Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model

• URL: http://arxiv.org/abs/2309.09357v5
• Date: Sat, 3 Feb 2024 06:32:56 GMT
• Title: Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
• Authors: Ziqi Yang, Xuhai Xu, Bingsheng Yao, Shao Zhang, Ethan Rogers, Stephen Intille, Nawar Shara, Guodong Gordon Gao, Dakuo Wang
• Abstract summary: We built an LLM-powered communication system, Talk2Care, for older adults and healthcare providers. For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered VA interface for effective information collection. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time.
• Score: 29.982507402325396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults (N=10) and healthcare providers (N=9) to understand their needs and opportunities for LLMs in patient-provider asynchronous communication. Based on the insights, we built an LLM-powered communication system, Talk2Care, and designed interactive components for both groups: (1) For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered VA interface for effective information collection. (2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time. We envision our work as an initial exploration of LLMs' capability in the intersection of healthcare and interpersonal communication.

Related papers

• Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health [1.8772687384996551]
  Large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. We showcase the power of LLMs in handling unstructured conversational data through four case studies.
  arXiv  Detail & Related papers  (2024-06-19T16:02:04Z)
• Polaris: A Safety-focused LLM Constellation Architecture for Healthcare [17.074456639617996]
  Polaris is the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents. We recruit over 1100 U.S. licensed nurses and over 130 U.S. licensed physicians to perform end-to-end conversational evaluations of our system.
  arXiv  Detail & Related papers  (2024-03-20T05:34:03Z)
• GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment [72.96949760114575]
  We propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA) GOMA formulates verbal communication as a planning problem that minimizes the misalignment between parts of agents' mental states that are relevant to the goals. We evaluate our approach against strong baselines in two challenging environments, Overcooked (a multiplayer game) and VirtualHome (a household simulator)
  arXiv  Detail & Related papers  (2024-03-17T03:52:52Z)
• Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation [96.22329536480976]
  We introduce the construction of a Healthcare Copilot designed for medical consultation. The proposed Healthcare Copilot comprises three main components: 1) the Dialogue component, responsible for effective and safe patient interactions; 2) the Memory component, storing both current conversation data and historical patient information; and 3) the Processing component, summarizing the entire dialogue and generating reports. To evaluate the proposed Healthcare Copilot, we implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual patient engaging in dialogue with the copilot, and as an evaluator to assess the quality of the dialogue.
  arXiv  Detail & Related papers  (2024-02-20T22:26:35Z)
• Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset [26.504409173684653]
  We introduce "ChatCoach", a human-AI cooperative framework designed to assist medical learners in practicing their communication skills during patient consultations. ChatCoachdifferentiates itself from conventional dialogue systems by offering a simulated environment where medical learners can practice dialogues with a patient agent, while a coach agent provides immediate, structured feedback. We have developed a dataset specifically for evaluating Large Language Models (LLMs) within the ChatCoach framework on communicative medical coaching tasks.
  arXiv  Detail & Related papers  (2024-02-08T10:32:06Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• LLM on FHIR -- Demystifying Health Records [0.32985979395737786]
  This study developed an app allowing users to interact with their health records using large language models (LLMs) The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles.
  arXiv  Detail & Related papers  (2024-01-25T17:45:34Z)
• Large Language Model Enhanced Multi-Agent Systems for 6G Communications [94.45712802626794]
  We propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language. We validate the effectiveness of the proposed multi-agent system by designing a semantic communication system.
  arXiv  Detail & Related papers  (2023-12-13T02:35:57Z)
• Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue [73.69510478736483]
  Large language models (LLMs) can generate fluent, coherent, and diverse responses. However, they lack a crucial ability: communication skills. This article aims to empower LLMs with communication skills through inner monologues. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines.
  arXiv  Detail & Related papers  (2023-11-13T16:19:42Z)
• Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project [0.0]
  Language difficulties between natives and immigrants present a common issue on a daily basis, especially in medical domain. The goal of the HYKIST Project is to develop a speech translation system to support patient-doctor communication with ASR and MT. We describe our efforts to construct ASR systems for a conversational telephone speech recognition task in the medical domain for Vietnamese language.
  arXiv  Detail & Related papers  (2023-09-26T21:12:09Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.