Digital GO: A Clinical Decision-Support Dashboard for Gynecologic Oncology Tumor Board
Clinical Snapshot
PICO Framework
| P — Population | Gynecologic oncology multidisciplinary tumor board meetings, specifically ovarian cancer cases |
| I — Intervention | Interactive digital dashboard with LLM-based data extraction and visualization |
| C — Comparator | Traditional paper-based or fragmented digital MTB workflows |
| O — Outcomes | Feasibility of integration, data streamlining, visualization capability |
Bottom Line
This Italian study presents a novel digital dashboard for gynecologic oncology tumor boards that uses large language models to extract and visualize patient data from clinical reports. While the concept addresses a genuine clinical need for streamlined multidisciplinary decision-making, the evidence is preliminary and lacks rigorous evaluation. The study demonstrates technical feasibility but provides no data on clinical outcomes, user satisfaction, or accuracy of data extraction. For Australian clinicians, this represents an interesting proof-of-concept that could inform local digital health initiatives, but implementation would require substantial infrastructure investment and validation studies. The approach aligns with trends toward digital transformation in oncology care, but more robust evaluation is needed before clinical adoption can be recommended.
Key Findings
Effect Size: Not quantified
Primary Outcome: Demonstrated feasibility of integrating LLM-based data extraction with visualization framework
Nnt Or Sensitivity: LLM extraction accuracy not specified
Confidence Interval: Not provided
Clinical Application
Requires significant IT infrastructure and LLM implementation capabilities Could align with Australian Digital Health Strategy and Cancer Australia's multidisciplinary care guidelines, but would need adaptation to local EMR systems and TGA digital therapeutics framework Gynecologic oncology MTBs in tertiary care centers with digital infrastructure
Abstract
Multidisciplinary Tumor Board (MTB) meetings are essential for coordinated oncologic decision-making but are often hindered by the complexity and fragmentation of patient data. This study presents the development of an interactive digital dashboard designed to streamline data access and visualization during ovarian cancer MTB sessions. The system leverages structured knowledge extracted from unstructured clinical reports using in-house large language model (LLM) pipelines. These outputs are harmonized within a cloud environment through dedicated processing workflows and mapped into a domain ontology, to ensure interoperability. The resulting dashboard, implemented using a dedicated functional module, enables both cohort-level exploration and patient-specific visualization, including longitudinal event timelines. Preliminary results demonstrate the feasibility of integrating LLM-based data extraction with a scalable visualization framework, offering a step toward the digital transformation of multidisciplinary oncology workflows.
References
- 1.Dachena, C., Luraschi, A., de Maria, C., Criscione, M., Rosati, A., Tomassini, F., Tobia, G. P., Patarnello, S., Fagotti, A., & Lilli, L. (2026). Digital GO: A Clinical Decision-Support Dashboard for Gynecologic Oncology Tumor Board. Studies in Health Technology and Informatics. https://doi.org/10.3233/SHTI260158
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