The problem with root cause analysis Peerally, Mohammad Farhad; Carr, Susan; Waring, Justin ...
BMJ quality & safety,
05/2017, Letnik:
26, Številka:
5
Journal Article
Conduct a secondary analysis of root cause analysis (RCA) reports of Never Events to determine whether and how Safety-II/resilient healthcare principles could contribute to improving the quality of ...investigation reports and therefore preventing future Never Events.
Qualitative and quantitative retrospective analysis of RCA reports.
A large acute healthcare Trust in London.
None.
None.
Quality of RCA reports, robustness of actions proposed.
RCA reports had low-to-moderate effectiveness ratings and low resilience ratings. Reports identified many system vulnerabilities that were not addressed in the actions proposed. Using a Safety-II/resilient healthcare lens to examine work-as-done and misalignments between demand and capacity would strengthen analysis of Never Events.
Safety-II/Resilient Healthcare concepts can increase the quality of RCA reports and focus attention on prospectively strengthening systems. Recommendations for incorporating Safety-II concepts into RCA processes are provided.
In recent years, fault detection and diagnosis for industrial processes have been rapidly developed to minimize costs and maximize efficiency by taking advantages of cheap sensors and ...microprocessors, data analysis and artificial intelligence methods. However, due to the nonlinear and dynamic characteristics of industrial process data, the accuracy and efficiency of fault detection and diagnosis methods have always been an urgent problem in industry and academia. Therefore, this study proposes an adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window kernel principle component analysis (KPCA) and information geometric causal inference (IGCI). The proposed scheme has three main contributions. Firstly, a research scheme combining moving window KPCA with adaptive threshold is presented to handle the nonlinear and dynamic characteristics of complex industrial processes. Then, the multiobjective evolutionary algorithm is employed to select the optimal hyperparameters for fault detection, which not only avoids the blindness of hyperparameters selection, but also maximize model accuracy. Finally, the IGCI-based fault root-cause analysis method can help field operators to take corrective measures in time to resume the normal process. The proposed scheme is tested by the Tennessee Eastman platform. Its results show that this scheme has a good performance in reducing the faulty false alarms and missed detection rates and locating fault root-cause.
Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are ...available. For tracing the root causes of poor damping and tuning modes of the system, the sensitivity of the modes to components and parameters are needed. The so-called critical admittance-eigenvalue sensitivity based on nodal admittance model has provided a partial solution but omits meaningful directional information. The alternative whole-system impedance model yields participation factors of shunt-connected apparatus with directional information that allows separate tuning for damping and frequency, yet do not cover series-connected components. This paper formalises the relationships between the two forms of impedance models and between the two forms of root-cause analysis. The calculation of system eigenvalue sensitivity in impedance models is further developed, which fills the gaps of previous research and establishes a complete theory of impedance-based root-cause analysis. The theoretical relationships and the tuning of parameters have been illustrated with a three-node passive network, a modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing that tools can be developed for tuning of IBR-rich power systems where only black-box impedance models are available.
•An unsupervised approach for fault detection in rotating machinery.•An unsupervised approach for fault classification based on feature importance ranking.•Possibility of performing root cause ...analysis and to be applied in different faults.•A new contribution to Explainable Artificial Intelligence in rotating machinery.•Industrial application with the possibility to change models according to the dataset.
The monitoring of rotating machinery is an essential task in today’s production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI). Lastly, an analysis of several state-of-art anomaly detection algorithms in rotating machinery is included.
Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality ...engineers must identify the root cause to improve any affected product and process. This root‐cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures.
In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root‐cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications.
The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root‐cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.
Analyze a multicenter cohort of deceased patients after pancreatectomy in high-volume centers in France by performing a root-cause analysis (RCA) to define the avoidable mortality rate.
Despite ...undeniable progress in pancreatic surgery for over a century, postoperative outcome remains particularly worse and could be further improved.
All patients undergoing pancreatectomy between January 2015 and December 2018 and died postoperatively within 90 days after were included. RCA was performed in 2 stages: the first being the exhaustive collection of data concerning each patient from preoperative to death and the second being blind analysis of files by an independent expert committee. A typical root cause of death was defined with the identification of avoidable death.
Among the 3195 patients operated on in 9 participating centers, 140 (4.4%) died within 90 days after surgery. After the exclusion of 39 patients, 101 patients were analyzed. The cause of death was identified in 90% of cases. After RCA, mortality was preventable in 30% of cases, mostly consequently to a preoperative assessment (disease evaluation) or a deficient postoperative management (notably pancreatic fistula and hemorrhage). An inappropriate intraoperative decision was incriminated in 10% of cases. The comparative analysis showed that young age and arterial resection, especially unplanned, were often associated with avoidable mortality.
One-third of postoperative mortality after pancreatectomy seems to be avoidable, even if the surgery is performed in high volume centers. These data suggest that improving postoperative pancreatectomy outcome requires a multidisciplinary, rigorous, and personalized management.
Emergency department (ED) crowding is common and associated with increased costs and negative patient outcomes. The aim of this study was to conduct an in-depth analysis to identify the root causes ...of an ED length of stay (ED-LOS) of more than six hours.
An observational retrospective record review study was conducted to analyse the causes for ED-LOS of more than six hours during a one-week period in an academic hospital in the Netherlands. Basic administrative data were collected for all visiting patients. A root cause analysis was conducted using the PRISMA-method for patients with an ED-LOS > 6 hours, excluding children and critical care room presentations.
568 patients visited the ED during the selected week (January 2017). Eighty-four patients (15%) had an ED-LOS > 6 hours and a PRISMA-analysis was performed in 74 (88%) of these patients. 269 root causes were identified, 216 (76%) of which were organisational and 53 (22%) patient or disease related. 207 (94%) of the organisational factors were outside the influence of the ED. Descriptive statistics showed a mean number of 2,5 consultations, 59% hospital admissions or transfers and a mean age of 57 years in the ED-LOS > 6 hours group. For the total group, there was a mean number of 1,9 consultations, 29% hospital admissions or transfers and a mean age of 43 years.
This study showed that the root causes for an increased ED-LOS were mostly organisational and beyond the control of the ED. These results confirm that interventions addressing the complete acute care chain are needed in order to reduce ED-LOS and crowding in ED's.
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