Award-Winning Animation: SystemsThinking - A New Direction in Healthcare Incident Investigation

For more information, please visit www.systemsthinkinglab.com
Richard Clive Holman Award was given in 2019 by CIEHF for Effective Communication of the Value of Human Factors
Many thanks for helping translation: Carlos Aceves González for Spanish; Gyoyoung Song for Korean; Yiqing Ji and Tong Ye for Chinese; M. C. Emre Simsekler and Serpil Acar for Turkish. Subtitles in these languages are available.

[@PowerBook_life]

Very clear analysis of System be Thinking. Makes a lot of sense now.

[@hercjayacademy]

A very interesting perspective. Beautiful visuals too!

[@lerennerel876]

Great video, makes my presentation easier to discuss.

[@walterricardomencholavasquez]

Very good, thank you

[@stephenprineas1578]

Thank you Gyuchan. Great video. It is especially interesting to me that Root Cause Analysis, in your video, is framed as an NHS 'performance-management tool' targeting clinician errors. This was never the intention when RCAs were introduced to Australian Healthcare. Instead it was meant as a 'systems-oriented' tool that was supposed to avoid blame. However (aside from the obvious shortcomings of applying RCA methodology in complex systems) there is a fundamental problem that healthcare executives and clinicians have not yet understood: when a serious adverse - analysis for determining accountability, and analysis for preventing future adverse events. Both processes are important and necessary (yes, some unsafe acts _are_ blameworthy), but should be kept separate - otherwise any well-intended systems improvement tool (RCA/London Protocol etc) just becomes another (albeit more sophisticated) blame weapon.

[@stephenprineas1578]

Sorry - there was a gap in my text which should have read "when a serious adverse event occurs, at least two analytical processes are triggered - analysis for determining accountability, and analysis for preventing future adverse events".

[@brunobarbosa7696]

sensacional

[@gidi1899]

I believe in system view. I believe we need the population to notice when a system is effecting the situation, and research specific systems. Also a big part of this view, is understanding the strings between systems. Too many strict examples, we can't see the picture of system vs non-system because you didn't show enough good examples, didn't make it clear the measures taken in comparing the two, and really missed on what make a situation better handled by system view. Thanks anyway for pushing the idea.

[@AdityasinghSisodiya]

Good

[@mauricioweber8879]

Easy complexity...