Studies have highlighted the importance of using objective physiological measures in quickly identifying critical patients who are at an increased risk of clinical deterioration and decompensation. In this exploratory study, we investigate the use of physiological measures within a modified Patient at Risk (PAR) framework for identifying potential ICU admissions during ED-MICU handoffs.
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References
1.
ChalfinD.B., Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Critical care medicine, 2007. 35(6): p. 1477-1483.
2.
Chung-EsakiH.M.WilsonJ.G.RodriguezR.M., Caring for the critically ill: A continuum from the emergency department to the intensive care unit. ICU Director, 2011. 2(5): p. 141-146.
3.
McFetridgeB., An exploration of the handover process of critically ill patients between nursing staff from the emergency department and the intensive care unit. Nursing in critical care, 2007. 12(6): p. 261-269.
4.
RoshdyA.Intensive care medicine: navigation into the future!Trends in Anaesthesia and Critical Care, 2017. 15: p. 8-11.
5.
ChoiJ., Symptom assessment in non-vocal or cognitively impaired ICU patients: Implications for practice and future research. Heart & Lung: The Journal of Acute and Critical Care, 2017.
6.
HarrisonD.A., Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. 2015.
7.
AbrahamJ.IhianleI.BurtonS.Exploring Information Seeking Behaviors in Inter-unit Clinician Handoffs. in Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care. 2017. SAGE Publications Sage India: New Delhi, India.
8.
ApkerJ.MallakL.A.GibsonS.C.Communicating in the “gray zone”: perceptions about emergency physician–hospitalist handoffs and patient safety. Academic Emergency Medicine, 2007. 14(10): p. 884-894.
9.
BeckmannU., Incidents relating to the intra-hospital transfer of critically ill patients. Intensive care medicine, 2004. 30(8): p. 1579-1585.
10.
DurieM.L., A “Code ICU” expedited review of critically ill patients is associated with reduced emergency department length of stay and duration of mechanical ventilation. Journal of Critical Care, 2017. 42: p. 123-128.
11.
BangertK., Non-indexed recordings in the intensive care unit. . Medical Clinic Intensive Care and Emergency Medicine, 2016. 111(4), : p. 310-316.
12.
BrandenburgR., The need for ICU admission in intoxicated patients: a prediction model. Clinical toxicology, 2017. 55(1): p. 4-11.
13.
HicksC., VESS10. ICU Admission After EVAR Is Primarily Determined by Hospital Factors, Adds Significant Cost, and Is Often Unnecessary. Journal of Vascular Surgery, 2017. 65(6): p. 8S.
14.
NatesJ.L., ICU admission, discharge, and triage guidelines: a framework to enhance clinical operations, development of institutional policies, and further research. Critical care medicine, 2016. 44(8): p. 1553-1602.
15.
AbrahamJ.ReddyM.C.Re-coordinating activities: an investigation of articulation work in patient transfers. in Proceedings of the 2013 conference on Computer supported cooperative work. 2013. ACM.
16.
EndacottR., Recognition and communication of patient deterioration in a regional hospital: a multi-methods study. Australian critical care, 2007. 20(3): p. 100-105.
17.
GoldhillD., A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia, 2005. 60(6): p. 547-553.
18.
BatesD.W.LeapeL.L., Doing better with critical test results. Jt Comm J Qual Patient Saf, 2005. 31(2): p. 66-7.
19.
FialhoA.S., Data mining using clinical physiology at discharge to predict ICU readmissions. Expert Systems with Applications, 2012. 39(18): p. 13158-13165.
20.
GoldhillD.McNarryA., Physiological abnormalities in early warning scores are related to mortality in adult inpatients. British journal of anaesthesia, 2004. 92(6): p. 882-884.
21.
AndrewsT.WatermanH., Packaging: a grounded theory of how to report physiological deterioration effectively. Journal of advanced nursing, 2005. 52(5): p. 473-481.
22.
ConsidineJ., Vital signs as predictors for aggression in hospital patients (VAPA). Journal of clinical nursing, 2017.
23.
O’DonnellC., Incorporating patient acuity rating score into patient handoffs and the correlation with rapid responses and unexpected ICU transfers. American Journal of Medical Quality, 2017. 32(2): p. 122-128.
24.
GoldhillD., The patient-at-risk team: identifying and managing seriously ill ward patients. ANAESTHESIA-LONDON-, 1999. 54: p. 853-860.
25.
ReesJ.MannC., Use of the patient at risk scores in the emergency department: a preliminary study. Emergency Medicine Journal, 2004. 21(6): p. 698-699.
26.
PriestleyG., Introducing Critical Care Outreach: a ward-randomised trial of phased introduction in a general hospital. Intensive care medicine, 2004. 30(7): p. 1398-1404.
27.
BakkerJ.NijstenM.W.JansenT.C., Clinical use of lactate monitoring in critically ill patients. Annals of intensive care, 2013. 3(1): p. 12.
28.
BarrantesF., Acute kidney injury criteria predict outcomes of critically ill patients. Critical care medicine, 2008. 36(5): p. 1397-1403.
29.
DuH., Early indicators of severity and construction of a risk model for prognosis based upon laboratory parameters in patients with hemorrhagic fever with renal syndrome. Clinical Chemistry and Laboratory Medicine (CCLM), 2014. 52(11): p. 1667-1675.
30.
FalcigliaM., Hyperglycemia-related mortality in critically ill patients varies with admission diagnosis. Critical care medicine, 2009. 37(12): p. 3001.
31.
GoyalM., Point-of-care testing at triage decreases time to lactate level in septic patients. Journal of Emergency Medicine, 2010. 38(5): p. 578-581.
32.
FlenadyT.DwyerT.ApplegarthJ., Explaining transgression in respiratory rate observation methods in the emergency department: a classic grounded theory analysis. International Journal of Nursing Studies, 2017. 74: p. 67-75.
33.
SinghH., Reducing diagnostic errors through effective communication: harnessing the power of information technology. Journal of General Internal Medicine, 2008. 23(4): p. 489-494.
34.
PivaE., Evaluation of effectiveness of a computerized notification system for reporting critical values. American Journal of Clinical Pathology, 2009. 131(3): p. 432-441.
35.
XuT., Clinical decision-making by the emergency department resident physicians for critically ill patients. Frontiers of medicine, 2012. 6(1): p. 89-93.
36.
BionJ.F.AbrusciT.HibbertP., Human factors in the management of the critically ill patient. British journal of anaesthesia, 2010. 105(1): p. 26-33.
37.
LevinS., Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Annals of Emergency Medicine, 2017.
38.
CostaD.K.KahnJ.M., Organizing critical care for the 21st century. Jama, 2016. 315(8): p. 751-752.