Paramedic streaming upon arrival in emergency department: A prospective study

Abstract

Objective The role of paramedics in hospital triage or streaming models has not been adequately explored and is potentially a missed opportunity for enhanced patient flow. The aim of the present study was to assess the concordance between a streaming decision by paramedics with the decision by nurses after arrival to the ED. Methods A prospective observational study was conducted. Paramedics were met at the entrance to the hospital and asked which destination they thought was appropriate (the index test). The ED nurse streaming decision was the reference standard. Cases of discordance were reviewed and assessed for clinical risk by an independent expert panel that was blinded. Results We collected data from 500 cases that were transported by ambulance consisting of 55% males with a median age of 57 years (interquartile range 38–75). The overall concordance between paramedics' and streaming decision was 86.4% (95% confidence interval 83.1–89.1). The concordance was highest among patients streamed to resuscitation and general cubicles. Among discordant cases (n = 68), 39 were streamed to a more acute destination than the paramedic suggested. Of the 68 discordant cases, 56 were deemed to be of no clinical risk. Conclusions Despite limited knowledge of patient load within the ED, paramedics can allocate a streaming destination with high accuracy and this appears to be associated with low clinical risks. Early pre‐hospital notification of streaming destination with proactive allocation of ED destination presents a real opportunity to minimise off‐load times and improve patient flow.

Review article: Pre‐hospital provider clinical judgement upon arrival to the emergency department: A systematic review and meta‐analysis

“…The attitude of PHPs to an expansion of their role upon arrival to the ED should be investigated prior to commencement of any training programmes to build upon these skills. Similarly, risks of inaccurate predictions of disease severity, assessment and outcomes should be weighed against potential benefits of improved times to care 28 …”

Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

“…Our model can be used by the EMS system to divert ambulance requests to other alternatives for those with low acuity 31 . The potential benefits include better reserve of the EMS resources and possible improvement in the outcome of patients who receive medical aid sooner [32][33][34] . For the patients who were predicted not requiring hospitalization, the emergency medical technicians could apply a "treat and release" protocol by giving primary medical aids in the field without transporting the patients to hospitals 35,36 .…”