Abstract
We analysed the trauma triage system at a specific level I trauma centre to assess rates of over- and undertriage and to support recommendations for system improvements. The triage process is designed to estimate the severity of patient injury and allocate resources accordingly, with potential errors of overestimation (overtriage) consuming excess resources and underestimation (undertriage) potentially leading to medical errors.
We first modelled the overall trauma system using risk analysis methods to understand interdependencies among the actions of the participants. We interviewed six experienced trauma surgeons to obtain their expert opinion of the over- and undertriage rates occurring in the trauma centre. We then assessed actual over- and undertriage rates in a random sample of 86 trauma cases collected over a six-week period at the same centre. We employed Bayesian analysis to quantitatively combine the data with the prior probabilities derived from expert opinion in order to obtain posterior distributions. The results were estimates of overtriage and undertriage in 16.1 and 4.9% of patients, respectively.
This Bayesian approach, which provides a quantitative assessment of the error rates using both case data and expert opinion, provides a rational means of obtaining a best estimate of the system's performance. The overall approach that we describe in this paper can be employed more widely to analyse complex health care delivery systems, with the objective of reduced errors, patient risk and excess costs.
References
- American College of Surgeons Committee on TraumaResources for optimal care of the injured patient. 1991Chicago, IL:American College of Surgeons. Google Scholar
Cales R& Trunkey D . 1985Preventable deaths: a review of trauma care systems development. J. Am. Med. Assoc. 254, 1059doi:10.1001/jama.254.8.1059. . Crossref, ISI, Google ScholarChampion H Triage. Trauma care systems& Cales R . 1986pp. 79–109. Eds. Gaithersburg, MD:Aspen. Google ScholarEmerman C, Shade B& Kubincanek J . 1991A comparison of EMT judgment and pre-hospital trauma triage instruments. J. Trauma. 31, 1369–1375. Crossref, PubMed, Google ScholarEsposito T, Offner P, Jurkovich G, Griffith J& Maier R.V . 1995Do pre-hospital trauma center criteria identify major trauma victims?. Arch Surg. 130, 171–176. Crossref, PubMed, Google ScholarHoff W, Tinkoff G, Lucke J& Lehr S . 1995Impact of minimal injuries on a level 1 trauma center. J. Trauma. 33, 408–412. Crossref, Google ScholarHoward R& Matheson J Influence diagrams. The principles and applications of decision analysis, Howard R& Matheson J . 1984pp. 719–762. Eds. Menlo Park, CA:Strategic Decision Group. Google ScholarKnopp R, Yanegi A, Kallsen G, Geide A& Doehring L . 1988Mechanism of injury and anatomic injury as criteria for prehospital trauma triage. Ann. Emerg. Med. 17, 895–902.doi:10.1016/S0196-0644(88)80666-8. . Crossref, PubMed, ISI, Google ScholarO'Keefe G, Jurkovich G, Copass M& Maier R . 1999Ten-year trend in survival and resource utilization at a level I trauma center. Ann. Surg. 229, 409–415.doi:10.1097/00000658-199903000-00015. . Crossref, PubMed, ISI, Google ScholarPaté-Cornell M.E . 1986Warning systems in risk management. Risk Anal. 5, 223–234.doi:10.1111/j.1539-6924.1986.tb00210.x. . Crossref, ISI, Google ScholarPaté-Cornell M.E . 1999Medical application of engineering risk analysis and anesthesia patient risk illustration. Am. J. Ther. 6, 245–255.doi:10.1097/00045391-199909000-00004. . Crossref, PubMed, Google ScholarPaté-Cornell M.E, Murphy D, Lakats L& Gaba D . 1996Patient risk in anesthesia: probabilistic risk analysis and management improvements. Ann. Oper. Res. 67, 211–233.doi:10.1007/BF02187030. . Crossref, ISI, Google ScholarPaté-Cornell M.E, Murphy D, Lakats L& Gaba D . 1997Anesthesia patient risk: a quantitative approach to organizational factors and risk management options. Risk Anal. 17, 511–523.doi:10.1111/j.1539-6924.1997.tb00892.x. . Crossref, PubMed, ISI, Google ScholarResnic F.S, Zou K.H, Faihung V.D, Apostolakis G& Ohno-Machado L . 2004Exploration of a Bayesian updating methodology to monitor the safety of interventional cardiovascular procedures. Med. Decis. Making. 4, 399–407.doi:10.1177/0272989X04267012. . Crossref, ISI, Google ScholarShatney C& Sensaki K . 1994Trauma team activation for mechanism of injury blunt trauma patients: time for a change. J. Trauma. 37, 275–282. Crossref, PubMed, Google ScholarStone M . 1961The linear opinion pool. Ann. Math. Statist. 32, 1339–1342.doi:10.1214/aoms/1177704873. . Crossref, Google Scholar
