Denecke, Kerstin; Cihoric, Nikola; Reichenpfader, Daniel (2023). Designing a Digital Medical Interview Assistant for Radiology In: Pfeiffer, Bernhard; Schreier, Günter; Baumgartner, Martin; Hayn, Dieter (eds.) dHealth 2023. Studies in Health Technology and Informatics: Vol. 301 (pp. 60-66). Amsterdam: IOS Press 10.3233/SHTI230012
|
Text
SHTI-301-SHTI230012.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC). Download (269kB) | Preview |
Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient’s medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group.
Item Type: |
Book Section (Book Chapter) |
|---|---|
Division/Institute: |
School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science |
Name: |
Denecke, Kerstin 0000-0001-6691-396X; Cihoric, Nikola; Reichenpfader, Daniel; Pfeiffer, Bernhard; Schreier, Günter; Baumgartner, Martin and Hayn, Dieter |
Subjects: |
Q Science > Q Science (General) |
ISBN: |
9781643683867 |
Series: |
Studies in Health Technology and Informatics |
Publisher: |
IOS Press |
Projects: |
[UNSPECIFIED] Smaragd - NLP-Unterstützung der Radiologischen Befundung |
Language: |
English |
Submitter: |
Kerstin Denecke |
Date Deposited: |
16 May 2023 15:43 |
Last Modified: |
25 Oct 2023 13:42 |
Publisher DOI: |
10.3233/SHTI230012 |
Uncontrolled Keywords: |
Medical History Taking, Natural Language Processing, Patients, Radiology |
ARBOR DOI: |
10.24451/arbor.19224 |
URI: |
https://arbor.bfh.ch/id/eprint/19224 |
Actions (login required)
 |
View Item |