A systems perspective of managing error recovery and tactical re-planning of operating teams in safety critical domains

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

INTRODUCTION

Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology.

METHOD

To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights.

CONCLUSIONS AND PRACTICAL APPLICATIONS

The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Applying AcciMap to test the common cause hypothesis using aviation near misses

The common cause hypothesis, as applied here, proposes that similar networks of influencing factors may contribute to both adverse outcomes and near misses. This hypothesis has not been evaluated using a systems-thinking perspective. The aims of this study are to evaluate whether networks of contributory and protective factors exist within aviation serious near miss reports and to determine if the common cause hypothesis is applicable in this context. Sixteen incident reports from French civil aviation crash investigation bureau were analysed using the AcciMap method. Contributory and protective factors, and relationships between both were identified via coding of the reports. The results indicate that considering protective factors support a richer picture of incidents and provide support for the common cause hypothesis as measured by similar mean factor volume and sociotechnical levels for both contributory and protective factors. However, the findings also show the direction of relationships among protective and contributory factors may be indicative of a difference among adverse outcomes, near misses, and normal work. Future research should consider how a network of relationships may impact on the common contributory and protective factors found in near misses.

Digital health technology enhances resilient behaviour: evidence from the ward

Purpose

In the healthcare management domain, there is a lack of knowledge concerning the role of resilience practices in improving patient safety. The purpose of this paper is to understand the capabilities that enable healthcare resilience and how digital technologies can support these capabilities.

Design/methodology/approach

Within- and cross-case research methodology was used to study resilience mechanisms and capabilities in healthcare and to understand how digital health technologies impact healthcare resilience. The authors analyze data from two Italian hospitals through the lens of the operational failure literature and anchor the findings to the theory of dynamic capabilities.

Findings

Five different dynamic capabilities emerged as crucial for managing operational failure. Furthermore, in relation to these capabilities, medical, organizational and patient-related knowledge surfaced as major enablers. Finally, the findings allowed the authors to better explain the role of knowledge in healthcare resilience and how digital technologies boost this role.

Practical implications

When trying to promote a culture of patient safety, the research suggests healthcare managers should focus on promoting and enhancing resilience capabilities. Furthermore, when evaluating the role of digital technologies, healthcare managers should consider their importance in enabling these dynamic capabilities.

Originality/value

Although operations management (OM) research points to resilience as a crucial behavior in the supply chain, this is the first research that investigates the concept of resilience in healthcare systems from an OM perspective, with only a few authors having studied similar concepts, such as “workaround” practices.

How do community pharmacies recover from e-prescription errors?

BACKGROUND

The use of e-prescribing is increasing annually, with over 788 million e-prescriptions received in US pharmacies in 2012. Approximately 9% of e-prescriptions have medication errors.

OBJECTIVE

To describe the process used by community pharmacy staff to detect, explain, and correct e-prescription errors.

METHODS

The error recovery conceptual framework was employed for data collection and analysis. 13 pharmacists and 14 technicians from five community pharmacies in Wisconsin participated in the study. A combination of data collection methods were utilized, including direct observations, interviews, and focus groups. The transcription and content analysis of recordings were guided by the three-step error recovery model.

RESULTS

Most of the e-prescription errors were detected during the entering of information into the pharmacy system. These errors were detected by both pharmacists and technicians using a variety of strategies which included: (1) performing double checks of e-prescription information; (2) printing the e-prescription to paper and confirming the information on the computer screen with information from the paper printout; and (3) using colored pens to highlight important information. Strategies used for explaining errors included: (1) careful review of patient's medication history; (2) pharmacist consultation with patients; (3) consultation with another pharmacy team member; and (4) use of online resources. In order to correct e-prescription errors, participants made educated guesses of the prescriber's intent or contacted the prescriber via telephone or fax. When e-prescription errors were encountered in the community pharmacies, the primary goal of participants was to get the order right for patients by verifying the prescriber's intent.

CONCLUSION

Pharmacists and technicians play an important role in preventing e-prescription errors through the detection of errors and the verification of prescribers' intent. Future studies are needed to examine factors that facilitate or hinder recovery from e-prescription errors.

Understanding human management of automation errors

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance.