Need Analysis of Clinician-Oriented Integrated Precision Oncology Decision Support Tools: Qualitative Descriptive Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".