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Does Point-of-Care Testing impact length of stay in emergency departments (EDs)? A before and after study of 26 rural and remote EDs Ling Li Euan McCaughey Juliana Iles-Mann Andrew Sargeant Johanna I Westbrook Andrew Georgiou Centre for

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Does Point-of-Care Testing impact length of stay in emergency departments (EDs)? A before and after study of 26 rural and remote EDs

Ling Li Euan McCaughey Juliana Iles-Mann Andrew Sargeant Johanna I Westbrook Andrew Georgiou

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Centre for Health Systems and Safety Research

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Point-of-Care Testing (PoCT)

  • One of the fastest growing sectors in in-vitro

diagnostics (Huckle 2013)

  • Fast growth facilitated by
  • advances in technology
  • greater integration with electronic systems
  • implementation of policies and standards

enhancing test accuracy and safety

(RCPA QAP 2012) Twitter: #HIC18 @lli_sydney

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  • Refers to “tests performed near patients and outside a traditional

laboratory” (Nichols 2013)

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PoCT: Key advantages

  • Enhanced access, esp. in rural and remote areas (Shephart

2013)

  • Faster test turnaround time, decision making and

treatment (Lee-Lewandrowski 2010; Rooney 2014)

  • Increased patient/clinician satisfaction (Blattner 2010)

Twitter: #HIC18 @lli_sydney

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The world’s largest accredited managed PoCT service

  • 550 PoCT devices
  • 360 EDs
  • 185 hospitals

(NSW Health Pathology, 2018)

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http://www.pathology.health.nsw.gov.au/clinical-services/point-of-care-testing

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Aim: To examine the impact of PoCT

  • n the length of stay (LOS)

in rural and remote EDs

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Study design and setting

  • A retrospective before and after study
  • 26 rural and remote EDs in Murrumbidgee

Local Health District (LHD)

  • Study period: Jan 2013 – Apr 2015
  • PoCT implementations: Mar – Apr 2014

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Available PoCT tests

iStat Cartridge PoCT Tests cTnI Troponin I CG4+ Bicarbonate, PCO2, Lactate, pH, sO2, PO2, HCO3, Base Excess Chem8+ Potassium, Sodium, Chloride, Bicarbonate, Anion Gap, Ionised Calcium, Glucose, Urea Nitrogen, Creatinine

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Data reconciliation and quality assessment

  • Only 29% of PoCT data

were reconciled from AQURE to LIS

  • PoCT results would have

been used for the decision making

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Study population

Patients presenting with a circulatory system illness – unplanned, 1st presentation only (N=7961) Patients discharged home from EDs (N=3808) Pre-PoCT (N=2092) Post- PoCT (N=1716)

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The 4-hour rule

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P<0.0001 82.2% (95% CI: 80.5% - 83.8%) 86.8% (95% CI: 85.2% - 88.4%)

Proportion of patients discharged home from EDs within 4-hours of presentation

Post Pre

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Observed ED LOS

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Statistical modelling

Generalised Estimating Equation (GEE) model

  • Correlation of patients presented to the same EDs
  • With a log-link function to fit skewed ED LOS
  • Adjusting for patient demographics and clinical

characteristics

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Estimated ED LOS (minutes)

Post Pre

Pre VS Post, p=0.07

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134 (95% CI: 116 – 155) 123 (95% CI: 109 – 139)

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Other significant factors

50 100 150

>=71 51-70 31-50 <31

Age category, p=0.02

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Estimated ED LOS (minutes)

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Other significant factors

50 100 150 200

Less Urgent (T5) Potentially serious (T4) Potentially life threatening (T3) Imminently life threatening (T2) Immediately life threatening (T1)

Triage category, p=0.04

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Estimated ED LOS (minutes)

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Other significant factors

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50 100 150 200

7 PM-1 AM 1 PM-7 PM 7 AM-1 PM 1 AM-7 AM

ED arrival time, p=0.02

Estimated ED LOS (minutes)

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Other significant factors

20 40 60 80 100 120 140 160

Sun Sat Fri Thu Wed Tue Mon

Day of week, p=0.02

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Estimated ED LOS (minutes)

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Discussion

  • Not conclusive after considering other important clinical factors
  • Consistent with existing limited international evidence (Rooney 2014; Pecoraro

2014)

  • ED LOS varied with time of day and day of week (Li 2015) – Better

targeting ED resources and staffing to reduce ED LOS

  • Less than 1/3 of PoCT data was reconciled to the LIS – Lack of real-

time patient information in rural and remote EDs

  • Sophisticated modelling approach could be applied in the future as

the quality of PoCT data improves.

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Twitter: #HIC18 @lli_sydney

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Ethics approval Greater Western Area Health Service Human Research Ethics Committee (Project

  • No. LNR/15/GWAHS/26)

Site Specific Assessment approval Far West LHD (SSA No. LNRSSA/15/GWAHS/48), Murrumbidgee LHD (SSA No. LNRSSA/15/MLHD/8) Western NSW LHD (SSA No. LNRSSA/15/GWAHS/49) This project was funded by NSW Health Pathology.

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Health Informatics Conference 2018

Research Team

Maria Dahm (Post-Doctoral Research Fellow) Andrew Georgiou (Professor) Ling Li (Senior Research Fellow) Euan McCaughey (Post-Doctoral Research Fellow) Virginia Mumford (Post-Doctoral Research Fellow) Keira Robinson (Research Assistant) Johanna Westbrook (Director, Professor)

Steering Committee

Sue Carter (Director, Planning and Performance NSW Health) Dominic Dwyer (Network Director, Pathology West) Julianna Iles-Mann (Pathology ICT Manager, Pathology West) Andrew Sargeant (Director Point of Care Testing Services) Michael Whiley (Director of Clinical Strategy and Reform) Roger Wilson (Chief Pathologist)

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Selected references

  • J.H. Nichols, Point-of-care testing, in: The Immunoassay Handbook: Theory and

applications of ligand binding, ELISA and related techniques, D. Wild, ed., Elsevier Oxford, UK, 2013, pp. 455-463.

  • Royal College of Pathologists of Australasia, Point of care testing, Common Sense

Pathology April 2015 (2015), 1-8.

  • M. Shephard, Point-of-Care testing in Australia: The status, practical advantages, and

benefits of community resiliency, Point of Care 12 (2013), 41-45.

  • E. Lee-Lewandrowski and K. Lewandrowski, Point-of-care testing in the Emergency

Department, in: Point-of-Care Testing: Needs, Opportunity and Innovation, C. Price, A. St John, and L.L. Kricka, eds., AACC Press, Washington, DC, 2010, pp. 397-410.

  • K.D. Rooney and U.M. Schilling, Point-of-care testing in the overcrowded emergency

department - Can it make a difference?, Critical Care 18 (2014).

  • L. Li, A. Georgiou, E. Vecellio, A. Eigenstetter, G. Toouli, R. Wilson, and W. JI, The impact
  • f pathology testing on Emergency Department length of stay: A multi-hospital

longitudinal study applying a cross-classified random effect modeling approach. , Academic Emergency Medicine 22 (2015), 338-346.

Twitter: #HIC18 @lli_sydney

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Thank you

Email: ling.li@mq.edu.au Twitter: @lli_Sydney Website: www.aihi.mq.edu.au

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