Properties for formally assessing the performance level of human-human collaborative procedures with miscommunications and erroneous human behavior

Pan, Dan; Bolton, Matthew L.

doi:10.1016/j.ergon.2016.04.001

Cited by 25 publications

(11 citation statements)

References 13 publications

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“…A fair amount of research has gone into investigating how formal methods (and especially model checking) can be used to evaluate erroneous human behavior in complex systems Bolton, Bass, & Siminiceanu, 2012Pan & Bolton, 2016). The vast majority of these analyses are concerned with finding specific unsafe system conditions.…”

Section: Formal Methods and Probabilistic Model Checkingmentioning

confidence: 99%

A Formal Human Reliability Analysis of a Community Pharmacy Dispensing Procedure

Zheng¹,

Bolton²,

Daly

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Medication errors in community pharmacies are a major threat to patient health and safety. Current experimental and observational studies are insufficient to address the medication error problem due to its complexity. Thus, we adapt a well-validated human reliability analyses (HRA) approach for use with the probabilistic model checking, to create and validate a formal approach that allows the analysts to predict medication error rates and explore interventions for different community pharmacy procedures. We use the method to analyze a common dispensing procedure of community pharmacies and compare our results to published error rates. Finally, we discuss our results and explore how our method could be further developed in the future.

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Section: Formal Methods and Probabilistic Model Checkingmentioning

confidence: 99%

A Formal Human Reliability Analysis of a Community Pharmacy Dispensing Procedure

Zheng¹,

Bolton²,

Daly

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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“…The concern with communication at the operational level is evident in some current discussions [3][4][5][6][7][8][9][10][11][12]. Among the objectives set out in these recent studies are the development of communication practices on the shop floor to facilitate the implementation of organizational strategies, the definition of a profile for the future shop floor operator, the understanding of the effect of communication and interpersonal relationships skills in employee satisfaction and supply chain integration, the development of human-human collaborative procedures with (and without) communication failure, the verification of the use of information from the supply chain, the investigation of ways to facilitate the interaction of the shop floor worker with Information and Communication Technologies (ICT), and the discussion of accidents caused by language problems, among others.…”

Section: Introductionmentioning

confidence: 99%

“…In the academic environment, the recognition of the importance of internal communication for operational safety is evident, it can also be seen that, in recent years, communication on the shop-floor has been discussed [3][4][5][6][7][8][9][10][11][12]. However, organizations do not have strategies or training for communication on the shop floor [15,16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Operational Communication Through Structural Equation Modeling

et al. 2020

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In this study a model was developed for analyzing communication on the shop floor using a Structural Equation Modeling with Partial Least Squares estimation (Partial Least Square, PLS). The model included as an exogenous variable (independent) Knowledge Management (KM), and four endogenous variables (dependent): Written Communication (WC), Oral Communication (OC), Man interaction with Information and Communication Technologies (M-ICT), and Operational Communication (OpC). The data for the exploratory study were collected in a land-based oil production region in the Northeast of Brazil. The data analysis was performed using the SmartPLS ® software. The structural model supported six of the nine hypotheses proposed, presenting statistical significance and predictive relevance. The evaluation of Pearson's determination coefficients (R 2) demonstrated a satisfactory degree of adjustment and adherence in explaining the variance of endogenous variables. The effect size (f 2) of the variance explained by the exogenous variable in the four endogenous variables was classified as "very strong", as shown: in WC f 2 = 0.802; OC f 2 = 0.825; M-ICT f 2 = 2,534; and OpC f 2 = 2.003. The model presented predictive validity, KM showed effect (q 2) classified as "very high" in the M-ICT and OpC. The effect (q 2) of KM on WC and OC presented predictive relevance between medium and high, with q 2 = 0.337 and q 2 = 0.267, respectively. The causal relationships between the observed variables and the percentage of participation of each indicator in its construct are contributions to the management of information about production.

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“…However, because they are extremely good at finding unexpected problems with interactions between components in complex environments, a growing body of research has been investigating how formal methods (and especially model checking) can be used in human factors engineering (Bolton, Bass, & Siminiceanu, 2013;Weyers, Bowen, Dix, & Palanque, 2017) to find problems in human-automation and human-human interaction. These works have predominantly focused on analyzing the usability of human-machine and human-computer interfaces (Abowd, Wang, & Monk, 1995;Campos & Harrison, 2008;Pa-ternò, 1997); finding potential mode confusions and automation surprises (Bredereke & Lankenau, 2002;Campos & Harrison, 2011;Degani, 2004;Degani & Heymann, 2002;Joshi, Miller, & Heimdahl, 2003;Rushby, 2002); assessing the impact of normative human task behavior on system safety (Aït-Ameur & Baron, 2006;Bolton, Siminiceanu, & Bass, 2011;Houser, Ma, Feigh, & Bolton, 2015;Paternò & Santoro, 2001); assessing the impact of human errors included (Bastide & Basnyat, 2007;Fields, 2001) or generated (Bolton, 2015;Bolton, Bass, & Siminiceanu, 2012;Pan & Bolton, 2016) in task analytic behavior models; or having problems arise organically from cognitive or perceptual models (Cerone, Lindsay, & Connelly, 2005;Hasanain, Boyd, & Bolton, 2015;Hasanain, Boyd, Edworthy, & Bolton, 2017;Rukšėnas, Curzon, Back, & Blandford, 2007).…”

Section: Introductionmentioning

confidence: 99%

Novel Developments in Formal Methods for Human Factors Engineering

Bolton

2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Formal methods are robust tools and techniques for modeling, specifying, and mathematically proving properties about (verifying) systems. They are particularly good at both finding unexpected problems that arise from complex system interactions and proving that specific types of problems will never manifest. Formal methods have predominantly been used in the analysis and design of computer hardware and software systems. However, a growing research area within the human factors engineering community has been examining how formal methods can be used to prove whether problems exist in systems that rely on human-automation and human-human interaction for their safe operation. This symposium contains four papers by researchers who have been pushing the boundaries of where and how formal methods can be used in human factors engineering.

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Properties for formally assessing the performance level of human-human collaborative procedures with miscommunications and erroneous human behavior

Cited by 25 publications

References 13 publications

A Formal Human Reliability Analysis of a Community Pharmacy Dispensing Procedure

A Formal Human Reliability Analysis of a Community Pharmacy Dispensing Procedure

Analysis of Operational Communication Through Structural Equation Modeling

Novel Developments in Formal Methods for Human Factors Engineering

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