Can emergency medical service staff predict the disposition of patients they are transporting?

Clesham, K; Mason, Suzanne; Gray, Jonathan; Walters, Stephen J.; Cooke, Virginia

doi:10.1136/emj.2007.054924

Cited by 37 publications

(28 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a UK study, the authors stated that EMS staff have a reasonable level of accuracy when predicting patient disposition following ED attendance, with a sensitivity of 72% and a specificity of 77% 20. In a similar study in the USA, the sensitivity for predicting admission was 62% with a specificity of 89% 21.…”

Section: Discussionmentioning

confidence: 98%

Can advanced paramedics in the field diagnose patients and predict hospital admission?

et al. 2013

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 98%

Can advanced paramedics in the field diagnose patients and predict hospital admission?

et al. 2013

View full text Add to dashboard Cite

show abstract

“…As described in the introduction, other published models for making admission predictions in the ED seek to assign a yes/no value to the patient 12–17 . This use of predictions can indeed drive the process suggested in Figure 1, by simply placing the admission order sooner.…”

Section: Methodsmentioning

confidence: 99%

“…Most studies that predict admission have focused either on the entire ED population 12,13 or on specific categories of patients 14–17 and treat admissions as binary in the sense of estimating “yes” or “no” at the patient level. This approach may be less useful when the goal is to predict aggregate demand.…”

mentioning

confidence: 99%

Predicting Emergency Department Inpatient Admissions to Improve Same‐day Patient Flow

Peck

Benneyan

Nightingale

et al. 2012

Academic Emergency Medicine

118

113

View full text Add to dashboard Cite

Objectives: The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. Methods: Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). Results: Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R 2 of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R 2 of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R 2 of 0.52 and an average difference in total bed need of 1.87 per day. Conclusions: Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management , patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

show abstract

“…177 A US study assessed emergency medical technicians' ability to identify intoxicated people who did not need transporting to hospital and found there was potential for emergency medical technicians to accurately triage patients to alternatives to ED. 178 Another recent UK study evaluated the utility and safety of a triage support tools (Paramedic Pathfinder) for deciding which medical and trauma patients needed transporting or could be left at the scene.…”

Section: Safety and Decision-makingmentioning

confidence: 99%

What evidence is there on the effectiveness of different models of delivering urgent care? A rapid review

Turner

Coster

Chambers

et al. 2015

Health Serv Deliv Res

View full text Add to dashboard Cite

BackgroundIn 2013 NHS England set out its strategy for the development of an emergency and urgent care system that is more responsive to patients’ needs, improves outcomes and delivers clinically excellent and safe care. Knowledge about the current evidence base on models for provision of safe and effective urgent care, and the gaps in evidence that need to be addressed, can support this process.ObjectiveThe purpose of the evidence synthesis is to assess the nature and quality of the existing evidence base on delivery of emergency and urgent care services and identify gaps that require further primary research or evidence synthesis.Data sourcesMEDLINE, EMBASE, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and the Web of Science.MethodsWe have conducted a rapid, framework-based, evidence synthesis approach. Five separate reviews linked to themes in the NHS England review were conducted. One general and five theme-specific database searches were conducted for the years 1995–2014. Relevant systematic reviews and additional primary research papers were included and narrative assessment of evidence quality was conducted for each review.ResultsThe review was completed in 6 months. In total, 45 systematic reviews and 102 primary research studies have been included across all five reviews. The key findings for each review are as follows: (1) demand – there is little empirical evidence to explain increases in demand for urgent care; (2) telephone triage – overall, these services provide appropriate and safe decision-making with high patient satisfaction, but the required clinical skill mix and effectiveness in a system is unclear; (3) extended paramedic roles have been implemented in various health settings and appear to be successful at reducing the number of transports to hospital, making safe decisions about the need for transport and delivering acceptable, cost-effective care out of hospital; (4) emergency department (ED) – the evidence on co-location of general practitioner services with EDs indicates that there is potential to improve care. The attempt to summarise the evidence about wider ED operations proved to be too complex and further focused reviews are needed; and (5) there is no empirical evidence to support the design and development of urgent care networks.LimitationsAlthough there is a large body of evidence on relevant interventions, much of it is weak, with only very small numbers of randomised controlled trials identified. Evidence is dominated by single-site studies, many of which were uncontrolled.ConclusionsThe evidence gaps of most relevance to the delivery of services are (1) a requirement for more detailed understanding and mapping of the characteristics of demand to inform service planning; (2) assessment of the current state of urgent care network development and evaluation of the effectiveness of different models; and (3) expanding the current evidence base on existing interventions that are viewed as central to delivery of the NHS England plan by assessing the implications of increasing interventions at scale and measuring costs and system impact. It would be prudent to develop a national picture of existing pilot projects or interventions in development to support decisions about research commissioning.FundingThe National Institute for Health Research Health Services and Delivery Research Programme.

show abstract

Can emergency medical service staff predict the disposition of patients they are transporting?

Cited by 37 publications

References 4 publications

Can advanced paramedics in the field diagnose patients and predict hospital admission?

Can advanced paramedics in the field diagnose patients and predict hospital admission?

Predicting Emergency Department Inpatient Admissions to Improve Same‐day Patient Flow

What evidence is there on the effectiveness of different models of delivering urgent care? A rapid review

Contact Info

Product

Resources

About