ArticleBMC emergency medicine2019
Developing outcome, process and balancing measures for an emergency department longitudinal patient monitoring system using a modified Delphi.
Article in BMC emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Development of a national maternity early warning score: centile based score development and Delphi informed escalation pathways.BMJ medicine · 2024Article
- Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning.Journal of healthcare engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly warning score systems have been widely recommended for use to detect clinical deterioration in patients. The Irish National Emergency Medicine Programme has developed and piloted an emergency department specific early warning score system. The objective of this study was to develop a consensus among frontline healthcare staff, quality and safety staff and health systems researchers regarding evaluation measures for an early warning score system in the Emergency Department.
methodsParticipatory action research including a modified Delphi consensus building technique with frontline hospital staff, quality and safety staff, health systems researchers, local and national emergency medicine stakeholders was the method employed in this study. In Stage One, a workshop was held with the participatory action research team including frontline hospital staff, quality and safety staff and health systems researchers to gather suggestions regarding the evaluation measures. In Stage Two, an electronic modified-Delphi study was undertaken with a panel consisting of the workshop participants, key local and national emergency medicine stakeholders. Descriptive statistics were used to summarise the characteristics of the panellists who completed the questionnaires in each round. The mean Likert rating, standard deviation and 95% bias-corrected bootstrapped confidence interval for each variable was calculated. Bonferroni corrections were applied to take account of multiple testing. Data were analysed using Stata 14.0 SE.
resultsUsing the Institute for Healthcare Improvement framework, 12 process, outcome and balancing metrics for measuring the effectiveness of an ED-specific early warning score system were developed.
conclusionThere are currently no published measures for evaluating the effectiveness of an ED early warning score system. It was possible in this study to develop a suite of evaluation measures using a modified Delphi consensus approach. Using the collective expertise of frontline hospital staff, quality and safety staff and health systems researchers to develop and categorise the initial set of potential measures was an innovative and unique element of this study.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.