Denecke Kerstin, Reichenpfader Daniel, Willi Dominic, Kennel Karin, Bonel Harald, Nairz Knud, Cihoric Nikola, Papaux Damien, von Tengg-Kobligk Hendrik
Artificial Intelligence for Health, Institute for Patient-Centered Digital Health, School of Engineering and Computer Science, Bern University of Applied Sciences, Biel, Switzerland.
Department of Radiology, Lindenhof Hospital, Bern, Switzerland.
Front Artif Intell. 2024 Aug 16;7:1431156. doi: 10.3389/frai.2024.1431156. eCollection 2024.
Radiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.
MIA, the digital medical interview assistant, was developed using a person-based design approach, involving patient opinions and expert knowledge during the design and development with a specific use case in collecting information before a mammography examination. MIA consists of two modules: the interview module and the question answering module (Q&A). To ensure interoperability with clinical information systems, we use HL7 FHIR to store and exchange the results collected by MIA during the patient interaction. The system was evaluated according to an existing evaluation framework that covers a broad range of aspects related to the technical quality of a conversational agent including usability, but also accessibility and security.
Thirty-six patients recruited from two Swiss hospitals (Lindenhof group and Inselspital, Bern) and two patient organizations conducted the usability test. MIA was favorably received by the participants, who particularly noted the clarity of communication. However, there is room for improvement in the perceived quality of the conversation, the information provided, and the protection of privacy. The Q&A module achieved a precision of 0.51, a recall of 0.87 and an F-Score of 0.64 based on 114 questions asked by the participants. Security and accessibility also require improvements.
The applied person-based process described in this paper can provide best practices for future development of medical interview assistants. The application of a standardized evaluation framework helped in saving time and ensures comparability of results.
由于时间限制，放射科医生常常缺乏与患者的直接接触。数字医疗问诊助手旨在促进健康信息的收集。在本文中，我们提议利用对话代理来实现一个医疗问诊助手，以方便病史采集，同时为患者提供就检查相关问题提问的机会。
数字医疗问诊助手MIA采用基于人的设计方法进行开发，在设计和开发过程中纳入了患者意见和专家知识，其具体用例是在乳房X光检查前收集信息。MIA由两个模块组成：问诊模块和问答模块（Q&A）。为确保与临床信息系统的互操作性，我们使用HL7 FHIR来存储和交换MIA在患者互动过程中收集的结果。该系统根据一个现有的评估框架进行评估，该框架涵盖了与对话代理技术质量相关的广泛方面，包括可用性，以及可访问性和安全性。
从两家瑞士医院（林登霍夫集团和伯尔尼大学医院）以及两个患者组织招募的36名患者进行了可用性测试。参与者对MIA评价良好，特别提到了沟通的清晰度。然而，在对话的感知质量、所提供的信息以及隐私保护方面仍有改进空间。基于参与者提出的114个问题，问答模块的精确率为0.51，召回率为0.87，F值为0.64。安全性和可访问性也需要改进。
本文所描述的基于人的应用过程可为医疗问诊助手的未来发展提供最佳实践。标准化评估框架的应用有助于节省时间并确保结果的可比性。
