Determining the cost of implementing and operating a remote patient monitoring programme for the elderly with chronic conditions: A systematic review of economic evaluations
Bibliographic record
Abstract
Introduction Remote patient monitoring (RPM) in conjunction with home nursing visits is becoming increasingly popular for the follow-up of patients with chronic conditions and evidence exists that it improves patients' health outcomes. Current cost data is reported inconsistently and often gathered from studies of poor methodological quality, making it difficult for decision-makers who consider implementing this service in their organizations. This study reviewed the cost of RPM programmes targeting elderly patients with chronic conditions. Methods After evaluation against the inclusion and exclusion criteria and appraisal against two criteria which are important for economic evaluations, data from selected studies were extracted and grouped into meaningful cost categories, then adjusted to reflect November 2015 US dollars. Results In the 13 selected studies, the newly-created cost category 'Combined intervention cost' (reflecting equipment purchasing, servicing and monitoring cost) for the various RPM programmes ranged from US$275-US$7963 per patient per year. The three main findings are: (a) RPM programme costs have decreased since 2004 due to cheaper technology; (b) monitoring a single vital sign is likely to be less costly than monitoring multiple vital signs; and (c) programmes targeting hypertension or congestive heart failure are less costly than those targeting respiratory diseases or multiple conditions. Conclusions This review recommends that future studies present their cost data with more granularity, that grouping of costs should be minimized and that any assumptions, such as amortization, should be made explicit. In addition, studies should compare programmes with similar characteristics in terms of type of conditions, number of vital signs monitored, etc. for more generalizable results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".