The Use of Virtual Care in Patients with Hematologic Malignancies: A Scoping Review
Bibliographic record
Abstract
There is increasing interest from cancer patients and their healthcare providers in the use of virtual care in routine clinical practice. In the setting of hematologic malignancy, where patients often undergo complex and immunodepleting treatments, understanding how to use virtual care safely and effectively is critically important. We aimed to describe the use of virtual care in patients with hematologic malignancies and to examine physician- and patient-reported outcomes in the form of a systematic scoping review. An electronic search of PubMed, Ovid MEDLINE, Elsevier Embase, Scopus, and EBSCO CINAHL was conducted from January 2000 to April 2021. A comprehensive search strategy was used to identify relevant articles, and data were extracted to assess the study design, population, setting, patient characteristics, virtual care platform, and study results. Studies were included if they described the use of virtual care for patients with hematologic malignancies; commentaries were excluded. Fifteen studies met the inclusion criteria after abstract and full-text review. Three studies found that app-based tools were effective in monitoring patient symptoms and triggering alerts for more urgent follow-up. Four studies described the use of phone-based interventions. Five studies found that videoconferencing, with both physicians and oncology nurses, was highly rated by patients. Emerging themes included high levels of patient satisfaction across all domains of virtual care. Provider satisfaction scores were rated lower than patient scores, with concerns about technical issues leading to challenges with virtual care. Four studies found that virtual care allowed providers to promptly respond to patient concerns, especially when patients were experiencing side-effects or had questions about their treatment. Overall, the use of virtual care in patients with hematologic malignancies appears feasible, and resulted in high patient satisfaction. Further research is needed in order to evaluate the optimal method of integrating virtual care into clinical practice.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".