A Study on Optimization and Evaluation of the Visualization of Complex Algorithm Results in Remote Monitoring of COPD

Mathilde D Sander*, Rikke L Madsen, Sisse H. Laursen, Stine Hangaard

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

BACKGROUND: Artificial intelligence (AI) can potentially increase the quality of telemonitoring in chronic obstructive pulmonary disease (COPD). However, the output from AI is often difficult for clinicians to understand due to the complexity. This challenge may be accommodated by visualizing the AI results, however it hasn't been studied how this could be done specifically, i.e., considering which visual elements to include.

AIM: To investigate how complex results from a predictive algorithm for patients with COPD can be translated into easily understandable data for the clinicians.

METHODS: Semi-structured interviews were conducted to explore clinicians' needs when visualizing the results of a predictive algorithm. This formed a basis for creating a prototype of an updated user interface. The user interface was evaluated using usability tests through the "Think aloud" method.

RESULTS: The clinicians pointed out the need for visualization of exacerbation alerts and the development in patients' data. Furthermore, they wanted the system to provide more information about what caused exacerbation alerts. Elements such as color and icons were described as particularly useful. The usability of the prototype was primarily assessed as easily understandable and advantageous in connection to the functions of the predictive algorithm.

CONCLUSION: Predictive algorithm use in telemonitoring of COPD can be optimized by clearly visualizing the algorithm's alerts, clarifying the reasons for algorithm output, and by providing a clear overview of the development in the patient's data. This can contribute to clarity when the clinicians should act and why they should act on alerts from predictive algorithms.

Original languageEnglish
Title of host publicationTelehealth Ecosystems in Practice
EditorsMauro Giacomini, Lăcrămioara Stoicu-Tivadar, Gabriella Balestra, Arriel Benis, Stefano Bonacina, Alessio Bottrighi, Thomas M. Deserno, Parisis Gallos, Lenka Lhotska, Sara Marceglia, Alejandro C. Pazos Sierra, Samanta Rosati, Lucia Sacchi
Number of pages5
Volume309
Publication date20 Oct 2023
Pages23-27
ISBN (Print)978-1-64368-450-5
ISBN (Electronic)978-1-64368-451-2
DOIs
Publication statusPublished - 20 Oct 2023
EventEuropean Federation for Medical Informatics Special Topic Conference: Telehealth Ecosystems in Practice - Politecnico di Torino, Torino, Italy
Duration: 25 Oct 202327 Oct 2023
https://www.stc2023.org/

Conference

ConferenceEuropean Federation for Medical Informatics Special Topic Conference: Telehealth Ecosystems in Practice
LocationPolitecnico di Torino
Country/TerritoryItaly
CityTorino
Period25/10/202327/10/2023
Internet address
SeriesStudies in Health Technology and Informatics
ISSN0926-9630

Keywords

  • Algorithms
  • Artificial Intelligence
  • Humans
  • Pulmonary Disease, Chronic Obstructive/diagnosis
  • Telemedicine/methods
  • User Interface
  • Predictive Algorithm
  • Telemonitoring
  • Usability
  • COPD

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