Systematic Human Error Reduction And Prediction Approach Research Articles

Potential errors during final inspection and certification process of an aircraft component

PurposeAccording to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.Design/methodology/approachThe Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.FindingsIn this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.Originality/valueThis study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

The application of human reliability analysis to carpal tunnel decompression.

Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

Human reliability analysis in deep excavation projects using a fuzzy Bayesian HEART-5M integrated method: case of a residential tower in north Tehran

Objectives. Numerous labourers lose their lives or suffer from injuries and disabilities yearly due to the lack of safety enforcement in construction projects and accidents caused by excavation collapses. The identification and ranking of human errors have always been a central concern in civil engineering. Previous studies on excavation work and related risks have focused on retaining structure methods, while human errors may be a significant contributor to accidents and near misses. Methods. This study identified human errors in deep excavation projects using hierarchical task analysis (HTA) and a systematic human error reduction and prediction approach (SHERPA). Results. The fuzzy Bayesian human error assessment and reduction technique (HEART)-5M method was implemented to determine the human error probability (HEP) for all case-study tasks. Critical tasks were obtained as ‘drainage system execution’, ‘water and wastewater pipes’, ‘gas pipes’, ‘checking cracks in surrounding buildings’ and ‘checking soil slippage’ with probability levels of 0.46, 0.44, 0.44, 0.37 and 0.37, respectively. Finally, remedial measures were presented for crucial tasks. Conclusions. Six unbiased experts approved the model’s desirability. The suggested approach can serve as a valuable guide for all project stakeholders in identifying, evaluating and taking corrective actions in similar projects.

Quantitative and Qualitative Assessment of Safety Status in a Medical Centre: Experience from Implementing Standard Safety System

Background: An accident is an unforeseen occurrence that happens in all organs, including medical centers, due to unsafe conditions and practices which cause damage and sometimes irreparable injuries. Establishing safety system in the medical center seek to prevent harm to both patients and health care professionals. Objectives: The aim of this study was to assess safety status in a medical center before and after implementing standard safety system. Materials & Methods: This case-study was carried out among the staff of a hospital in Guilan province, Iran in 2018-2019. The information was collected through observation and interview with staff and checking the observance of instructions and safety tips. Then, the possibility of needle stick injury was identified by calculating accident indices. human errors were assessed using risk assessment using Systematic human error reduction and prediction approach (SHERPA). Results: A total of 9 departments of a selected hospital were studied and the accident and human errors were identified. The recurrence rate of the accident in 2019 compared to 2018 had a decreasing rate from 11.36 to 4.09 (safe_T_score=-3.14). Risk assessment using SHERPA method in the gynecology ward revealed 4 important types of errors in this department. Conclusion: There was considerable reduction in frequency and severity of needle stick injuries after establishment of safety system in the hospital. The SHERPA method, detailed the task errors and specific remedial measure to correct the task. The results of this study can be helpful for medical staff, managers, employers, and safety experts in identifying and preventing the causes of the accident.

Exploring the human factors of medication errors in community pharmacy: a mixed methods study

Abstract Introduction Medication errors and near misses in the community pharmacy dispensing process have the potential to adversely impact patient safety. The World Health Organisation has identified the importance of Human factors (HF) in the Patient Safety Curriculum guide (1). However, there is a lack of knowledge on how HF principles have or could be applied in the community pharmacy setting. Adopting a HF approach and using qualitative methods can provide in-depth understanding of factors that contribute to these errors, and contribute to intervention development that may improve patient safety. Aim The study aims to investigate the factors contributing to medication dispensing errors and near misses in community pharmacy, and to gather pharmacists’ views of these factors and how these could be mitigated. Methods Three Irish community pharmacies were recruited and provided details of the last ten dispensing errors or near misses which occurred. Each error was mapped to the Hierarchical Task Analysis (HTA) steps developed for this study, and mapped to the Systematic Human Error Reduction and Prediction Approach (SHERPA) framework (2). A detailed report was prepared for each pharmacy outlining the error analysis, with recommendations to prevent the errors in future. A qualitative semi-structured interview was conducted with the three pharmacists in the recruited pharmacies to discuss the report, and analysed by thematic analysis. Results A total of 30 medication errors/near misses were analysed (10 errors reached the patients and were not administered). The HTA developed outlines 185 subtasks potentially involved in dispensing a prescribed medication. On mapping to the SHERPA framework, selection-based errors were the most frequently reported error category (21/30, 70%); this included incorrect product selection from the shelf (17/30, 56.7%) and incorrect product selection at the point of computer entry (4/30, 13.3%). Of the 75 HTA steps involved across the 30 errors, the most frequent point of error was in the gathering medication steps (47/75, 62.7%), followed by the pharmacist accuracy check steps (16/75, 21.3%) and patient mix up errors (5/30, 16.7%). The pharmacist interview themes found that cognitive burden, fatigue, distraction and staffing deficiencies were reported as increasing the risk of error and near miss. Knowledge gaps and inexperience with certain medications were also reported as contributing to errors. Recommendations to prevent errors included changes to the physical environment (e.g. using product shelf alerts), improved checking processes and taking short mental breaks. Conclusion This study found that the most common errors/near misses were at the product selection stage of dispensing, with pharmacy accuracy checks sometimes, but not always, detecting these before they reached the patient. Medication errors occur due to several varying and often interacting factors; cognitive burden and lack of standardised medication checking processes. Despite the small sample size and potential for social desirability bias in the interviews, this study has demonstrated how HF techniques can be applied to the dispensing process as a means of understanding and preventing error occurrence in the community pharmacy. References (1) Vosper H, Hignett S. A UK perspective on human factors and patient safety education in pharmacy Curricula. Am J Pharm Educ. 2017;82:6184. (2) Sutherland A, Ashcroft DM, Phipps DL. Exploring the human factors of prescribing errors in paediatric intensive care units. Arch Dis Child 2019;104:588-595.

Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario

Engine damage as a consequence of foreign object debris (FOD) during flight is frequently caused by birds. One approach to minimising disruption caused by this damage is to provide flight crew with accurate information relating to the continuing operational status of the aircraft’s engines. Before designing such avionic systems however, understanding of current procedures is needed. Hierarchical Task Analysis (HTA) and Systematic Human Error Reduction and Prediction Approach (SHERPA) were used to identify potential failures that flight crew may make when managing an engine bird strike. Workshops with commercial pilots generated insights into current practice and a commercial pilot SME reviewed outputs for accuracy. Over 200 potential failures were identified, most commonly related to communication. Remedial measures, considering future avionic systems, are proposed to mitigate identified failures. This analysis provides a starting point for future design concepts for assisting flight crew in dealing with engine malfunction due to FOD strikes. Practitioner summary: Hierarchical Task Analysis was conducted to show all tasks involved in dealing with an in-flight aircraft engine bird strike. Systematic Human Error Reduction and Prediction Approach analysis was performed and over 200 possible failures were identified when managing this event. Remedial measures are proposed to help mitigate possible failures.

Predicting dispensing errors in community pharmacies: An application of the Systematic Human Error Reduction and Prediction Approach (SHERPA).

IntroductionThe objective of this study was to use a prospective error analysis method to examine the process of dispensing medication in community pharmacy settings and identify remedial solutions to avoid potential errors, categorising them as strong, intermediate, or weak based on an established patient safety action hierarchy tool.MethodFocus group discussions and non-participant observations were undertaken to develop a Hierarchical Task Analysis (HTA), and subsequent focus group discussions applied the Systematic Human Error Reduction and Prediction Approach (SHERPA) focusing on the task of dispensing medication in community pharmacies. Remedial measures identified through the SHERPA analysis were then categorised as strong, intermediate, or weak based on the Veteran Affairs National Centre for Patient Safety action hierarchy. Non-participant observations were conducted at 3 pharmacies, totalling 12 hours, based in England. Additionally, 7 community pharmacists, with experience ranging from 8 to 38 years, participated in a total of 4 focus groups, each lasting between 57 to 85 minutes, with one focus group discussing the HTA and three applying SHERPA. A HTA was produced consisting of 10 sub-tasks, with further levels of sub-tasks within each of them.ResultsOverall, 88 potential errors were identified, with a total of 35 remedial solutions proposed to avoid these errors in practice. Sixteen (46%) of these remedial measures were categorised as weak, 14 (40%) as intermediate and 5 (14%) as strong according to the Veteran Affairs National Centre for Patient Safety action hierarchy. Sub-tasks with the most potential errors were identified, which included ‘producing medication labels’ and ‘final checking of medicines’. The most common type of error determined from the SHERPA analysis related to omitting a check during the dispensing process which accounted for 19 potential errors.DiscussionThis work applies both HTA and SHERPA for the first time to the task of dispensing medication in community pharmacies, detailing the complexity of the task and highlighting potential errors and remedial measures specific to this task. Future research should examine the effectiveness of the proposed remedial solutions to improve patient safety.

Prospective risk assessments of patient safety events related to drug shortages in hospitals: Three actor-level perspectives

Background: The increasing number of drug shortages (DSs) worldwide calls for more proactive solutions to prevent the negative impacts of DSs on patient care. Such solutions require in-depth knowledge about potential patient safety risks related to DSs, the processes of recognizing and managing DSs, the contextual setting in which DSs occur, and the actors involved.Objective: The aim of the study is to use prospective risk assessment to identify patient safety risks in hospitals associated with the management of DSs among actors at national, regional and local level in Denmark.Methods: Healthcare Failure Mode and Effect Analysis (HFMEA) was employed in composition with elements from the Systematic Human Error Reduction and Prediction Approach (SHERPA) and the Systems-Theoretic Accident Model and Processes (STAMP). Potential risks related to DS management across three actor levels (national, regional and local) in the Danish healthcare system were described. Each actor level consisted of six participants that were identified using a purposive sampling strategy. Processes and sub-processes related to managing critical DSs were outlined and the actors identified, prioritized and rated potential failure modes, causes and consequences related to the processes. Recommendations to mitigate failures were proposed for high risk failures modes.Results: Overall, a total of 167 failure modes were identified across the three actor levels. At the national level, the process of DS management consisted of 17 sub-processes, from which 71 failure modes were identified. Nine of them were rated as high risk. At regional level, 7 sub-processes and 33 failure modes were identified, of which 9 were rated as high risk. At local level, 14 sub-processes and 63 failure modes were identified, of which 32 were rated as high risk. The high-risk failures were related to a lack of IT support in the medication modules, underestimation of patient safety aspects, and insufficient personnel training and patient information.Conclusion: Exploring DS management failure modes across actor levels provided an overview of interrelated failures. Potential solutions related to high risk failures were developed to ensure that actors ensure patient safety related to DS in healthcare.

SHERPA기법을 이용한 항공기 착륙상황에서 발생 가능한 인적오류 예측

This study aims to examine probable human errors when landing an airplane by the use of SHERPA(systematic human error reduction and prediction approach) and propose methods for preventing the predictive human errors. It has been reported that human errors are concerned with a lot of accidents or incidents of an airplane. It is significant to predict presumable human errors, particularly in the operation mode of human-automation interaction, and attempt to reduce the likelihood of predicted human error. By referring to task procedures and interviewing domain experts, we analyzed airplane landing task by using HTA(hierarchical task analysis) method. In total, 6 sub-tasks and 19 operations were identified from the task analysis. SHERPA method was used for predicting probable human error types for each task. As a result, we identified 31 human errors and predicted their occurrence probability and criticality. Based on them, we suggested a set of methods for minimizing the probability of the predicted human errors. From this study, it can be said that SHERPA can be effectively used for predicting probable human error types in the context of human-automation interaction needed for navigating an airplane.

Task, usability, and error analyses of ambulance-based telemedicine for stroke care

Past research has established that telemedicine improves stroke care through decreased time to treatment and more accurate diagnoses. The goals of this study were to 1) study how clinicians complete stroke assessment using a telemedicine system integrated in ambulances, 2) determine potential errors and usability issues when using the system, and 3) develop recommendations to mitigate these issues. This study investigated use of a telemedicine platform to evaluate a stroke patient in an ambulance with a geographically distributed caregiving team comprised of a paramedic, nurse, and neurologist. It first determined the tasks involved based on 13 observations of a simulated stroke using 39 care providers. Based on these observational studies, a Hierarchical Task Analysis (HTA) was developed, and subsequently, a heuristic evaluation was conducted to determine the usability issues in the interface of the telemedicine system. This was followed by a Systematic Human Error Reduction and Prediction Approach (SHERPA) to determine the possibility of human error while providing care using the telemedicine work system. The results from the HTA included 6 primary subgoals categorizing the 97 tasks to complete the stroke evaluation. The heuristic evaluation found 123 unique violations to heuristics, with an average severity of 2.38. One hundred and thirty-one potential human errors were found with SHERPA, the two most common being miscommunication and selecting an incorrect option. Several recommendations are proposed, including improvement of labeling, consistent formatting, rigid or suggested formatting for data input, automation of task structure and camera movement, and audio/visual improvements to support communication.

HUMAN ERROR IDENTIFICATION IN BUS DRIVER WORK USING SHERPA AND HEART

Traffic accidents, especially with a large capacity such as bus, can be caused by several factors. According to the Indonesian Directorate General of Land Transportation of the Ministry of Transportation in 2012, the factors causing traffic accidents in Indonesia are a human factor of 93.52%, vehicle factor by 2.76%, road factor 3.23%, and environmental factor by 0.49%. Therefore, research is needed to identify which human error has the greatest probability of accident cause using Systematic Human Error Reduction and Prediction Approach (SHERPA) method to identify job desk using Hierarchical Task Analysis (HTA) and Human Error Assessment Reduction Technique (HEART) method to calculate Human Error Probability (HEP). Based on the calculation of Human Error Probability value known the highest HEP value is not running the vehicle in accordance with the provisions of the speed that has been set with 0.375. Next is not to record or forget to record the damage that occurred during the trip with a value of 0.21. It did not check Bus equipment with a HEP value of 0.19, did not report when there was a problem on the street with a HEP value of 0.18 and did not break for the next preparation for departure with a HEP value of 0.15

Human reliability analysis of bronchoscope-assisted percutaneous dilatational tracheostomy: implications for simulation-based education

BackgroundTeaching and assessing clinical procedures requires a clear delineation of the individual steps required to successfully complete the procedure. For decades, human reliability analysis (HRA) has been used to identify the steps required to complete technical procedures in higher risk industries. However, the use of HRA is uncommon in healthcare. HRA has great potential supporting simulation-based education (SBE) in two ways: (1) to support training through the identification of the steps required to complete a clinical procedure; and (2) to support assessment by providing a framework for evaluating performance of a clinical procedure. The goal of this study was to use HRA to identify the steps (and the risk associated with each of these steps) required to complete a bronchoscope-assisted percutaneous dilatational tracheostomy (BPDT). BPDT is a potentially high-risk minimally invasive procedure used to facilitate tracheostomy placement at the bedside or in the operating theatre.MethodsThe subgoals, or steps, required to complete the BPDT procedure were identified using hierarchical task analysis. The Systematic Human Error Reduction and Prediction Approach (SHERPA) was then used to identify potential human errors at each subgoal, the level of risk and how these potential errors could be prevented.ResultsThe BPDT procedure was broken down into 395 subgoals, of which 18% were determined to be of high-risk. The most commonly identified remediation strategies for reducing the risk of the procedure included: checklist implementation and audit, statutory and mandatory training modules, simulation training, consultant involvement in all procedures, and fostering a safety-focused hospital culture.ConclusionThis study provides an approach for how to systematically identify the steps required to complete a clinical procedure for both training and assessment. An understanding of these steps is the foundation of SBE. HRA can identify ‘a correct way’ for teaching learners how to complete a technical procedure, and support teachers to give systematic and structured feedback on performance.

Risk analysis of human error in interaction design by using a hybrid approach based on FMEA, SHERPA, and fuzzy TOPSIS

AbstractUser experience is the focus of interaction design, and designing for errors is crucial for improving user experience. One method of designing for errors is to identify human errors and then initiate corrective actions on high‐risk errors to reduce their adverse effects. In this study, we proposed a hybrid approach for risk analysis of human error concerning user experience of interactive systems. In this approach, systematic human error reduction and prediction approach (SHERPA) is first adopted to identify human error concerning user experience. Subsequently, failure mode and effect analysis (FMEA) is used to analyze the risk factors of the error, including occurrence, severity, and detection. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is then used to calculate the risk priority number to rank the errors. Finally, corrective actions for high‐risk errors are recommended. An in‐vehicle information system was used to demonstrate the proposed approach. The results indicated that the proposed approach can effectively analyze the risk of human error concerning user experience and be used as a universal reliability approach for improving user experience in interaction design.

Predicting Design-Induced Error on the Flight Deck : An Aircraft Engine Oil Leak Scenario.

To explore the types of errors that commercial pilots may make when trying to resolve a suspected engine oil leak using the interfaces currently available. The decisions that pilots make often have to be made quickly and under time pressure, with the emphasis on avoiding critical situations from arising. To make the correct decisions, it is vital that pilots have accurate and up-to-date information available. However, interaction with flight deck interfaces may lead to error if they are not effectively designed. A hierarchical task analysis was conducted using evidence from pilot interview data to understand the pilots' typical response to a suspected engine oil leak scenario. This was used as the primary input into the Systematic Human Error Reduction and Prediction Approach (SHERPA). A total of 108 possible errors were identified. The most common error type was a retrieval error, in which flight crews may retrieve the wrong information about the engine. A number of remedial measures are proposed to try and overcome such issues. This analysis provides an initial starting point for identifying potential future design ideas that can assist the pilots in dealing with oil leaks. This work has identified the value of applying human error identification methodologies to the assessment of current flight deck processes surrounding engine oil leaks. The method presented permits the operational analysis of possible errors on the flight deck and facilitates the proposition of remedial measures to implement technological innovations that can mitigate error.

Human error analysis on production process of door products with SHERPA and HEART method

Company engaged in manufacturing that produces door leaf. This company is a factory whose work is done semi-automatic that man has a role in sustainability of production process. A problem in the production process of the door where there is a defect which is the main cause factor is human (human error). This problem can be solved by performing human error analysis with SHERPA and HEART methods. Systematic Human Error Reduction and Prediction Approach (SHERPA) analyzed occurrence of human error by using a hierarchical task input basic level while Human Error Assessment and Reduction Technique (HEART) part the calculation reliability is defined as how much operator made a mistake in the task that should be done. From the research found the results obtained SHERPA methods work items that have a critical level that is 2.2 (too long), 8.2 ( too long), and 11.2 (the flat part of the door yet). Based on the research with HEART method obtained the total value of HEP is 0.4986 where the value is influenced by EPCs factor. Based on the data obtained then possible troubleshooting for item 2.2, 8.2, and 11.2 issues is by improving operator skill.

Identifying and reducing risks in functional endoscopic sinus surgery through a hierarchical task analysis.

ObjectiveTo develop a hierarchical task listing of steps required to perform successful Functional Endoscopic Sinus Surgery (FESS). To complete a technical and human factor analysis of tasks resulting in the identification of errors, frequency of occurrence, severity, and reduction through remediation.MethodsA triangulation of methods was used in order to derive the steps required to complete a FESS: 1) a literature review was carried out of published descriptions of FESS techniques; 2) observations of three FESS; 3) interviews with surgeons on FESS techniques. Data sets were combined to develop a task analysis of a correct approach to conducting FESS. A review by 12 surgeons, and observation of 25 FESS resulted in refinement of the task analysis. With input from five consultant surgeons and one consultant anesthetist, a Systematic Human Error Reduction and Prediction Approach (SHERPA) was used to identify the risks and mitigating steps in FESS.ResultsTen tasks and 49 subtasks required for a correct approach to completing FESS were identified based on literature review and expert consensus. A risk score for each subtask was calculated from a suitable risk matrix. Risk reduction methods at each subtask were detailed. High‐scoring subtasks were evaluated and varying strategies examined to reduce the likelihood and mitigate the impact of error. The study demonstrates the usefulness of the HTA and SHERPA approach in standardization and optimization of clinical practice in order to improve patient safety.ConclusionHierarchical Task Analysis and SHERPA are valuable tools to deconstruct expert performance and to highlight potential errors in FESS. The HTA and SHERPA approach to surgical procedures are useful learning and assessment tools for novice surgeons. The information derived offers the opportunity to improve surgical training and enhance patient safety by identifying high‐risk steps in the procedure, and how risk can be mitigated.Level of Evidence2c Outcomes Research

Neonatal nasogastric tube feeding in a low-resource African setting \u2013 using ergonomics methods to explore quality and safety issues in task sharing

BackgroundSharing tasks with lower cadre workers may help ease the burden of work on the constrained nursing workforce in low- and middle-income countries but the quality and safety issues associated with shifting tasks are rarely critically evaluated. This research explored this gap using a Human Factors and Ergonomics (HFE) method as a novel approach to address this gap and inform task sharing policies in neonatal care settings in Kenya.MethodsWe used Hierarchical Task Analysis (HTA) and the Systematic Human Error Reduction and Prediction Approach (SHERPA) to analyse and identify the nature and significance of potential errors of nasogastric tube (NGT) feeding in a neonatal setting and to gain a preliminary understanding of informal task sharing.ResultsA total of 47 end tasks were identified from the HTA. Sharing, supervision and risk levels of these tasks reported by subject matter experts (SMEs) varied broadly. More than half of the tasks (58.3%) were shared with mothers, of these, 31.7% (13/41) and 68.3% were assigned a medium and low level of risk by the majority (≥4) of SMEs respectively. Few tasks were reported as ‘often missed’ by the majority of SMEs. SHERPA analysis suggested omission was the commonest type of error, however, due to the low risk nature, omission would potentially result in minor consequences. Training and provision of checklists for NGT feeding were the key approaches for remedying most errors. By extension these strategies could support safer task shifting.ConclusionInclusion of mothers and casual workers in care provided to sick infants is reported by SMEs in the Kenyan neonatal settings. Ergonomics methods proved useful in working with Kenyan SMEs to identify possible errors and the training and supervision needs for safer task-sharing.

IDENTIFIKASI BAHAYA DAN PENILAIAN RISIKO OPERATOR MESIN GERINDA

Research in the fabrication unit of PT. Bangun Sarana Baja Gresik to determine hazard identification and risk assessment in grinding machine operators. This research uses Systematic Human Error Reduction and Prediction Approach (SHERPA) to determine error in identify hazard consisting of task stage, error mode, error description, consequence analysis and recovery analysis. The AS/NZS 4360: 2004 standard is used to determine the severity, likelihood and risk matrix scales in assessing risks consisting of frequency analysis, severity analysis, and criticality analysis that results in the level of risk at each stage of the task. Population in this research is all grinding machine operator at PT. BangunSarana Baja Gresik which are 60 operators. The sample of this research is as much as 20 grinding machine operatorsfabrication unit of PT. Bangun Sarana Baja Gresik with purposive sampling technique.Hazard identification on the grinding process such as falling material, unstable material condition, cracked grinding wheel, grinding wheel specifications are incompatible with the grinding machine and vice versa, the cable is flaked, melted, even broken, cracked cable terminal, starter (switch on/off) is damaged, sparks, gram sparks, ergonomic body positions, high speed grinding wheels, welding rays, noise and tool hand vibration can result in scratches, abrasions, dislocations, fractures, scratches broken grinding stones, electric shock, pincers, burns, low back pain, sore eyes, hand arm vibration syndrome and carpal tunnel syndrome in the worker's body. Risk assessment for low risk task stage is 6 error descriptions which means acceptable risk and no action required, medium risk is 31 error descriptions which means more alert and corrective action is recommended if cost effective, and high risk is 12 error descriptions which means the risk to be aware and there are actions needed to control the risks. Keywords: grinding, hazard identification, risk assessment

A Hybrid Model for Human Error Probability Analysis

The aim of the present research is to propose a hybrid model for human error probability analysis based on Cognitive Reliability and Error Analysis Method (CREAM) and Systematic Human Error Reduction and Prediction Approach (SHERPA) Method. The model aims to provide a theoretical framework to understand human behavior and to predict error probability. This model is based on a fundamental distinction between competence and control that offers a way of describing how performance depends on context. The most important result of this work is to provide a model that can be used in the preventive phase of an accident in order to mitigate the damages.

Identification and assessment of medical errors in the triage area of an educational hospital using the SHERPA technique in Iran

Accidents caused by human error are prominent in the medical field. The present study identified medical errors in the emergency triage area by assessing the tasks of all healthcare workers employed in the triage area of an educational hospital in Tehran, Iran in 2014. Data were collected using the systematic human error reduction and prediction approach (SHERPA). The tasks and sub-tasks were determined and analyzed using hierarchical analysis and the errors were extracted. A total of 199 human errors were identified in the different tasks. The rate of error for action was 46.8%, checking was 25.6%, retrieval was 8.5%, communication was 12.1% and selection was 7%. Rate of unacceptable and unfavorable risks were 21.1% and 38.6%, respectively. SHERPA was shown to be an appropriate technique for detecting medical errors. The establishment of control programs should be a high priority in the management and implementation of health facilities in triage areas.