The Effect of Nurse Navigators in Digital Remote Monitoring in Cancer Care: Case Study Using Structural Equation Modeling
Bibliographic record
Abstract
BACKGROUND: The purpose of digital remote monitoring (DRM) is improving cancer care management. However, its effectiveness largely depends on the role of nurse navigators (NNs) within these systems to process data and lead action. OBJECTIVE: This study aims to fill gaps in our understanding of the role of NNs within a specific system, drawing on the Cancérologie parcours région Ile-de-France (CAPRI) DRM program applied to oncology patients. METHODS: The CAPRI DRM, targeting patients taking oral anticancer agents, combines digital interfaces with NN interventions. A phase 3 randomized controlled trial involving 559 patients assessed its safety and efficacy, with the primary end point being the relative dose intensity. This report focuses on patients in the CAPRI arm, evaluating the impact of NN interventions on outcomes such as toxicity, hospitalization, and emergency visits. Data on patient characteristics, NN interventions, and patient satisfaction surveys were analyzed using structural equation modeling. RESULTS: The study included 187 patients. Patient characteristics were significantly correlated with outcomes. Across all the models we used, the quality of NN interventions was consistently associated with higher patient satisfaction, with correlation coefficients ranging from 0.332 (95% CI 0.154-0.510; P<.001) to 0.366 (95% CI 0.182-0.550; P<.001). The number of grade ≥3 toxicity events correlated positively with NN referrals to oncologists. Hospitalization length was positively related to NN referral (coefficient 0.102, 95% CI 0.051-0.153; P<.001) and inversely to NN advice (coefficient -0.045, 95% CI -0.096 to 0.006; P=.08). Emergency visits showed a negative correlation with NN actions (coefficient -0.478, 95% CI -0.923 to 0.033; P=.04) and a positive correlation with NN calls and referrals (coefficient 0.516, 95% CI 0.069-0.963; P=.02). CONCLUSIONS: This study shows the central role of NNs in making DRM effective. Despite the study's limitations, these results support the design of DRM as a hybrid model of automated digital tools and human support. Future research should explore the applicability of such a DRM model in various clinical settings to clarify the optimal association between automated systems and NN expertise. TRIAL REGISTRATION: ClinicalTrials.gov NCT02828462; https://www.clinicaltrials.gov/study/NCT02828462.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".