Challenges With Continuous Pulse Oximetry Monitoring and Wireless Clinician Notification Systems After Surgery: Reactive Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Research has shown that introducing electronic Health (eHealth) patient monitoring interventions can improve healthcare efficiency and clinical outcomes. The VIGILANCE (VItal siGns monItoring with continuous puLse oximetry And wireless cliNiCian notification aftEr surgery) study was a randomized controlled trial (n=2049) designed to assess the impact of continuous vital sign monitoring with alerts sent to nursing staff when respiratory resuscitations with naloxone, code blues, and intensive care unit transfers occurred in a cohort of postsurgical patients in a ward setting. This report identifies and evaluates key issues and challenges associated with introducing wireless monitoring systems into complex hospital infrastructure during the VIGILANCE eHealth intervention implementation. Potential solutions and suggestions for future implementation research are presented. OBJECTIVE: The goals of this study were to: (1) identify issues related to the deployment of the eHealth intervention system of the VIGILANCE study; and (2) evaluate the influence of these issues on intervention adoption. METHODS: During the VIGILANCE study, issues affecting the implementation of the eHealth intervention were documented on case report forms, alarm event forms, and a nursing user feedback questionnaire. These data were collated by the research and nursing personnel and submitted to the research coordinator. In this evaluation report, the clinical adoption framework was used as a guide to organize the identified issues and evaluate their impact. RESULTS: Using the clinical adoption framework, we identified issues within the framework dimensions of people, organization, and implementation at the meso level, as well as standards and funding issues at the macro level. Key issues included: nursing workflow changes with blank alarm forms (24/1030, 2.33%) and missing alarm forms (236/1030, 22.91%), patient withdrawal (110/1030, 10.68%), wireless network connectivity, false alarms (318/1030, 30.87%), monitor malfunction (36/1030, 3.49%), probe issues (16/1030, 1.55%), and wireless network standards. At the micro level, these issues affected the quality of the service in terms of support provided, the quality of the information yielded by the monitors, and the functionality, reliability, and performance of the monitoring system. As a result, these issues impacted access through the decreased ability of nurses to make complete use of the monitors, impacted care quality of the trial intervention through decreased effectiveness, and impacted productivity through interference in the coordination of care, thus decreasing clinical adoption of the monitoring system. CONCLUSIONS: Patient monitoring with eHealth technology in surgical wards has the potential to improve patient outcomes. However, proper planning that includes engagement of front-line nurses, installation of appropriate wireless network infrastructure, and use of comfortable cableless devices is required to maximize the potential of eHealth monitoring. TRIAL REGISTRATION: ClinicalTrials.gov NCT02907255; https://clinicaltrials.gov/ct2/show/NCT02907255.
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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 | 0.182 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".