Optimizing Triage and Hospitalization in Adult General Medical Emergency Patients. The Triage Project

Schuetz, Philipp; Hausfater, Pierre; Amin, Devendra; Haubitz, Sebastian; Fässler, Lukas; Grolimund, Eva; Kutz, Alexander; Schild, Ursula; Caldara, Zeljka; Regez, Katharina; Zhydkov, Andriy; Kahles, Timo; Nedeltchev, Krassen; von Felten, Stefanie; De Geest, Sabina; Conca, Antoinette; Schäfer-Keller, Petra; Huber, Andreas; Bargetzi, Mario; Buergi, Ulrich; ... (2013). Optimizing Triage and Hospitalization in Adult General Medical Emergency Patients. The Triage Project. BMC emergency medicine, 13, p. 12. BioMed Central 10.1186/1471-227X-13-12

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Background: Patients presenting to the emergency department (ED) currently face inacceptable delays in initial treatment, and long, costly hospital stays due to suboptimal initial triage and site-of-care decisions. Accurate ED
triage should focus not only on initial treatment priority, but also on prediction of medical risk and nursing needs to improve site-of-care decisions and to simplify early discharge management. Different triage scores have been
proposed, such as the Manchester triage system (MTS). Yet, these scores focus only on treatment priority, have suboptimal performance and lack validation in the Swiss health care system. Because the MTS will be introduced
into clinical routine at the Kantonsspital Aarau, we propose a large prospective cohort study to optimize initial patient triage. Specifically, the aim of this trial is to derive a three-part triage algorithm to better predict (a)
treatment priority; (b) medical risk and thus need for in-hospital treatment; (c) post-acute care needs of patients at the most proximal time point of ED admission.
Methods/design: Prospective, observational, multicenter, multi-national cohort study. We will include all consecutive medical patients seeking ED care into this observational registry. There will be no exclusions except for
non-adult and non-medical patients. Vital signs will be recorded and left over blood samples will be stored for later batch analysis of blood markers. Upon ED admission, the post-acute care discharge score (PACD) will be recorded.
Attending ED physicians will adjudicate triage priority based on all available results at the time of ED discharge to the medical ward. Patients will be reassessed daily during the hospital course for medical stability and readiness for discharge from the nurses and if involved social workers perspective. To assess outcomes, data from electronic medical records will be used and all patients will be contacted 30 days after hospital admission to assess vital and
functional status, re-hospitalization, satisfaction with care and quality of life measures.
We aim to include between 5000 and 7000 patients over one year of recruitment to derive the three-part triage algorithm. The respective main endpoints were defined as (a) initial triage priority (high vs. low priority) adjudicated by the attending ED physician at ED discharge, (b) adverse 30 day outcome (death or intensive care unit admission) within 30 days following ED admission to assess patients risk and thus need for in-hospital treatment and (c) post acute care needs after hospital discharge, defined as transfer of patients to a post-acute care institution, for early recognition and planning of post-acute care needs. Other outcomes are time to first physician contact, time to initiation of adequate medical therapy, time to social worker involvement, length of hospital stay, reasons fordischarge delays, patient’s satisfaction with care, overall hospital costs and patients care needs after returning home.
Discussion: Using a reliable initial triage system for estimating initial treatment priority, need for in-hospital treatment and post-acute care needs is an innovative and persuasive approach for a more targeted and efficient
management of medical patients in the ED. The proposed interdisciplinary , multi-national project has unprecedented potential to improve initial triage decisions and optimize resource allocation to the sickest patients
from admission to discharge. The algorithms derived in this study will be compared in a later randomized controlled trial against a usual care control group in terms of resource use, length of hospital stay, overall costs and
patient’s outcomes in terms of mortality, re-hospitalization, quality of life and satisfaction with care.

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