115 research outputs found
Better ViEWS ahead? It is high time to improve patient safety by standardizing Early Warning Scores.
No full textCentile-based Early Warning Scores derived from statistical distributions of vital signs
No full textTraduzione e adattamento in italiano dell'indice "Modified Early Warning Score" (MEWS) per il riconoscimento precoce dell'instabilità clinica
No full textVital-sign data-fusion methods to identify patient deterioration in the emergency department
No full textIn the United Kingdom Emergency Departments (ED), clinical staff requires
to diagnose, give treatment and discharge patients, within 4 hours of their
arrival. The patientsâ vital signs are traditionally managed using paper Track
and Trigger (T&T) charts, prone to human error, and bedside monitors, whose
alerts are often ignored. Consequently, patient deterioration might be missed
at and between nursesâ observation sets. This thesis has analysed data from
a three stage study in the ED of the John Radcliffe Hospital, Oxford, to
investigate the use of an electronic T&T system (VitalPac) and a data-fusion
system (Visensia) to help staff identify physiological deterioration in patients
attending the majors area.
Data was collected from a total of 10,488 ED attendances receiving standard
care in stage 1, followed by two technology interventions in stages 2 and 3,
respectively, for a total period of 6 months. It was shown that 9% of the
observations sets, conducted on stable ED patients in stage 1, were done
on unstable patients when staff was guided by VitalPac in stage 2. One of
the causes might have been the increase in the Early Warning Score (EWS)
completion from 52% to 100% of the observation sets. In stage 3, 35.7%
of the Visensia alerts generated on continuous bedside monitor data, were
deemed âtechnicalâ alerts due to data artefacts. On the other hand, clinical
staff responded within 15 minutes to 85% of the âphysiologicalâ alerts.
A two-stage Machine Learning (ML) architecture was proposed to fuse intermittent
and continuous vital-sign data and use a sub-population novelty detection
model to identify multivariate data deviating from normal physiology.
This ML approach out-performed the baseline Visensia model (a population based
model, applied over the continuous data), and the National EWS system
(applied over the observation sets data) in detecting patients escalated
to the Resuscitation area during their ED stay, on a test set of 1,070 ED attendances
(AUROCs and 95% confidence intervals were 0.737 (0.623, 0.830),
0.657 (0.521, 0.755), and 0.643 (0.522, 0.749), respectively).</p
Continuous Monitoring of Respiratory Rate in Emergency Admissions: Evaluation of the RespiraSense™ Sensor in Acute Care Compared to the Industry Standard and Gold Standard
No full textRespiratory Rate (RR) is the best marker to indicate deterioration but measurement are often inaccurate. The RespiraSense™ is a non-invasive, wireless, body worn, motion-tolerant and continuous respiratory rate monitor. We aimed to determine whether the performance of RespiraSense™ was equivalent to that of a gold standard measurement technique of capnography and the industry standard of manual counts. RespiraSense™ measures respiratory rate and transmit signals wirelessly to a tablet device. We measured respiratory rate in 24 emergency admissions to an Acute Medical Unit in the UK. Patients were observed for two hours. Manual counts were undertaken every 15 min and compared to measurements with capnography and RespiraSense™. Data from 17 patients admitted as medical emergencies was evaluated. For measurements obtained at rest a mean RR of 19.3 (SD 4.89) for manual measurements compared to mean RR of 20.2 (SD 4.54) for measurements obtained with capnography and mean RR of 19.8 (SD 4.52) with RespiraSense™. At rest, RespiraSense™ has a bias of 0.38 and limits of agreement of 1.0 to 1.8 bpm, when compared to the capnography derived RR. Measurements were within pre-defined limits of error at rest. Continuous measurement of RR with RespiraSense™ in patients admitted as acute emergencies is both feasible and reliable
It’s Been a Hard Day’s Night—What Determines Impact of Less Than 24/7 Rapid Response Systems?*
No full text
Identifying the patient at risk of deterioration, intensive care unit admission, or cardiac arrest: Stop predicting, start preventing
No full textImpact of electronic health records on predefined safety outcomes in patients admitted to hospital: a scoping review
No full textObjectives Review available evidence for impact of electronic health records (EHRs) on predefined patient safety outcomes in interventional studies to identify gaps in current knowledge and design interventions for future research.Design Scoping review to map existing evidence and identify gaps for future research.Data sources PubMed, the Cochrane Library, EMBASE, Trial registers.Study selection Eligibility criteria: We conducted a scoping review of bibliographic databases and the grey literature of randomised and non-randomised trials describing interventions targeting a list of fourteen predefined areas of safety. The search was limited to manuscripts published between January 2008 and December 2018 of studies in adult inpatient settings and complemented by a targeted search for studies using a sample of EHR vendors. Studies were categorised according to methodology, intervention characteristics and safety outcome.Results from identified studies were grouped around common themes of safety measures.Results The search yielded 583 articles of which 24 articles were included. The identified studies were largely from US academic medical centres, heterogeneous in study conduct, definitions, treatment protocols and study outcome reporting. Of the 24 included studies effective safety themes included medication reconciliation, decision support for prescribing medications, communication between teams, infection prevention and measures of EHR-specific harm. Heterogeneity of the interventions and study characteristics precluded a systematic meta-analysis. Most studies reported process measures and not patient-level safety outcomes: We found no or limited evidence in 13 of 14 predefined safety areas, with good evidence limited to medication safety.Conclusions Published evidence for EHR impact on safety outcomes from interventional studies is limited and does not permit firm conclusions regarding the full safety impact of EHRs or support recommendations about ideal design features. The review highlights the need for greater transparency in quality assurance of existing EHRs and further research into suitable metrics and study designs
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