Computer Driven Triage

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Computer Driven Triage

• An Evidenced Based Change

Erika Bartee, Julie Chenoweth, Deborah Graves,
Hannelore Hager
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Emergency Department
Overload

• In the United States, the Number of ED visits
  increased by 20 from 1995 to 2005 however the
  number of hospitals with an ED has decreased by
  9, which puts an even heavier load onto the
  remaining Emergency Departments (Gilboy Tanabe,
  2008).

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Problem Defined

• Overcrowding
• Patients leave without being seen (LWS) (Chan et
  al., 2005).
• Delays in treatment (Gilboy et al., 2005).
• Triage drift (Dong et al., 2005).
• Patient satisfaction (Dong et al., 2006).
• Staff satisfaction (Gilboy Tanabe, 2008).

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Problem Defined

• Increases hospital liability
• Loss of revenue (Gilboy Tanabe, 2008).
• Potential loss of 3,881,506 annually (Falvo,
  Grove, Stachura, Zirkin, 2007)
• Loss of 1,086 in potential revenue per hour of
  diversion status (McConnell, Richards,
  Daya, Weathers,  Lowe R.A).

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Research Question

• What is the efficacy of a computer-assisted
  emergency department triage system compared to
  traditional memory-led triage systems?

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Patient Outcomes Definition

• Accuracy and Reliability of triage Scores
• Patient overall satisfaction.

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Staff Satisfaction Defined

• Staff confidence in the computer system
• Ease of implementation
• Effect on staff workload

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Hospital Cost Defined

• Improved Resource utilization
• Prevention of lost Funds
• Decrease in liability
• Improved budget
• projection

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Review of Literature

• Prospective, observational and retrospective
  studies to fill need for uniform and reliable
  triage method.

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Benefits of Computerization

• Predictive validity for rates of admission
  resource utilization (Dong et al 2007 a).
• Decreased triage drift.

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Disadvantages

• Inability to determine acuity in the elderly and
  patients with dementia (Hwang Morrison, 2007).
• Power outages

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Patient Outcomes

• Good interrater reliability
• Good agreement between two nurses using e-TRIAGE
  (kappagt 0.8 CI95) (Dong et al., 2005).
• Not affected by differing levels of overcrowding
  (Dong et al., 2005).
• Exact agreement in 89 of patients (Maningas,
  2006).

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Patient Outcomes

• Good interrater reliability
• Agreement with expert review panel
• Memory-based fair (0.2lt klt 0.4)
• e-TRIAGE moderate (0.4lt kappalt 0.6)
• - (Dong et al., 2005).

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Patient Outcomes

• Average ED length of stay (LOS) improved from 163
  min to 126 min (Maningas, 2006).

• Long wait times was number one reason for leaving
  the ED without being seen (Gilboy Tanabe, 2008).

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Hospital Cost

• 40 of those who LWS sought care at another ED
  (Gilboy Tanabe, 2008).
• Predictive Validity
• Significant differences in hospital charges
  (plt0.0001) (Maningas et al.,2006).

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Hospital Cost

• Predictive validity (cont.)
• Cost (p0.05).
• Odds for a specialist consultation and CT scan.
• ED length of stay (plt0.001)
• -(Dong et al., 2007).

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Staff Satisfaction

• No statistical data

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Reliability During Peak Hours

• Not affected by overcrowding (Dong et al., 2006)

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Current ESI recomendations

• Standardization of triage across all Emergency
  Departments in the U.S. by using the 5-Level
  Emergency Severity Index (Gilboy et al., 2005).

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Local Variations

• 5 hospitals with ED in Greater Tulsa
• Varying triage systems.
• Door to Floor
• Level-3 paper triage
• Level -4 paper triage
• Level -5 paper triage
• Level -5 computer assisted

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• Computer-assisted 5-level triage system.
• Optional manual nurse override.
• Training program.
• Experienced triage Nurse.

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Evaluation

• Improved patient outcomes
• Decreased wait times, improved patient acuity
  scores, and improved patient satisfaction.

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Evaluation

• Improved cost effectiveness
• Resource utilization
• Staff utilization
• Did the ED budget fall within expected range?

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Evaluation

• Improved staff satisfaction with the
  implementation of the computer triage system.
• Ease of implementation for the staff.
• Affects on staff workload.
• Staff satisfaction with the process.

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Evaluation

• Formal and informal
• Quantitative data (Descriptive statistics, cost
  analysis, etc.).
• Qualitative data (Follow-up calls/survey after
  discharge, suggestions in ED waiting room, etc.).

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Planned Change

• Lewins model of linear change.
• Early and ongoing analysis of the barriers and
  facilitators for change (Yoder-Wise, 2007).

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Lewins Stages of Change

• Unfreezing awareness of a problem.
• Experiencing incorporation of a new idea.
• Refreezing change is accepted as standard.
• (Yoder-Wise 2007).

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Change Evaluation Involves

• Surveys
• Structured or Unstructured observation.
• Analysis of the Implementation.
• Identification of additional alterations.
• (Yoder-Wise, 2007).

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Evaluation of Effectiveness

• Looks at all the outcomes.
• Improved patient outcomes.
• Cost effectiveness.
• Staff satisfaction.

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Recommendations for Study

• Long term patient outcomes.
• Actual effects on cost.
• Effects on staff satisfaction.
• Effects on different sized hospitals.
• Universal measures of outcomes.

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New research questions

• What is the long term effect of using
  computerized triage on employee retention?
• What is the long term effect of using
  computerized triage on the numbers of patients
  who leave the Emergency Department without being
  seen?

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References

• Aronsky, D., Jones, I., Raines, B., Hemphill, R.,
  Mayberry, S. R., Luther, M. A., Sluser, T.
  (2008). An integrated computerized triage system
  in the emergency department. AMIA Annual
  Symposium Proceedings 16-20.
• Dong, S. L., Bullard, M. J., Meurer, D. P.,
  Colman, I., Blitz, S., Holroyd, B. R. et.al.
  (2005). Emergency triage Comparing a novel
  computer triage program with standard triage.
  Society of Academic Emergency Medicine. 12(6)
  502-507.
• Dong, S., Bullard, M., Meurer, D., Blitz, S.,
  Ohinmaa, A., Holroyd, B. et.al. (2006).
  Reliability of computerized emergency triage.
  Society of Academic Emergency Medicine. 13(3)
  269-275.
• Dong, S., Bullard, M., Meurer, D., Blitz, S.,
  Akhmetshin, E., Ohinmaa, A., et.al. (2007a).
  Predictive validity of a computerized emergency
  triage tool. Society of Academic Emergency
  Medicine. 14(1) 16-21.

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References

• Dong, S., Bullard, M., Meurer, D., Blitz, S.,
  Holroyd, B. Rowe, B. (2007b). The effect of
  training on nurse agreement using an electronic
  triage system. Canadian Journal of Emergency
  Medicine. 9(4) 260-266.
• Falvo, T., Grove, L., Stachura, R., Zirkin, W.
  (2007). The financial impact of ambulance
  diversions and patient elopement. Society for
  Academic Emergency Medicine. 14(1) 58-62.
• Gilboy, N. Tanabe, P. (2008). Who is leaving
  the emergency department without being seen?
  Advanced Emergency Nursing. 30(1) 3-10.
• Gilboy, N., Tanabe, P., Travers, D. A., Rosenau,
  A. M. Eitel, D. R. (2005, May). Emergency
  Severity Index, Version 4 Implementation
  Handbook. (Publication No. 05-0046-2).
  Rockville, MD Agency for Healthcare Research
  and Quality.

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References

• Hwang Morrison. (2007). Models of geriatric
  care, quality improvement, and program
  dissemination. The Geriatric Emergency
  Department. 55(11) JAGS
• Maningas, P. A., Hime, D. A., Parker, D. E.
  McMurray, T. A. (2006). Computers in emergency
  medicine The soterion rapid triage system
  Evaluation of inter-rater reliability and
  validity. Journal of Emergency Medicine. 30(4)
  461-469.
• McConnell, J.K, Richards, C., Daya,
  M., Weathers, C.C., Lowe, R.A. (2006).
  Ambulance diversion and lost hospital revenues
  Abstract. Annals of emergency. 48(6) 702-710.
  
• Yoder-Wise, P. S. (2007). Leading and managing in
  nursing (4th ed.). St. Louis, MO Mosby Elsevier.

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Questions?