Patient safety, staff moral and system performance are at the heart of healthcare delivery. Investigation of adverse outcomes is one strategy that enables organisations to learn and improve. Healthcare is now understood as a complex, possibly the most complex, socio-technological system. Despite this the use of a 20th century linear investigation model is still recommended for the investigation of adverse outcomes. In this review the authors use data gathered from the investigation of a real life healthcare near incident and apply three different methodologies to the analysis of this data. They compare both the methodologies themselves and the outputs generated. This illustrates how different methodologies generate different system level recommendations. The authors conclude that system based models generate the strongest barriers to improve future performance. Healthcare providers and their regulatory bodies need to embrace system based methodologies if they are to effectively learn from, and reduce future, adverse outcomes.
CarayonP.Human factors of complex sociotechnical systems. Appl Ergon2006;
37: 525–535.
2.
ShorrockSTWilliamsC.Human factors and ergonomics in practice: Improving system performance and human well-being in the real world.
Boca Raton, FL:
CRC Press, 2017, p.422.
3.
MakaryMADanielM.Medical error-the third leading cause of death in the US. BMJ2016;
353: 1–5.
4.
PanagiotiMKhanKKeersRN, et al.
Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ2019;
366: l4185.
5.
ShojaniaKGDIxon-WoodsM.Estimating deaths due to medical error: the ongoing controversy and why it matters. BMJ Qual Saf2017;
26: 423–428.
UnderwoodPWatersonP.Systems thinking, the Swiss cheese model and accident analysis: a comparative systemic analysis of the Grayrigg train derailment using the ATSB, AcciMap and STAMP models. Accid Anal Prev2014;
68: 75–94.
HollnagelE.Safety-I and safety-II: the past and future of safety management.
Farnham, UK:
Ashgate Publishing Limited, 2014, p.187.
10.
Farhad PeerallyMCarrSWaringMd-W J.The problem with root cause analysis. BMJ Qual Saf2017;
26: 417–422.
11.
WatersonP. Analysis of medication dosing error related to Computerized Provider Order Entry (CPOE) System – a comparison of ECF, HFACS, STAMP and AcciMap Models. 2019.
12.
SvedungIRasmussenJ.Graphic representation of accident scenarios: mapping system structure and the causation of accidents. Saf Sci2002;
40: 397–417.
13.
Australian Transport Safety Bureau. Analysis; causality and proof in safety investigations. Aviation Research and Analysis Report AR-2007-05, Canberra, Australia, 2008.
14.
HerreraaIAWoltjerR.Comparing a multi-linear (STEP) and systemic (FRAM) method for accident analysis.Reliab Eng Syst Saf2010;
95: 1269–1275.
15.
UnderwoodPWatersonP.Systemic accident analysis: examining the gap between research and practice. Accid Anal Prev2013;
55: 154–164.
BraithwaiteJWestbrookMTMallockNA, et al.
Experiences of health professionals who conducted root cause analyses after undergoing a safety improvement programme. Qual Saf Health Care2006;
15: 393–399.
ShappellSAWiegmannDA.A human error approach to accident investigation: the taxonomy of unsafe operations. Int J Aviat Psychol2009;
7: 269–291.
25.
BuchananAShappellSDillerT, et al.
The human factors analysis classification system (HFACS) applied to health care. Am J Med Qual2014 May–Jun;
29: 181–190.
26.
SalmonPMCornelissenMTrotterMJ.Systems-based accident analysis methods: a comparison of accimap, HFACS, and STAMP. Saf Sci2012;
50: 1158–1170.
27.
RasmussenJ.Risk management in a dynamic society – a modelling problem.Saf Sci1997;
27: 183–213.
28.
BranfordKNaikarNHopkinsA. Guidelines for AcciMap analysis. In: Learning from High Reliability Organisations, p.193–212, www.researchgate.net/publication/245024991 (2009, accessed 15 January 2021).
