reports (OPRs) and IPRs was also evaluated, with results showing high levels of consistency across all assessed dimensions, achieving an average score of 4.95 out of 5. Conclusion This research demonstrates the efficacy of LLMs like GPT-4 in enhancing doctor-patient communication by translating pathology reports into more accessible language. While this study did not directly measure patient outcomes or satisfaction, it provides evidence that improved understanding and reduced communication time may positively influence patient engagement. These findings highlight the potential of AI to bridge gaps between medical professionals and the public in healthcare environments. |
| format | Article |
| id | doaj-art-39dbdbd25f7a47fbafcfe13d8c4296cb |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-39dbdbd25f7a47fbafcfe13d8c4296cb2025-01-26T12:36:52ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111610.1186/s12911-024-02838-zEnhancing doctor-patient communication using large language models for pathology report interpretationXiongwen Yang0Yi Xiao1Di Liu2Yun Zhang3Huiyin Deng4Jian Huang5Huiyou Shi6Dan Liu7Maoli Liang8Xing Jin9Yongpan Sun10Jing Yao11XiaoJiang Zhou12Wankai Guo13Yang He14WeiJuan Tang15Chuan Xu16Department of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Cardio-Thoracic Surgery, the Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Pathology, Guizhou Provincial People’s HospitalDepartment of Anesthesiology, the Third Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, Jiangxi Cancer HospitalDepartment of Radiology, Guizhou Provincial People’s HospitalDepartment of Medical Records and Statistics, Guizhou Provincial People’s HospitalNHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalDepartment of Thoracic Surgery, Guizhou Provincial People’s HospitalAbstract Background Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients about their diagnosis and treatment options, potentially impacting patient outcomes such as understanding of their condition, treatment adherence, and overall satisfaction. Materials and methods This study analyzed text pathology reports from four hospitals between October and December 2023, focusing on malignant tumors. Using GPT-4, we developed templates for interpretive pathology reports (IPRs) to simplify medical terminology for non-professionals. We randomly selected 70 reports to generate these templates and evaluated the remaining 628 reports for consistency and readability. Patient understanding was measured using a custom-designed pathology report understanding level assessment scale, scored by volunteers with no medical background. The study also recorded doctor-patient communication time and patient comprehension levels before and after using IPRs. Results Among 698 pathology reports analyzed, the interpretation through LLMs significantly improved readability and patient understanding. The average communication time between doctors and patients decreased by over 70%, from 35 to 10 min (P < 0.001), with the use of IPRs. The study also found that patients scored higher on understanding levels when provided with AI-generated reports, from 5.23 points to 7.98 points (P < 0.001), with the use of IPRs. indicating an effective translation of complex medical information. Consistency between original pathology reports (OPRs) and IPRs was also evaluated, with results showing high levels of consistency across all assessed dimensions, achieving an average score of 4.95 out of 5. Conclusion This research demonstrates the efficacy of LLMs like GPT-4 in enhancing doctor-patient communication by translating pathology reports into more accessible language. While this study did not directly measure patient outcomes or satisfaction, it provides evidence that improved understanding and reduced communication time may positively influence patient engagement. These findings highlight the potential of AI to bridge gaps between medical professionals and the public in healthcare environments.https://doi.org/10.1186/s12911-024-02838-zLarge language modelsDoctor-patient communicationSurgical oncology scenePostoperative pathology reports |
| spellingShingle | Xiongwen Yang Yi Xiao Di Liu Yun Zhang Huiyin Deng Jian Huang Huiyou Shi Dan Liu Maoli Liang Xing Jin Yongpan Sun Jing Yao XiaoJiang Zhou Wankai Guo Yang He WeiJuan Tang Chuan Xu Enhancing doctor-patient communication using large language models for pathology report interpretation BMC Medical Informatics and Decision Making Large language models Doctor-patient communication Surgical oncology scene Postoperative pathology reports |
| title | Enhancing doctor-patient communication using large language models for pathology report interpretation |
| title_full | Enhancing doctor-patient communication using large language models for pathology report interpretation |
| title_fullStr | Enhancing doctor-patient communication using large language models for pathology report interpretation |
| title_full_unstemmed | Enhancing doctor-patient communication using large language models for pathology report interpretation |
| title_short | Enhancing doctor-patient communication using large language models for pathology report interpretation |
| title_sort | enhancing doctor patient communication using large language models for pathology report interpretation |
| topic | Large language models Doctor-patient communication Surgical oncology scene Postoperative pathology reports |
| url | https://doi.org/10.1186/s12911-024-02838-z |
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