Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia
One of the key issues contributing to inefficiency in Puskesmas is the time-consuming nature of doctor-patient interactions. Doctors need to conduct thorough consultations, which include diagnosing the patient's condition, providing treatment advice, and transcribing detailed notes into medical...
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Format | Journal Article |
Language | English |
Published |
25.09.2024
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Abstract | One of the key issues contributing to inefficiency in Puskesmas is the
time-consuming nature of doctor-patient interactions. Doctors need to conduct
thorough consultations, which include diagnosing the patient's condition,
providing treatment advice, and transcribing detailed notes into medical
records. In regions with diverse linguistic backgrounds, doctors often have to
ask clarifying questions, further prolonging the process. While diagnosing is
essential, transcription and summarization can often be automated using AI to
improve time efficiency and help doctors enhance care quality and enable early
diagnosis and intervention. This paper proposes a solution using a localized
large language model (LLM) to transcribe, translate, and summarize
doctor-patient conversations. We utilize the Whisper model for transcription
and GPT-3 to summarize them into the ePuskemas medical records format. This
system is implemented as an add-on to an existing web browser extension,
allowing doctors to fill out patient forms while talking. By leveraging this
solution for real-time transcription, translation, and summarization, doctors
can improve the turnaround time for patient care while enhancing the quality of
records, which become more detailed and insightful for future visits. This
innovation addresses challenges like overcrowded facilities and the
administrative burden on healthcare providers in Indonesia. We believe this
solution will help doctors save time, provide better care, and produce more
accurate medical records, representing a significant step toward modernizing
healthcare and ensuring patients receive timely, high-quality care, even in
resource-constrained settings. |
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AbstractList | One of the key issues contributing to inefficiency in Puskesmas is the
time-consuming nature of doctor-patient interactions. Doctors need to conduct
thorough consultations, which include diagnosing the patient's condition,
providing treatment advice, and transcribing detailed notes into medical
records. In regions with diverse linguistic backgrounds, doctors often have to
ask clarifying questions, further prolonging the process. While diagnosing is
essential, transcription and summarization can often be automated using AI to
improve time efficiency and help doctors enhance care quality and enable early
diagnosis and intervention. This paper proposes a solution using a localized
large language model (LLM) to transcribe, translate, and summarize
doctor-patient conversations. We utilize the Whisper model for transcription
and GPT-3 to summarize them into the ePuskemas medical records format. This
system is implemented as an add-on to an existing web browser extension,
allowing doctors to fill out patient forms while talking. By leveraging this
solution for real-time transcription, translation, and summarization, doctors
can improve the turnaround time for patient care while enhancing the quality of
records, which become more detailed and insightful for future visits. This
innovation addresses challenges like overcrowded facilities and the
administrative burden on healthcare providers in Indonesia. We believe this
solution will help doctors save time, provide better care, and produce more
accurate medical records, representing a significant step toward modernizing
healthcare and ensuring patients receive timely, high-quality care, even in
resource-constrained settings. |
Author | Arief, Mansur M Irfan, Azmul Asmar Khatim, Nur Ahmad |
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BackLink | https://doi.org/10.48550/arXiv.2409.17054$$DView paper in arXiv |
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Snippet | One of the key issues contributing to inefficiency in Puskesmas is the
time-consuming nature of doctor-patient interactions. Doctors need to conduct
thorough... |
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SubjectTerms | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Sound |
Title | Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia |
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