Patient and Healthcare Provider Perspectives on the Implementation of a Web-Based Clinical Communication System for Cancer: A Qualitative Study
Bibliographic record
Abstract
Previous research has identified communication and care coordination problems for patients with cancer. Healthcare providers (HCPs) have reported communication issues due to the incompatibility of electronic medical records (EMR) software and not being consistently copied on patient reports. We evaluated an asynchronous web-based communication system ("eOncoNote") for primary care providers and cancer specialists to improve cancer care coordination. The objectives were to examine patients' perceptions of the role of eOncoNote in their healthcare, and HCPs' experiences of implementing eOncoNote. Qualitative interviews were conducted with patients with breast and prostate cancer, primary care providers, and cancer specialists. Eighteen patients and fourteen HCPs participated. Six themes were identified from the patient interviews focusing on HCP and patient roles related to care coordination and patient awareness of communication among their HCPs. Four themes were identified from HCP interviews related to the context of care coordination and experience with eOncoNote. Both patients and HCPs described the important role patients and caregivers play in care coordination. The results show that patients were often unaware of the communication between their HCPs and assumed they were communicating. HCPs encountered challenges incorporating eOncoNote into their workflow.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".