Effect of remote patient monitoring on healthcare use among patients with cancer: A systematic review
Bibliographic record
Abstract
Purpose Remote patient monitoring (RPM) allows healthcare providers to monitor patient outcomes outside of a traditional healthcare setting, potentially supporting reductions in acute care utilization. This systematic review aims to assess whether RPM use among cancer patients reduces hospitalizations and length of stay (LOS). Methods A systematic review was conducted to identify articles published in PubMed and CINAHL between 2019 and 2024 that evaluated the impacts of RPM in cancer patients compared to the standard care. The primary outcomes examined were hospitalizations and LOS. Secondary outcomes were emergency department (ED) visits and hospital readmissions. Two reviewers screened and assessed the studies. Evidence strength was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Risk of bias was assessed using the Newcastle-Ottawa Scale and the Cochrane risk-of-bias tool for randomized trials (RoB 2). While statistical tests were not conducted on results, outcomes were categorized as having increased, decreased, or no change. Results This review included one randomized controlled trial, seven cohort studies, and one case-control study. RPM was associated with reductions in healthcare utilization among cancer patients. There were significant reductions in hospitalizations, LOS, ED visits, and hospital readmissions in 67% (4/6), 67% (4/6), 75% (3/4), and 67% (2/3) of studies, respectively. Conclusion This systematic review builds upon existing literature that demonstrates the effectiveness of using RPM to manage acute conditions such as cancer. RPM may support management of various cancer-related conditions and, in turn, potentially reduce acute care use. However, study homogeneity and additional rigorous study designs are necessary to draw more definitive conclusions about the impacts of RPM in cancer care.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".