Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach
Abstract Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate c...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01039-2 |
| _version_ | 1827276902153322496 |
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| author | Rohaid Ali Ian D. Connolly Oliver Y. Tang Fatima N. Mirza Benjamin Johnston Hael F. Abdulrazeq Rachel K. Lim Paul F. Galamaga Tiffany J. Libby Neel R. Sodha Michael W. Groff Ziya L. Gokaslan Albert E. Telfeian John H. Shin Wael F. Asaad James Zou Curtis E. Doberstein |
| author_facet | Rohaid Ali Ian D. Connolly Oliver Y. Tang Fatima N. Mirza Benjamin Johnston Hael F. Abdulrazeq Rachel K. Lim Paul F. Galamaga Tiffany J. Libby Neel R. Sodha Michael W. Groff Ziya L. Gokaslan Albert E. Telfeian John H. Shin Wael F. Asaad James Zou Curtis E. Doberstein |
| author_sort | Rohaid Ali |
| collection | DOAJ |
| description | Abstract Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers. |
| first_indexed | 2024-04-24T07:11:52Z |
| format | Article |
| id | doaj.art-6ac331ae728a4e08b70ef37ec85b97d3 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-04-24T07:11:52Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-6ac331ae728a4e08b70ef37ec85b97d32024-04-21T11:31:12ZengNature Portfolionpj Digital Medicine2398-63522024-03-01711610.1038/s41746-024-01039-2Bridging the literacy gap for surgical consents: an AI-human expert collaborative approachRohaid Ali0Ian D. Connolly1Oliver Y. Tang2Fatima N. Mirza3Benjamin Johnston4Hael F. Abdulrazeq5Rachel K. Lim6Paul F. Galamaga7Tiffany J. Libby8Neel R. Sodha9Michael W. Groff10Ziya L. Gokaslan11Albert E. Telfeian12John H. Shin13Wael F. Asaad14James Zou15Curtis E. Doberstein16Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Neurosurgery, Massachusetts General HospitalDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Dermatology, The Warren Alpert Medical School of Brown UniversityDepartment of Neurosurgery, Brigham and Women’s HospitalDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Surgery & Division of Cardiothoracic Surgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityRatcliffe Harten Galamaga LLPDepartment of Dermatology, The Warren Alpert Medical School of Brown UniversityDepartment of Surgery & Division of Cardiothoracic Surgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Neurosurgery, Brigham and Women’s HospitalDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartment of Neurosurgery, Massachusetts General HospitalDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityDepartments of Electrical Engineering, Biomedical Data Science, and Computer Science, Stanford UniversityDepartment of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown UniversityAbstract Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.https://doi.org/10.1038/s41746-024-01039-2 |
| spellingShingle | Rohaid Ali Ian D. Connolly Oliver Y. Tang Fatima N. Mirza Benjamin Johnston Hael F. Abdulrazeq Rachel K. Lim Paul F. Galamaga Tiffany J. Libby Neel R. Sodha Michael W. Groff Ziya L. Gokaslan Albert E. Telfeian John H. Shin Wael F. Asaad James Zou Curtis E. Doberstein Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach npj Digital Medicine |
| title | Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach |
| title_full | Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach |
| title_fullStr | Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach |
| title_full_unstemmed | Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach |
| title_short | Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach |
| title_sort | bridging the literacy gap for surgical consents an ai human expert collaborative approach |
| url | https://doi.org/10.1038/s41746-024-01039-2 |
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