Need Analysis of Clinician-Oriented Integrated Precision Oncology Decision Support Tools: Qualitative Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs. A lack of comprehensive needs assessment may result in unaddressed requirements, limiting the effectiveness of these tools in real-world practice. OBJECTIVE: This study aimed to explore clinicians' needs and expectations regarding the functionalities of integrated PODS tools, providing insights into essential features that could enhance their usability and impact. METHODS: We conducted a qualitative investigation at Peking University Cancer Hospital to explore clinicians' needs and expectations for the functions of integrated PODS tools. Data were collected through 143 structured participant observations during multidisciplinary team meetings and 17 in-depth semistructured interviews with a diverse group of oncology specialists, including physicians, surgeons, molecular biologists, radiotherapists, radiologists, and pathologists. Thematic analysis was applied to identify key functional requirements, and a requirements framework was formed. RESULTS: Three overarching functional needs emerged: (1) better access to oncological knowledge, including support for therapy selection (guidelines, conferences, and consensuses), clinical trials, drug and treatment information, and complex case knowledge, as well as improved diagnostic and prognostic insights; (2) clinical contextualization and resource navigation, referring to the process of contextualizing scientific knowledge within real-world clinical settings, including access to clinical trials and drugs, along with predictive models for treatment response; and (3) support abilities in the decision-making process, highlighting the need for integration of flexible biological knowledge and phenotypic data; automated patient information synthesis; improved data visualization; and optimized retrieval, recommendation, and question-answering functionalities. A functional framework for integrated PODS tools was proposed based on these findings. CONCLUSIONS: The study conducted a qualitative descriptive observation and interview in the use, needs of integrated PODS tools. PODS tools serve as complex, multilevel decision support systems. A clear understanding of clinicians' actual needs is crucial for their refinement and practical adoption. By capturing perspectives directly from oncology professionals, this study provides actionable insights into the functional enhancements required for PODS tools, ultimately aiming to bridge the gap between genomic advancements and clinical decision-making in precision oncology.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,038 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».