Remote telemonitoring is associated with improved patient safety and decreased workload of nurses
Bibliographic record
Abstract
Objective: There is significant interest in exploring new technologies to improve efficiency and work-life quality for nurses. We aimed to evaluate the impact of a remote video monitoring (RVM) solution that provides continuous in-hospital patient audio-video (AV) monitoring by technicians. Methods: saturation monitoring device, has been deployed in all inpatient units within our hospital network, including 3 acute care hospitals and 2 rehabilitation facilities. Data were collected before and after implementation on safety measures including fall rates and adverse events, along with device utilization and number of escalation events requiring nursing intervention. Nurse job satisfaction was assessed with surveys. Results: Data were collected from April 2020 to May 2022. A total of 2087 patients were monitored at 5 hospital sites. The technicians identified 54,716 safety concerns that required them to intervene remotely and address with the patient. Of these, 46,289 required escalation of nursing staff, who were called to the bedside through the RVM alerting technology. Importantly, 8427 safety concerns were managed solely by the technicians without the need for nursing intervention, resulting in 8427 avoided nursing visits to the bedside. The surveyed nurses reported that the RVM technology provided reassurance that additional support was available to assist them in managing their patients. Patients and their families also expressed high degree of satisfaction. Since implementation, the rates of falls and other adverse events have been reduced, with the greatest impact in patients on high-flow oxygen. Code blue and mortality rates decreased from 7% to 1%. Conclusions: The use of RVM has proven to be a successful innovation at our hospital and has led to improved patient safety. RVM was able to reduce 8427 individual nurse visits to the bedside, allowing nurses to manage the care of patients more effectively while improving both patient and staff satisfaction.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".