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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2402.01711 |
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| _version_ | 1866909091180314624 |
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| author | Schmiedmayer, Paul Rao, Adrit Zagar, Philipp Ravi, Vishnu Zahedivash, Aydin Fereydooni, Arash Aalami, Oliver |
| author_facet | Schmiedmayer, Paul Rao, Adrit Zagar, Philipp Ravi, Vishnu Zahedivash, Aydin Fereydooni, Arash Aalami, Oliver |
| contents | Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs). Materials and Methods: The research involved developing LLM on FHIR, an open-source mobile application allowing users to interact with their health records using LLMs. The app is built on Stanford's Spezi ecosystem and uses OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient dataset and evaluated by medical experts to assess the app's effectiveness in increasing health literacy. The evaluation focused on the accuracy, relevance, and understandability of the LLM's responses to common patient questions. Results: LLM on FHIR demonstrated varying but generally high degrees of accuracy and relevance in providing understandable health information to patients. The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles. However, challenges included variability in LLM responses and the need for precise filtering of health data. Discussion and Conclusion: LLMs offer significant potential in improving health literacy and making health records more accessible. LLM on FHIR, as a pioneering application in this field, demonstrates the feasibility and challenges of integrating LLMs into patient care. While promising, the implementation and pilot also highlight risks such as inconsistent responses and the importance of replicable output. Future directions include better resource identification mechanisms and executing LLMs on-device to enhance privacy and reduce costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM on FHIR -- Demystifying Health Records Schmiedmayer, Paul Rao, Adrit Zagar, Philipp Ravi, Vishnu Zahedivash, Aydin Fereydooni, Arash Aalami, Oliver Computers and Society Artificial Intelligence Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs). Materials and Methods: The research involved developing LLM on FHIR, an open-source mobile application allowing users to interact with their health records using LLMs. The app is built on Stanford's Spezi ecosystem and uses OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient dataset and evaluated by medical experts to assess the app's effectiveness in increasing health literacy. The evaluation focused on the accuracy, relevance, and understandability of the LLM's responses to common patient questions. Results: LLM on FHIR demonstrated varying but generally high degrees of accuracy and relevance in providing understandable health information to patients. The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles. However, challenges included variability in LLM responses and the need for precise filtering of health data. Discussion and Conclusion: LLMs offer significant potential in improving health literacy and making health records more accessible. LLM on FHIR, as a pioneering application in this field, demonstrates the feasibility and challenges of integrating LLMs into patient care. While promising, the implementation and pilot also highlight risks such as inconsistent responses and the importance of replicable output. Future directions include better resource identification mechanisms and executing LLMs on-device to enhance privacy and reduce costs. |
| title | LLM on FHIR -- Demystifying Health Records |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2402.01711 |