ABSTRACT
Background: A major challenge in healthcare is the interpretation of the constantly increasing amount of clinical data of interest to inpatients for diagnosis and therapy. It is vital to accurately structure and represent data from different sources to help clinicians make informed decisions. Objective: We evaluated the usability of our tool 'Triptychon' - a three-part visualisation dashboard of essential patients' medical data provided by a direct overview of their hospitalisation information, laboratory, and vital parameters over time. Methods: The study followed a cohort of 20 participants using the mixed-methods approach, including interviews and the usability questionnaires, Health Information Technology Usability Evaluation Scale (Health-ITUES), and User Experience Questionnaire (UEQ). The participant's interactions with the dashboard were also observed. A thematic analysis approach was applied to analyse qualitative data and the quantitative data's task completion time and success rates. Results: The usability evaluation of the visualisation dashboard revealed issues relating to the terminology used in the user interface and colour coding in its left and middle panels. The Health-ITUES score was 3.72 (standard deviation (SD) = 1.0), and the UEQ score was 1.6 (SD = 0.74). The study demonstrated improvements in intuitive dashboard use and overall satisfaction with using the dashboard daily. Conclusion: The Triptychon dashboard is a promising new tool for medical data presentation. We identified design and layout issues of the dashboard for improving its usability in routine clinical practice. According to users' feedback, the three panels on the dashboard provided a holistic view of a patient's hospital stay.
ABSTRACT
BACKGROUND: Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT. METHODS: Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting. RESULTS: The adapted integrated model classifying patients into "ICU/no ICU demand" comprises six radiomics and seven laboratory biomarkers. The models' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91. CONCLUSION: The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.