Challenges associated with the integration of immuno-oncology agents in clinical practice
Bibliographic record
Abstract
BACKGROUND: The availability of new immuno-oncology therapeutics markedly impacts oncology clinicians' treatment decision-making. To effectively support healthcare professionals (HCPs) in their practice, it is important to better understand the challenges and barriers that can accompany the introduction of these agents. This study aimed to establish the types and causes of clinical challenges posed by the introduction of new immuno-oncology agents. METHODS: The mixed-methods design included qualitative in-depth interviews and group discussions with HCPs, in which participants discussed clinical challenges and potential underlying reasons for these challenges. Qualitative findings informed a quantitative survey. This survey investigated the extent and distribution of challenges using HCPs' self-rating of knowledge, skill, confidence, and exposure to system-level effects. These two phases were conducted sequentially with distinctly stratified samples of oncologists, nurse practitioners (NPs), physician assistants (PAs), pathologists, clinical pharmacists, interventional radiologists, rheumatologists, pulmonologists, and emergency department physicians. Participants were from the United States and had various levels of clinical experience and represented both academic and community-based settings. RESULTS: The final sample included 107 HCPs in the qualitative phase and 554 in the quantitative phase. Analyses revealed clinical challenges related to the use of pharmacodiagnostics. For example, 47% of pathologists and 42% of oncologists reported skill gaps in identifying the appropriate marker and 46% of oncologists, 61% of PAs, 66% of NPs, 74% of pulmonologists and 81% of clinical pharmacists reported skill gaps in selecting treatment based on test results. Challenges also emerged regarding the integration of immuno-oncology agents, as oncologists, rheumatologists, pulmonologists, clinical pharmacists, PAs, and NPs reported knowledge gaps (74-81%) of the safety profiles of recently approved agents. In addition, 90% of clinical pharmacists reported skill gaps weighing the risks and benefits of treating patients with immuno-oncology agents while affected by lupus. Finally, patient communication challenges were identified: HCPs reported difficulties discussing essential aspects of immunotherapy to patients as well as how they might compare to other types of therapies. CONCLUSION: The challenges highlighted in this study reveal substantial educational gaps related to the integration of immuno-oncology agents into practice for various groups of HCPs. These findings provide a strong base of evidence for future educational initiatives.
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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 | 0.074 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".