Where's the Remote? Failure to Report Clinical Workflows in Heart Failure Remote Monitoring Studies
Bibliographic record
Abstract
BACKGROUND: Remote patient monitoring (RPM) clinical trials have reported mixed results in improving outcomes for patients with chronic heart failure (HF). The impact of clinical workflows that could impact RPM effectiveness is often overlooked. We sought to characterize workflows and response protocols that could impact outcomes in studies of noninvasive RPM in HF. METHODS: We reviewed studies (1999-2024) assessing noninvasive RPM interventions for adults with HF. We collected 24 aspects of workflows describing education, physiologic and symptomatic data collection, transmission and review, clinical escalation protocols, and response time. We attempted to perform a meta-analysis to identify associations between workflow components and outcomes of death and hospitalization. RESULTS: We identified 63 studies (57.1% randomized controlled, 23.8% pilot/feasibility, 19.1% other) comprising 16,699 subjects. Despite a large number of studies and subjects, workflow reporting was insufficient to perform our intended meta-analysis regarding key workflow components. RPM clinical workflows were diverse in configuration, with high variability in component description ranging from always reported to never reported. Specifics of monitoring devices and related training were well reported as expected based on most trial hypotheses. However, elements of clinical data response such as frequency of data review, clinical escalation criteria, and provider response time were often underreported or not reported at all (48%, 24%, and 97%, respectively), hindering study replication and evidence-based implementation. CONCLUSIONS: Clinical workflows are poorly described in noninvasive RPM studies, preventing systematic assessment, device comparison, and replication. A standardized approach to reporting HF RPM workflows is vital to evaluate effectiveness and guide evidence-based clinical implementation.
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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 | 0.473 | 0.666 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".