Human Factors in Financial Trading: An Analysis of Trading Incidents

Online patient feedback as a safety valve: An automated language analysis of unnoticed and unresolved safety incidents

Safety reporting systems are widely used in healthcare to identify risks to patient safety. But, their effectiveness is undermined if staff do not notice or report incidents. Patients, however, might observe and report these overlooked incidents because they experience the consequences, are highly motivated, and independent of the organization. Online patient feedback may be especially valuable because it is a channel of reporting that allows patients to report without fear of consequence (e.g., anonymously). Harnessing this potential is challenging because online feedback is unstructured and lacks demonstrable validity and added value. Accordingly, we developed an automated language analysis method for measuring the likelihood of patient-reported safety incidents in online patient feedback. Feedback from patients and families (n = 146,685, words = 22,191,427, years = 2013-2019) about acute NHS trusts (hospital conglomerates; n = 134) in England were analyzed. The automated measure had good precision (0.69) and excellent recall (0.98) in identifying incidents; was independent of staff-reported incidents (r = -0.04 to 0.19); and was associated with hospital-level mortality rates (z = 3.87; p < 0.001). The identified safety incidents were often reported as unnoticed (89%) or unresolved (21%), suggesting that patients use online platforms to give visibility to safety concerns they believe have been missed or ignored. Online stakeholder feedback is akin to a safety valve; being independent and unconstrained it provides an outlet for reporting safety issues that may have been unnoticed or unresolved within formal channels.

John Senders, Human Error, and System Safety

OBJECTIVE

I examine John Senders' work and discuss his influence on the study of error causation,error mitigation, and sociotechnical system safety.

BACKGROUND

John Senders' passing calls for an evaluation of the impact of his work.

METHOD

I review literature and accident investigation findings to discuss themes in Senders' work and potential associations between that work and error causation and system safety.

RESULTS

Senders consistently emphasized empirical rigor and theoretical exploration in his research, with the desire to apply that work to enhance human performance. He has contributed to changing the way error has been viewed, and to developing and implementing programs and techniques to mitigate error. While a causal relationship between Senders' work and safety cannot be established, an association can be drawn between his research and efforts to mitigate error.

CONCLUSION

Because of Senders' work, we have a better understanding of error causation and enhanced ways of mitigating system errors. However, new sources of error, involving advanced systems and operators' knowledge and understanding of their functionalities can, if not addressed, degrade system safety.

APPLICATION

Modifications to advanced automation and operator training are suggested, and research to improve operator expertise in interacting with automated systems proposed.

Modeling Automation With Cognitive Work Analysis to Support Human-Automation Coordination

Cognitive work analysis is useful to develop displays for complex situations, but it has not been well explored in providing support for human-automation coordination. To fill this gap, we propose a degree of automation (DOA) layering approach, demonstrated by modeling an automated financial trading domain, with a goal of supporting interface design in this domain. The abstraction hierarchy and the decision ladder each adopted an additional layer, mapping functions allocated to the trader and to the automation. In addition to the mapping, we marked the four stages of automation on the decision ladder to provide guidance on representing the function allocation at the task level. Next, we compared the DOA layering approach to how automation was represented in the cognitive work analysis literature. We found that a DOA-layered decision ladder, which included well-developed knowledge of the stages and levels of automation, can be suited to modern automated systems with different DOAs. This study suggests that the DOA layering approach has important implications for designing automation displays and deciding stages and levels of automation and may be a useful approach for modeling adaptive automation.