Barriers to safety and efficiency in robotic surgery docking.

BACKGROUND: The introduction of new technology into the operating room (OR) can be beneficial for patients, but can also create new problems and complexities for physicians and staff. The observation of flow disruptions (FDs)-small deviations from the optimal course of care-can be used to understand how systems problems manifest. Prior studies showed that the docking process in robotic assisted surgery (RAS), which requires careful management of process, people, technology and working environment, might be a particularly challenging part of the operation. We sought to explore variation across multiple clinical sites and procedures; and to examine the sources of those disruptions. METHODS: Trained observers recorded FDs during 45 procedures across multiple specialties at three different hospitals. The rate of FDs was compared across surgical phases, sites, and types of procedure. A work-system flow of the RAS docking procedure was used to determine which steps were most disrupted. RESULTS: The docking process was significantly more disrupted than other procedural phases, with no effect of hospital site, and a potential interaction with procedure type. Particular challenges were encountered in room organization, retrieval of supplies, positioning the patient, and maneuvering the robot. CONCLUSIONS: Direct observation of surgical procedures can help to identify approaches to improve the design of technology and procedures, the training of staff, and configuration of the OR environment, with the eventual goal of improving safety, efficiency and teamwork in high technology surgery.

Human Factors Integration in Robotic Surgery.

OBJECTIVE: Using the example of robotic-assisted surgery (RAS), we explore the methodological and practical challenges of technology integration in surgery, provide examples of evidence-based improvements, and discuss the importance of systems engineering and clinical human factors research and practice. BACKGROUND: New operating room technologies offer potential benefits for patients and staff, yet also present challenges for physical, procedural, team, and organizational integration. Historically, RAS implementation has focused on establishing the technical skills of the surgeon on the console, and has not systematically addressed the new skills required for other team members, the use of the workspace, or the organizational changes. RESULTS: Human factors studies of robotic surgery have demonstrated not just the effects of these hidden complexities on people, teams, processes, and proximal outcomes, but also have been able to analyze and explain in detail why they happen and offer methods to address them. We review studies on workload, communication, workflow, workspace, and coordination in robotic surgery, and then discuss the potential for improvement that these studies suggest within the wider healthcare system. CONCLUSION: There is a growing need to understand and develop approaches to safety and quality improvement through human-systems integration at the frontline of care.Precis: The introduction of robotic surgery has exposed under-acknowledged complexities of introducing complex technology into operating rooms. We explore the methodological and practical challenges, provide examples of evidence-based improvements, and discuss the implications for systems engineering and clinical human factors research and practice.

A Smartphone Application for Teamwork and Communication in Trauma: Pilot Evaluation "in the Wild".

OBJECTIVE: To evaluate the potential for a smartphone application to improve trauma care through shared and timely access to patient and contextual information. BACKGROUND: Disruptions along the trauma pathway that arise from communication, coordination, and handoffs problems can delay progress through initial care, imaging diagnosis, and surgery to intensive care unit (ICU) disposition. Implementing carefully designed and evaluated information distribution and communication technologies may afford opportunities to improve clinical performance. METHODS: This was a pilot evaluation "in the wild" using a before/after design, 3 month, and pre- post-intervention data collection. Use statistics, usability assessment, and direct observation of trauma care were used to evaluate the app. Ease of use and utility were assessed using the technology acceptance model (TAM) and system usability scale (SUS). Direct observation deployed measures of flow disruptions (defined as "deviations from the natural progression of an procedure"), teamwork scores (T-NOTECHS), and treatment times (total time in emergency department [ED]). RESULTS: The app was used in 367 (87%) traumas during the trial period. Usability was generally acceptable, with higher scores found by operating room (OR), ICU, and neuro and orthopedic users. Despite positive trends, no significant effects on flow disruptions, teamwork scores, or treatment times were observed. CONCLUSIONS: Pilot trials of a clinician-centered smartphone app to improve teamwork and communication demonstrate potential value for the safety and efficiency of trauma care delivery as well as benefits and challenges of "in-the-wild" evaluation.

Morbidity, mortality, and systems safety in non-operating room anaesthesia: a narrative review.

Non-operating room anaesthesia (NORA) describes anaesthesia delivered outside a traditional operating room (OR) setting. Non-operating room anaesthesia cases have increased significantly in the last 20 yr and are projected to account for half of all anaesthetics delivered in the next decade. In contrast to most other medication administration contexts, NORA is performed in high-volume fast-paced environments not optimised for anaesthesia care. These predisposing factors combined with increasing case volume, less provider experience, and higher-acuity patients increase the potential for preventable adverse events. Our narrative review examines morbidity and mortality in NORA settings compared with the OR and the systems factors impacting safety in NORA. A review of the literature from January 1, 1994 to March 5, 2021 was conducted using PubMed, CINAHL, Scopus, and ProQuest. After completing abstract screening and full-text review, 30 articles were selected for inclusion. These articles suggested higher rates of morbidity and mortality in NORA cases compared with OR cases. This included a higher proportion of death claims and complications attributable to inadequate oxygenation, and a higher likelihood that adverse events are preventable. Despite relatively few attempts to quantify safety concerns, it was possible to find a range of systems safety concerns repeated across multiple studies, including insufficient lighting, noise, cramped workspace, and restricted access to patients. Old and unfamiliar equipment, lack of team familiarity, and limited preoperative evaluation are also commonly noted challenges. Applying a systems view of safety, it is possible to suggest a range of methods to improve NORA safety and performance.

Work-system interventions in robotic-assisted surgery: a systematic review exploring the gap between challenges and solutions.

BACKGROUND: The introduction of a robot into the surgical suite changes the dynamics of the work-system, creating new opportunities for both success and failure. An extensive amount of research has identified a range of barriers to safety and efficiency in Robotic Assisted Surgery (RAS), such as communication breakdowns, coordination failures, equipment issues, and technological malfunctions. However, there exists very few solutions to these barriers. The purpose of this review was to identify the gap between identified RAS work-system barriers and interventions developed to address those barriers. METHODS: A search from three databases (PubMed, Web of Science, and Ovid Medline) was conducted for literature discussing system-level interventions for RAS that were published between January 1, 1985 to March 17, 2020. Articles describing interventions for systems-level issues that did not involve technical skills in RAS were eligible for inclusion. RESULTS: A total of 30 articles were included in the review. Only seven articles (23.33%) implemented and evaluated interventions, while the remaining 23 articles (76.67%) provided suggested interventions for issues in RAS. Major barriers identified included disruptions, ergonomic issues, safety and efficiency, communication, and non-technical skills. Common solutions involved team training, checklist development, and workspace redesign. CONCLUSION: The review identified a significant gap between issues and solutions in RAS. While it is important to continue identifying how the complexities of RAS affect operating room (OR) and team dynamics, future work will need to address existing issues with interventions that have been tested and evaluated. In particular, improving RAS-associated non-technical skills, task management, and technology management may lead to improved OR dynamics associated with greater efficiency, reduced costs, and better systems-level outcomes.

Train-the-trainer: Pilot trial for ebola virus disease simulation training.

Background: Highly infectious but rare diseases require rapid dissemination of safety critical skills to health-care workers (HCWs). Simulation is an effective method of education; however, it requires competent instructors. We evaluated the efficacy of an internet-delivered train-the-trainer course to prepare HCWs to care for patients with Ebola virus disease (EVD). Methods: Twenty-four individuals without prior EVD training were recruited and divided into two groups. Group A included nine trainees taught by three experienced trainers with previous EVD training. Group B included 15 trainees taught by five novice trainers without previous EVD training who completed the train-the-trainer course. We compared the efficacy of the train-the-trainer course by examining subject performance, measured by time to complete 13 tasks and the proportion of steps per task flagged for critical errors and risky and positive actions. Trainees' confidence in their ability to safely care for EVD patients was compared with a self-reported survey after training. Results: Overall trainees' confidence in ability to safely care for EVD patients did not differ by group. Participants trained by the novice trainers were statistically significantly faster at waste bagging (P = 0.002), lab specimen bagging (P = 0.004), spill clean-up (P = 0.01), and the body bagging (P = 0.008) scenarios compared to those trained by experienced trainers. There were no significant differences in the completion time in the remaining nine training tasks. Participants trained by novice and experienced trainers did not differ significantly with regard to the proportion of steps in a task flagged for critical errors, risky actions, or positive actions with the exception of the task "Man Down in Gown" (12.5% of steps graded by experienced trainers compared to 0 graded by novice trainers, P = 0.007). Discussion: The online train-the-trainer EVD course is effective at teaching novices to train HCWs in protective measures and can be accomplished swiftly.

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.

BACKGROUND: Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration. OBJECTIVE: This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. METHODS: This study used a data set of 861 PSE reports from a large academic hospital's maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier's predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. RESULTS: The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. CONCLUSIONS: This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

Checking all the boxes: a checklist for when and how to use checklists effectively.

Checklists are a type of cognitive aid used to guide task performance; they have been adopted as an important safety intervention throughout many high-risk industries. They have become an ubiquitous tool in many medical settings due to being easily accessible and perceived as easy to design and implement. However, there is a lack of understanding for when to use checklists and how to design them, leading to substandard use and suboptimal effectiveness of this intervention in medical settings. The design of a checklist must consider many factors including what types of errors it is intended to address, the experience and technical competencies of the targeted users, and the specific tools or equipment that will be used. Although several taxonomies have been proposed for classifying checklist types, there is, however, little guidance on selecting the most appropriate checklist type, nor how differences in user expertise can influence the design of the checklist. Therefore, we developed an algorithm to provide guidance on checklist use and design. The algorithm, intended to support conception and content/design decisions, was created based on the synthesis of the literature on checklists and our experience developing and observing the use of checklists in clinical environments. We then refined the algorithm iteratively based on subject matter experts' feedback provided at each iteration. The final algorithm included two parts: the first part provided guidance on the system safety issues for which a checklist is best suited, and the second part provided guidance on which type of checklist should be developed with considerations of the end users' expertise.

Investigating Racial and Ethnic Disparities in Maternal Care at the System Level Using Patient Safety Incident Reports.

BACKGROUND: Maternal mortality in the United States is high, and women and birthing people of color experience higher rates of mortality and severe maternal morbidity (SMM). More than half of maternal deaths and cases of SMM are considered preventable. The research presented here investigated systems issues contributing to adverse outcomes and racial/ethnic disparities in maternal care using patient safety incident reports. METHODS: The authors reviewed incidents reported in the labor and delivery unit (L&D) and the antepartum and postpartum unit (A&P) of a large academic hospital in 2019 and 2020. Deliveries associated with a reported incident were described by race/ethnicity, age group, method of delivery, and several other process variables. Differences across racial/ethnic group were statistically evaluated. RESULTS: Almost two thirds (64.8%) of the 528 reports analyzed were reported in L&D, and 35.2% were reported in A&P. Non-Hispanic white (NHW) patients accounted for 43.9% of reported incidents, non-Hispanic Black (NHB) patients accounted for 43.2%, Hispanic patients accounted for 8.9%, and patients categorized as "other" accounted for 4.0%. NHB patients were disproportionally represented in the incident reports, as they accounted for only 36.5% of the underlying birthing population. The odds ratio (OR) demonstrated a higher risk of a reported adverse incident for NHB patients; however, adjustment for cesarean section attenuated the association (OR 1.25, 95% confidence interval 1.01-1.54). CONCLUSION: Greater integration of patient safety and health equity efforts in hospitals are needed to promptly identify and alleviate racial and ethnic disparities in maternal health outcomes. Although additional systems analysis is necessary, the authors offer recommendations to support safer, more equitable maternal care.

Racial and ethnic disparities in long-term contraception use among the birthing population at an academic hospital in the Southeastern United States.

Ensuring women and birthing people have access to the contraceptive of their choice is essential for patient-centered care, health equity, and reproductive justice. While trends in national data in the United States reveal racial disparities in long-term contraceptive use, health-system and hospital-level investigations are essential to understand disparities and encourage interventions. We used data from 5011 patients who delivered at a large academic hospital to determine the effect of race/ethnicity and social vulnerability index (SVI) on the odds of undergoing a long-term contraceptive procedure. Results indicate that SVI substantially affects the odds of long-term contraception for non-Hispanic White women and birthing people. In contrast, Hispanic and non-Hispanic Black women and birthing people have significantly higher odds of undergoing a long-term contraceptive procedure due to race/ethnicity. Contributions to these disparities may be based on factors including healthcare providers, organizational and external policies. Interventions at all levels of care are essential to address disparities in contraceptive care, outcomes, and patient experience.

Applying an equity lens to hospital safety monitoring: a critical interpretive synthesis protocol.

INTRODUCTION: Hospital safety monitoring systems are foundational to how adverse events are identified and addressed. They are well positioned to bring equity-related safety issues to the forefront for action. However, there is uncertainty about how they have been, and can be, used to achieve this goal. We will undertake a critical interpretive synthesis (CIS) to examine how equity is integrated into hospital safety monitoring systems. METHODS AND ANALYSIS: This review will follow CIS principles. Our initial compass question is: How is equity integrated into safety monitoring systems? We will begin with a structured search strategy of hospital safety monitoring systems in CINAHL, EMBASE, MEDLINE and PsycINFO for up to May 2023 to identify papers on safety monitoring systems generally and those linked to equity (eg, racism, social determinants of health). We will also review reference lists of selected papers, contact experts and draw on team expertise. For subsequent literature searching stages, we will use team expertise and expert contacts to purposively search the social science, humanities and health services research literature to support the development of a theoretical understanding of our topic. Following data extraction, we will use interpretive processes to develop themes and a critique of the literature. The above processes of question formulation, article search and selection, data extraction, and critique and synthesis will be iterative and interactive with the goal to develop a theoretical understanding of equity in hospital monitoring systems that will have practice-based implications. ETHICS AND DISSEMINATION: This review does not require ethical approval because we are reviewing published literature. We aim to publish findings in a peer-reviewed journal and present at conferences.

Using flow disruptions to understand healthcare system safety: A systematic review of observational studies.

This systematic review provides information on the methodologies, measurements and classification systems used in observational studies of flow disruptions in clinical environments. The PRISMA methodology was applied and authors searched two databases (PubMed and Web of Science) for studies meeting the following inclusion criteria: (a) were conducted in a healthcare setting, (b) explored systems-factors leading to deviations in care processes, (c) were prospective and observational, (d) classified observations, and (e) were original research studies published in peer-reviewed journals. Thirty studies were analyzed and a variety of methods were identified for observer training, data collection and observation classification. Although primarily applied in surgery, comparable research has been successfully conducted in other venues such as trauma care, and delivery rooms. The findings of this review were synthesized into a framework of considerations for conducting rigorous methodological studies aimed at understanding clinical systems.

Improving safety in the operating room: Medication icon labels increase visibility and discrimination.

Misreading labels, syringes, and ampoules is reported to make up a 54.4% of medication administration errors. The addition of icons to medication labels in an operating room setting could add additional visual cues to the label, allowing for improved discrimination, visibility, and easily processed information that might reduce medication administration errors. A multi-disciplinary team proposed a method of enhancing visual cues and visibility of medication labels applied to vasoactive medication infusions by adding icons to the labels. Participants were 1.12 times more likely to correctly identify medications from farther away (p < 0.001, AOR = 1.12, 95% CI: 1.02, 1.22) with icons. When icons were present, participants were 2.16 times more likely to be more confident in their identifications (p < 0.001, AOR = 2.16, 95%CI: 1.80, 2.57). Carefully designed icons may offer an additional method for identifying medications, and thus reducing medication administration errors.

A better way: training for direct observations in healthcare.

Direct observation is valuable for identifying latent threats and elucidating system complexity in clinical environments. This approach facilitates prospective risk assessment and reveals workarounds, near-misses and recurrent safety problems difficult to diagnose retrospectively or via outcome data alone. As observers are an instrument of data collection, developing effective and comprehensive observer training is critical to ensuring the reliability of the data collection and reproducibility of the research. However, methodological rigour for ensuring these data collection properties remains a key challenge in direct observation research in healthcare. Although prior literature has offered key considerations for observational research in healthcare, operationalising these recommendations may pose a challenge and unless guidance is also provided on observer training. In this article, we offer guidelines for training non-clinical observers to conduct direct observations including conducting a training needs analysis, incorporating practice observations and evaluating observers and inter-rater reliability. The operationalisation of these guidelines is described in the context of a 5-year multisite observational study investigating technology integration in the operating room. We also discuss novel tools developed during the course our project to support data collection and examine inter-rater reliability among observers in direct observation studies.

Work systems analysis of sterile processing: assembly.

BACKGROUND: Sterile processing departments (SPDs) play a crucial role in surgical safety and efficiency. SPDs clean instruments to remove contaminants (decontamination), inspect and reorganise instruments into their correct trays (assembly), then sterilise and store instruments for future use (sterilisation and storage). However, broken, missing or inappropriately cleaned instruments are a frequent problem for surgical teams. These issues should be identified and corrected during the assembly phase. OBJECTIVE: A work systems analysis, framed within the Systems Engineering Initiative for Patient Safety (SEIPS) model, was used to develop a comprehensive understanding of the assembly stage of reprocessing, identify the range of work challenges and uncover the inter-relationship among system components influencing reliable instrument reprocessing. METHODS: The study was conducted at a 700-bed academic hospital in the Southeastern United States with two reprocessing facilities from October 2017 to October 2018. Fifty-six hours of direct observations, 36 interviews were used to iteratively develop the work systems analysis. This included the process map and task analysis developed to describe the assembly system, the abstraction hierarchy developed to identify the possible performance shaping factors (based on SEIPS) and a variance matrix developed to illustrate the relationship among the tasks, performance shaping factors, failures and outcomes. Operating room (OR) reported tray defect data from July 2016 to December 2017 were analysed to identify the percentage and types of defects across reprocessing phases the most common assembly defects. RESULTS: The majority of the 3900 tray defects occurred during the assembly phase; impacting 5% of surgical cases (n=41 799). Missing instruments, which could result in OR delays and increased surgical duration, were the most commonly reported assembly defect (17.6%, n=700). High variability was observed in the reassembling of trays with failures including adding incorrect instruments, omitting instruments and failing to remove damaged instrument. These failures were precipitated by technological shortcomings, production pressures, tray composition, unstandardised instrument nomenclature and inadequate SPD staff training. CONCLUSIONS: Supporting patient safety, minimising tray defects and OR delays and improving overall reliability of instrument reprocessing require a well-designed instrument tracking system, standardised nomenclature, effective coordination of reprocessing tasks between SPD and the OR and well-trained sterile processing technicians.

Room Size Influences Flow in Robotic-Assisted Surgery.

The introduction of surgical technology into existing operating rooms (ORs) can place novel demands on staff and infrastructure. Despite the substantial physical size of the devices in robotic-assisted surgery (RAS), the workspace implications are rarely considered. This study aimed to explore the impact of OR size on the environmental causes of surgical flow disruptions (FDs) occurring during RAS. Fifty-six RAS procedures were observed at two academic hospitals between July 2019 and January 2021 across general, urologic, and gynecologic surgical specialties. A multiple regression analysis demonstrated significant effects of room size in the pre-docking phase (t = 2.170, df = 54, ß = 0.017, p = 0.035) where the rate of FDs increased as room size increased, and docking phase (t = -2.488, df = 54, ß = -0.017, p = 0.016) where the rate of FDs increased as room size decreased. Significant effects of site (pre-docking phase: p = 0.000 and docking phase: p = 0.000) were also demonstrated. Findings from this study demonstrate hitherto unrecognized spatial challenges involved with introducing surgical robots into the operating domain. While new technology may provide benefits towards patient safety, it is important to consider the needs of the technology prior to integration.

Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design.

BACKGROUND: The integration of high technology into health care systems is intended to provide new treatment options and improve the quality, safety, and efficiency of care. Robotic-assisted surgery is an example of high technology integration in health care, which has become ubiquitous in many surgical disciplines. OBJECTIVE: This study aims to understand and measure current robotic-assisted surgery processes in a systematic, quantitative, and replicable manner to identify latent systemic threats and opportunities for improvement based on our observations and to implement and evaluate interventions. This 5-year study will follow a human factors engineering approach to improve the safety and efficiency of robotic-assisted surgery across 4 US hospitals. METHODS: The study uses a stepped wedge crossover design with 3 interventions, introduced in different sequences at each of the hospitals over four 8-month phases. Robotic-assisted surgery procedures will be observed in the following specialties: urogynecology, gynecology, urology, bariatrics, general, and colorectal. We will use the data collected from observations, surveys, and interviews to inform interventions focused on teamwork, task design, and workplace design. We intend to evaluate attitudes toward each intervention, safety culture, subjective workload for each case, effectiveness of each intervention (including through direct observation of a sample of surgeries in each observational phase), operating room duration, length of stay, and patient safety incident reports. Analytic methods will include statistical data analysis, point process analysis, and thematic content analysis. RESULTS: The study was funded in September 2018 and approved by the institutional review board of each institution in May and June of 2019 (CSMC and MDRH: Pro00056245; VCMC: STUDY 270; MUSC: Pro00088741). After refining the 3 interventions in phase 1, data collection for phase 2 (baseline data) began in November 2019 and was scheduled to continue through June 2020. However, data collection was suspended in March 2020 due to the COVID-19 pandemic. We collected a total of 65 observations across the 4 sites before the pandemic. Data collection for phase 2 was resumed in October 2020 at 2 of the 4 sites. CONCLUSIONS: This will be the largest direct observational study of surgery ever conducted with data collected on 680 robotic surgery procedures at 4 different institutions. The proposed interventions will be evaluated using individual-level (workload and attitude), process-level (perioperative duration and flow disruption), and organizational-level (safety culture and complications) measures. An implementation science framework is also used to investigate the causes of success or failure of each intervention at each site and understand the potential spread of the interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25284.

Work systems analysis of sterile processing: decontamination.

BACKGROUND: Few studies have explored the work of sterile processing departments (SPD) from a systems perspective. Effective decontamination is critical for removing organic matter and reducing microbial levels from used surgical instruments prior to disinfection or sterilisation and is delivered through a combination of human work and supporting technologies and processes. OBJECTIVE: In this paper we report the results of a work systems analysis that sought to identify the complex multilevel interdependencies that create performance variation in decontamination and identify potential improvement interventions. METHODS: The research was conducted at a 700-bed academic hospital with two reprocessing facilities decontaminating approximately 23 000 units each month. Mixed methods, including 56 hours of observations of work as done, formal and informal interviews with relevant stakeholders and analysis of data collected about the system, were used to iteratively develop a process map, task analysis, abstraction hierarchy and a variance matrix. RESULTS: We identified 21 different performance shaping factors, 30 potential failures, 16 types of process variance, and 10 outcome variances in decontamination. Approximately 2% of trays were returned to decontamination from assembly, while decontamination problems were found in about 1% of surgical cases. Staff knowledge, production pressures, instrument design, tray composition and workstation design contributed to outcomes such as reduced throughput, tray defects, staff injuries, increased inventory and equipment costs, and patient injuries. CONCLUSIONS: Ensuring patients and technicians' safety and efficient SPD operation requires improved design of instruments and the decontamination area, skilled staff, proper equipment maintenance and effective coordination of reprocessing tasks.