Background:Health care organizations have failed to replicate the performance of high-reliability organizations (HROs), systems that operate nearly error-free despite inherently complex, interdependent, and time-pressured environments.Purposes:We sought to better understand conditions that facilitate and inhibit HRO in health care by expanding the conceptualization of who contributes to high reliability to include patients. We also examine how cancer care teams and patients describe jointly enacting (or failing to enact) each of the five HRO principles.Methodology/Approach:We conducted a qualitative field study, interviewing 25 oncology clinicians from diverse roles (e.g., surgery, nursing, dentistry) (17 interviews) and eight cancer patients. Interviews were audio-recorded, transcribed, and deductively coded for barriers and facilitators to the five HRO principles.Findings:We identified conditions that supported and hindered high-reliability cancer care. HRO principles commonly overlapped to support or hinder system resilience. A common barrier was insufficient processes to proactively identify clinical deterioration. Patients and their family caregiver(s) were primarily responsible for monitoring their health and identifying and communicating any signs of deterioration. Cancer patients and caregivers were routinely engaged as team members, and patients developed expertise throughout treatment, thereby supporting system safety.Conclusions:These findings expand the conceptualization of how high reliability is produced. Specifically, we highlight the key role that patients play in preventing errors and monitoring care.Practice Implications:Our findings expand our understanding of how HRO principles interact to promote cancer care system safety and elucidate how patients and their family caregivers contribute to system resilience. This work can inform the redesign of care processes to optimize patient safety.

BACKGROUND:Excessive workload is associated with degraded operator performance and outcomes. The construct of surgical "team workload" is poorly specified, and anesthesiologist workload has rarely been measured concurrent with that of operating room nurses and surgeons during the same cases. We sought to measure workload, operationalized as individual and team "case difficulty," as well as the occurrence of non-routine events, from all surgical team members. METHODS:This multicenter prospective observational study at 5 Veterans Affairs medical centers involved 1107 non-cardiac surgical procedures. Individual and team "case difficulty" ratings (1 "very easy" to 10 "very difficult") and the reported occurrence of non-routine events (ie, deviations from optimal care) were independently collected through facilitated survey from surgeons, anesthesia professionals, and operating room nurses before incision (pre-case) and again after skin closure (post-case). RESULTS:At least one non-routine event was reported by at least one team member in 464 (42.0%) of the cases. Overall, nurses were the most likely to report an NRE (in 57% of NRE-containing cases), whereas surgeons were the least likely (40%). Anesthesia professionals and nurses had similar ratings for both the pre-case and post-case difficulty ratings. Surgeons' pre-case and post-case difficulty ratings were at least one unit greater than either of the other two OR team members ( P <0.01). Post-case, anesthesia and nursing team difficulty ratings were at least 0.4 units higher than individual ratings ( P <0.01), whereas surgeons' pre-case and post-case ratings were similar. Only nurses' individual case difficulty ratings were significantly lower post-case versus pre-case. Individual role and team case difficulty ratings were both associated with the reporting of non-routine events. CONCLUSIONS:Capturing surgical team case difficulty ratings and non-routine events is feasible and may shed insight into factors affecting surgical performance. In particular, pre-case difficulty ratings may be a complementary predictor of surgical risk.

Anesthesiologists are sometimes slow to adopt new technologies despite proven benefits. The authors undertook to understand community anesthesiologists’ perspectives and practices regarding neuromuscular monitoring and reversal. Specifically, they sought to generate evidence for why some guidance-congruent practices— i.e. , quantitative train-of-four monitoring, ulnar nerve stimulation, and evidence-based reversal—remain inconsistently adopted despite robust evidence of patient harm from residual neuromuscular blockade and decades of effort to improve adoption. Thirty-six U.S. community anesthesiologists underwent a detailed qualitative cognitive interview. Analysis revealed four clinician archetypes whose neuromuscular monitoring and reversal practices are shaped by identifiable individual and practice-related biases and device- and system-level barriers. Perceived confidence in sugammadex efficacy was frequently cited as a rationale for omitting quantitative neuromuscular monitoring. The authors propose a research and implementation action agenda to close the evidence-to-practice gap in neuromuscular monitoring and reversal.

BACKGROUND:Milestones evaluation is mandated by the Accreditation Council for Graduate Medical Education (ACGME) to help training programs measure residents' progress toward competency and identify specific areas for trainee improvement. Data from training programs raised concerns that Milestones ratings may reflect the year of training more than resident progress toward competency. We examined the relationship between residents' Milestones ratings toward the end of residency training and their performance on the American Board of Anesthesiology (ABA) examinations, widely considered as the gold standard of competency. METHODS:We compared Milestones 2.0 ratings and board scores of all anesthesiologists who completed an ACGME-accredited residency program between July 2021 and June 2022 (AY22) and had their first-time ABA ADVANCED Examination (written), Standardized Oral Examination (SOE) and Objective Structured Clinical Examination (OSCE) performance available by 2023. We first assessed the correlation between the average rating achieved across all 23 Milestones during the last 6 months of residency training and the Z-scores of these three examinations among first-time takers. Then, we evaluated the correlations between 9 specific Milestones and their conceptually related domains tested by the ADVANCED, the SOE, and the OSCE; we calculated Pearson and polychoric correlation coefficients for continuous and ordinal data, respectively. RESULTS:All 23 Milestones 2.0 AY22 ratings were available for 1849 Post Graduate Year (PGY)-4, Clinical Anesthesia Year 3 (CA-3) residents. These were matched to 1799 first-time ADVANCED and 1383 first-time SOE and OSCE takers. The average ACGME Milestones ratings across all competencies were significantly correlated with examination Z-scores (all P < .001)-the ADVANCED (r = 0.135 [95% confidence interval {CI}, 0.089-0.180], the SOE (r = 0.117 [0.065-0.169]), and the OSCE (r = 0.112 [0.060-0.164]). For the domain-specific comparisons, scores on the ADVANCED Examination correlated modestly with the Medical Knowledge Milestone domain (r = 0.289, P < .001), but there were no statistically significant associations between SOE task ratings and their related Milestones domains (ρ = 0.027 to 0.091, P = .31 to 0.81). In comparisons of similar domains evaluated by OSCE stations and the Milestones, 2 were statistically significantly correlated with a weak magnitude (Interpretation of Monitors and Echocardiograms [ρ = 0.093, P = .031] and Ethical Issues [ρ = 0.049, P = .003]) while 2 others were not statistically significant (Application of Ultrasonography [ρ = 0.052, P = .775] and Communication with other Professionals [ρ = 0.052, P = .086]). CONCLUSIONS:There was a modest correlation between the last Medical Knowledge Milestone achieved and the ADVANCED Examination. However, the weak correlations between residency Milestones and the SOE or OSCE performance suggest that the Milestones system, as currently implemented by anesthesiology training programs, does not predict certifying examination performance.

Background  A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in medically complex patients, to mitigate the effects of adverse events, disease progression, and medical error. This challenging problem requires clinical surveillance, early recognition, timely notification of the appropriate clinicians, and effective intervention. Objectives  We determined the feasibility of designing, developing, and implementing the tools and processes to create a surveillance-and-risk prediction system to detect clinical deterioration in cancer outpatients. Methods  We used systems engineering and iterative human-centered design to develop a functional prototype of a surveillance-and-risk prediction system. The system includes passive surveillance involving wearable sensors, active surveillance involving patient event and symptom reporting as well as extraction of selected patient data from the electronic health record (EHR), a predictive model, and communication of estimated risk to clinicians. System usability was evaluated using patient and clinician interviews and clinician ratings using the System Usability Scale (SUS). Results  Fifty of 71 recruited patients enrolled in the feasibility study. Patient-reported outcome measures and clinical data extracted from the EHR were the best predictors of a patient's 7-day risk of experiencing unplanned treatment events (UTEs, i.e., emergency room visits, hospital admissions, or major treatment changes). Deep learning neural network models using these predictors demonstrated modest performance in predicting 7-day UTE risk (PROMS, F-measure: 0.900, area under the receiver operating characteristic curve [AUC-ROC]: 0.983; clinical data from EHR F-measure: 0.625, AUC-ROC: 0.983). Patient risk scores were communicated to clinicians using a risk communication prototype rated favorably by clinicians with a SUS score of 76 out of 100 (median = 80; range: 60-85). Conclusion  We demonstrate the feasibility of a surveillance-and-risk prediction system for detecting and reporting clinical deterioration in cancer outpatients. Future research is needed to fully implement and evaluate system adoption and effectiveness under different clinical situations.

Background:Only 15% of the nearly 30 million Americans with hearing loss use hearing aids, partly due to high cost, stigma, and limited access to professional hearing care. Hearing impairment in adults can lead to social isolation and depression and is associated with an increased risk of falls. Given the persistent barriers to hearing aid use, the Food and Drug Administration issued a final rule to allow over-the-counter hearing aids to be sold directly to adult consumers with perceived mild to moderate hearing loss at pharmacies, stores, and online retailers without seeing a physician or licensed hearing health care professional. Objective:We evaluated the safety and usability of an over-the-counter hearing aid prior to Food and Drug Administration approval and market release. Methods:We first conducted a formative usability test of the device and associated app with 5 intended users to identify outstanding safety and usability issues (testing round 1). Following design modifications, we performed a summative usability test with 15 intended users of the device (testing round 2). We concurrently conducted a test with 21 nonintended users (ie, users with contraindications to use) to ascertain if consumers could determine when they should not use the device, based on the packaging, instructions, and labeling (testing round 3). Participants were asked to complete 2-5 tasks, as if they were using the hearing aid in real life. After each task, participants rated the task difficulty. At the end of each session, participants completed a 10-question knowledge assessment and the System Usability Scale and then participated in debriefing interviews to gather qualitative feedback. All sessions were video recorded and analyzed to identify use errors and design improvement opportunities. Results:Usability issues were identified in all 3 usability testing rounds. There were minimal safety-related issues with the device. Round 1 testing led to several design modifications which then increased task success in round 2 testing. Participants had the most difficulty with the task of pairing the hearing aids to the cell phone. Participants also had difficulty distinguishing the right and left earbuds. Nonintended users did not always understand device contraindications (eg, tinnitus and severe hearing loss). Overall, test findings informed 9 actionable design modifications (eg, clarifying pairing steps and increasing font size) that improved device usability and safety. Conclusions:This study evaluated the usability and safety of an over-the-counter hearing aid for adults with mild to moderate hearing loss. Human factors engineering methods identified opportunities to improve the safety and usability of this direct-to-consumer medical device for individuals with perceived mild-moderate hearing loss.

Introduction: As the number of available treatment options for breast cancer increases, decision-making for patients has become complex. Patients often struggle to make decisions as treatment options can vary in terms of short- and long-term side effects, risks of recurrence, and impact on daily life.1 Numerous decision aids have been developed to support patient decision-making.2 However, sustained implementation and use of these tools remains limited. We propose that cognitive engineering approaches, such as naturalistic decision making (NDM), can provide a deeper understanding of how patients make treatment decisions, which can improve the design of decision support tools. Naturalistic decision making (NDM) is a theoretical perspective and methodological approach used to understand how people make decisions in the real world. Originally developed to understand decision-making of expert firefighters during crises, NDM approaches have been used to understand complex decision-making across domains including the military, offshore oil rigs, and healthcare.3,4 In this study, we used an NDM approach, the critical decision method (CDM), to gain an in-depth understanding of how breast cancer patients make treatment decisions following diagnosis. Methods: We conducted CDM interviews,5 with breast cancer patients diagnosis in the last 12 years. CDM interviews aim to understand critical or difficult events by unpacking the event using structured probes. One researcher conducted each interview over Zoom. We started each interview by asking the patient to reflect back on the beginning of their cancer journey and what they remember about their diagnosis. We then drew a timeline and asked the patient to relay the different treatments they considered or underwent for breast cancer. We then asked “Can you think of a time during your breast cancer journey when you had to make a difficult decision?” and probed patients about that decision. We continued asking patients about their treatment decision-making as time allowed. Each interview was audio-recorded and transcribed. A researcher and a patient advocate coded each interview and created a decision requirements table,6 which detailed the decisions made by the patient, what made that decision challenging, what strategies and information they used, and what their goals were at the time. We then met to discuss and come to consensus. Once a decision requirements table was created for each transcript, we developed aggregate tables and identified key themes. Results: We conducted 20 interviews, averaging 57 minutes each; patient age ranged from 42 to 81 years. Patients described an average of 8 decisions that they made following breast cancer diagnosis. Despite many patients facing the same decisions (e.g., mastectomy vs. lumpectomy), we found variability in which decisions were most difficult for patients. We identified 11 categories of difficult decisions for patients including whether to receive chemotherapy, getting genetic testing, stopping a medication due to side effects, and deciding where to receive treatment. Patients reported feeling time pressure and urgency to make treatment decisions and a fear of regretting their decisions. We found that patients’ firsthand experiences from friends who had cancer influenced their treatment decision-making. Given the heterogeneous nature of breast cancer treatment, this often presented a barrier to decision-making as patients expected to have the same experience and treatment options as their friends. Patients expressed variable goals when making treatment decisions, which often changed throughout their treatment journey. Conclusion: In this study, we explored how breast cancer patients made treatment decisions using NDM methods. This cognitive engineering approach revealed intricacies in the decision-making process of patients that will be valueable for improving the design of decision support tools. Next steps include collaborative design with patients to develop a tool that supports the broad spectrum of treatment decisions made across the patient journey. Citation Format: Megan Salwei, Barbara Voigtman, Janelle Faiman, Carrie Reale, Shilo Anders, Matthew Weinger. Harnessing Cognitive Engineering to Understand Breast Cancer Patient Decision Making [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-07.

BACKGROUND:Hospitalization rates for childhood pneumonia vary widely. Risk-based clinical decision support (CDS) interventions may reduce unwarranted variation. METHODS:We conducted a pragmatic randomized trial in two US pediatric emergency departments (EDs) comparing electronic health record (EHR)-integrated prognostic CDS versus usual care for promoting appropriate ED disposition in children (<18 years) with pneumonia. Encounters were randomized 1:1 to usual care versus custom CDS featuring a validated pneumonia severity score predicting risk for severe in-hospital outcomes. Clinicians retained full decision-making authority. The primary outcome was inappropriate ED disposition, defined as early transition to lower- or higher-level care. Safety and implementation outcomes were also evaluated. RESULTS:The study enrolled 536 encounters (269 usual care and 267 CDS). Baseline characteristics were similar across arms. Inappropriate disposition occurred in 3% of usual care encounters and 2% of CDS encounters (adjusted odds ratio: 0.99, 95% confidence interval: [0.32, 2.95]). Length of stay was also similar and adverse safety outcomes were uncommon in both arms. The tool's custom user interface and content were viewed as strengths by surveyed clinicians (>70% satisfied). Implementation barriers include intrinsic (e.g., reaching the right person at the right time) and extrinsic factors (i.e., global pandemic). CONCLUSIONS:EHR-based prognostic CDS did not improve ED disposition decisions for children with pneumonia. Although the intervention's content was favorably received, low subject accrual and workflow integration problems likely limited effectiveness. Clinical Trials Registration: NCT06033079.

Objectives To support a pragmatic, electronic health record (EHR)-based randomized controlled trial, we applied user-centered design (UCD) principles, evidence-based risk communication strategies, and interoperable software architecture to design, test, and deploy a prognostic tool for children in emergency departments (EDs) with pneumonia. Methods Risk for severe in-hospital outcomes was estimated using a validated ordinal logistic regression model to classify pneumonia severity. To render the results usable for ED clinicians, we created an integrated SMART on Fast Healthcare Interoperability Resources (FHIR) web application built for interoperable use in two pediatric EDs using different EHR vendors: Epic and Cerner. We followed a UCD framework, including problem analysis and user research, conceptual design and early prototyping, user interface development, formative evaluation, and postdeployment summative evaluation. Results Problem analysis and user research from 39 clinicians and nurses revealed user preferences for risk aversion, accessibility, and timing of risk communication. Early prototyping and iterative design incorporated evidence-based design principles, including numeracy, risk framing, and best-practice visualization techniques. After rigorous unit and end-to-end testing, the application was successfully deployed in both EDs, which facilitated enrollment, randomization, model visualization, data capture, and reporting for trial purposes. Conclusion The successful implementation of a custom application for pneumonia prognosis and clinical trial support in two health systems on different EHRs demonstrates the importance of UCD, adherence to modern clinical data standards, and rigorous testing. Key lessons included the need for understanding users' real-world needs, regular knowledge management, application maintenance, and the recognition that FHIR applications require careful configuration for interoperability.

Only 15% of the nearly 30 million Americans with hearing loss use hearing aids, partly due to high cost, stigma, and limited access to required prior medical evaluations. Hearing impairment in adults can lead to social isolation and depression and is associated with an increased risk of falls as well as dementia. Given the persistent barriers to hearing aid use, the Food and Drug Administration (FDA) recently issued a final rule to allow over-the-counter hearing aids to be sold directly to adult consumers with perceived mild-to-moderate hearing loss at pharmacies, stores, and online retailers without seeing a physician or licensed hearing health care professional. We evaluated the safety and usability of an over-the-counter hearing aid prior to FDA approval and market release. We first conducted a formative usability test of the device and associated App with 5 intended users to identify outstanding safety and usability issues. Following design modifications, we performed a second round with 15 intended users of the device. Participants were asked to complete 2-5 tasks, as if they were using the hearing aid in real life. After each task, participants rated the task difficulty. At the end of each session, participants completed a 10-question knowledge assessment and the system usability scale (SUS) and then participated in debriefing interviews to gather qualitative feedback. All sessions were video recorded and analyzed to identify use errors and design improvement opportunities. We concurrently conducted a test with 21 non-intended users (i.e., users with contraindications to use) to ascertain if consumers could determine when they should not use the device, based on the packaging, instructions, and labeling. In all three usability tests, usability issues were identified. There were minimal safety-related issues with the device. Round 1 testing led to several design modifications which then increased task success in Round 2 testing. Participants had the most difficulty with the task of pairing the hearing aids to the cellphone. Participants also had difficulty distinguishing the right and left earbuds. Non-intended users did not always understand device contraindications (e.g., tinnitus, severe hearing loss). Overall, test findings informed eight actionable design modifications that improved device usability and safety. This study evaluated the usability and safety of an over-the-counter hearing aid for adults with mild-to-moderate hearing loss. Human factors engineering methods identified opportunities to improve the safety and usability of this direct-to-consumer medical device for individuals with perceived mild-moderate hearing loss.

Purpose of review This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists’ decision-making. Recent findings Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists’ decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists’ decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound. Summary To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists’ clinical decision-making in collaboration with artificial intelligence.

Cognitive task analysis (CTA) methods are traditionally used to conduct small-sample, in-depth studies. In this case study, CTA methods were adapted for a large multi-site study in which 102 anesthesiologists worked through four different high-fidelity simulated high-consequence incidents. Cognitive interviews were used to elicit decision processes following each simulated incident. In this paper, we highlight three practical challenges that arose: (1) standardizing the interview techniques for use across a large, distributed team of diverse backgrounds; (2) developing effective training; and (3) developing a strategy to analyze the resulting large amount of qualitative data. We reflect on how we addressed these challenges by increasing standardization, developing focused training, overcoming social norms that hindered interview effectiveness, and conducting a staged analysis. We share findings from a preliminary analysis that provides early validation of the strategy employed. Analysis of a subset of 64 interview transcripts using a decompositional analysis approach suggests that interviewers successfully elicited descriptions of decision processes that varied due to the different challenges presented by the four simulated incidents. A holistic analysis of the same 64 transcripts revealed individual differences in how anesthesiologists interpreted and managed the same case.

Editor—Hazardous attitudes contribute to degraded performance in aviation 1 US Department of Transportation-Federal Aviation AdministrationPilot's handbook of aeronautical knowledge, FAA-H-8083-25C. Ch 2: Aeronautical decision-making, p2-1 to 2-32. Oklahoma City, OK, USA. 2023https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/faa-h-8083-25c.pdfDate accessed: September 16, 2023 Google Scholar , 2 Nuñez B. López C. Velazquez J. Mora O.A. Román K. Hazardous attitudes in US part 121 airline accidents. 20th Int Symp Aviat Psychol. 2019; (Available from:): 37-42https://corescholar.libraries.wright.edu/isap_2019/7Date accessed: September 16, 2023 Google Scholar , 3 Hunter D.R. Measurement of hazardous attitudes among pilots. Int J Aviat Psychol. 2005; 15: 23-43 Crossref Scopus (61) Google Scholar , 4 Hunter D.R. Martinussen M. Wiggins M. O'Hare D. Situational and personal characteristics associated with adverse weather encounters by pilots. Accid Anal Prev. 2011; 43: 176-186 Crossref PubMed Scopus (28) Google Scholar and surgery. 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar Elevated hazardous attitudes scores appear to predispose to poor decision-making and 'at-risk' behaviours. 4 Hunter D.R. Martinussen M. Wiggins M. O'Hare D. Situational and personal characteristics associated with adverse weather encounters by pilots. Accid Anal Prev. 2011; 43: 176-186 Crossref PubMed Scopus (28) Google Scholar , 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar , 6 Saeed N.A. Blakaj A. Kelly J.R. et al. Hazardous attitudes: physician decision making in radiation oncology. Adv Rad Onc. 2022; 7101033 Google Scholar In post-accident analyses, 86% of fatal general aviation accidents involved at least one hazardous attitude. 7 Wetmore M. Lu C.T. The effects of hazardous attitudes on crew resource management skills. Int J Appl Aviat Stud. 2006; 6: 165-182 Google Scholar A key set of hazardous attitudes was identified by the United States Federal Aviation Administration (FAA): anti-authority, impulsivity, invulnerability, resignation, and macho (describing risk tolerance driven by ego and social concern). 1 US Department of Transportation-Federal Aviation AdministrationPilot's handbook of aeronautical knowledge, FAA-H-8083-25C. Ch 2: Aeronautical decision-making, p2-1 to 2-32. Oklahoma City, OK, USA. 2023https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/faa-h-8083-25c.pdfDate accessed: September 16, 2023 Google Scholar Their description of hazardous attitudes, pilot behaviours and mindsets, and potential adverse consequences is summarised in Supplementary Table 1. The domains self-confidence and worry were added to scale versions used in healthcare. 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar ,6 Saeed N.A. Blakaj A. Kelly J.R. et al. Hazardous attitudes: physician decision making in radiation oncology. Adv Rad Onc. 2022; 7101033 Google Scholar ,8 Bruinsma W.E. Becker S.J.E. Guitton T.G. Kadzielski J. Ring D. How prevalent are hazardous attitudes among orthopaedic surgeons?. Clin Orthop. 2015; 473: 1582-1589 Crossref PubMed Scopus (28) Google Scholar , 9 Kadzielski J. McCormick F. Zurakowski D. Herndon J.H. Patient safety climate among orthopaedic surgery residents. J Bone Jt Surg. 2011; 93: e62 Crossref PubMed Scopus (8) Google Scholar , 10 Meunier A. Posadzy K. Tinghög G. Aspenberg P. Risk preferences and attitudes to surgery in decision making: a survey of Swedish orthopedic surgeons. Acta Orthop. 2017; 88: 466-471 Crossref PubMed Scopus (12) Google Scholar

OBJECTIVE:Nonroutine events (NREs, i.e., deviations from optimal care) can identify care process deficiencies and safety risks. Nonroutine events reported by clinicians have been shown to identify systems failures, but this methodology fails to capture the patient perspective. The objective of this prospective observational study is to understand the incidence and nature of patient- and clinician-reported NREs in ambulatory surgery.METHODS:We interviewed patients about NREs that occurred during their perioperative care using a structured interview tool before discharge and in a 7-day follow-up call. Concurrently, we interviewed the clinicians caring for these patients immediately postoperatively to collect NREs. We trained 2 experienced clinicians and 2 patients to assess and code each reported NRE for type, theme, severity, and likelihood of reoccurrence (i.e., likelihood that the same event would occur for another patient).RESULTS:One hundred one of 145 ambulatory surgery cases (70%) contained at least one NRE. Overall, 214 NREs were reported-88 by patients and 126 by clinicians. Cases containing clinician-reported NREs were associated with increased patient body mass index ( P = 0.023) and lower postcase patient ratings of being treated with respect ( P = 0.032). Cases containing patient-reported NREs were associated with longer case duration ( P = 0.040), higher postcase clinician frustration ratings ( P < 0.001), higher ratings of patient stress ( P = 0.019), and lower patient ratings of their quality of life ( P = 0.010), of the quality of clinician teamwork ( P = 0.010), being treated with respect ( P = 0.003), and being listened to carefully ( P = 0.012). Trained patient raters evaluated NRE severity significantly higher than did clinician raters ( P < 0.001), while clinicians rated recurrence likelihood significantly higher than patients for both clinician ( P = 0.032) and patient-reported NREs ( P = 0.001).CONCLUSIONS:Both patients and clinicians readily report events during clinical care that they believe deviate from optimal care expectations. These 2 primary stakeholders in safe, high-quality surgical care have different experiences and perspectives regarding NREs. The combination of patient- and clinician-reported NREs seems to be a promising patient-centered method of identifying healthcare system deficiencies and opportunities for improvement.

Effective decision-making in crisis events is challenging due to time pressure, uncertainty, and dynamic decisional environments. We conducted a systematic literature review in PubMed and PsycINFO, identifying 32 empiric research papers that examine how trained professionals make naturalistic decisions under pressure. We used structured qualitative analysis methods to extract key themes. The studies explored different aspects of decision-making across multiple domains. The majority (19) focused on healthcare; military, fire and rescue, oil installation, and aviation domains were also represented. We found appreciable variability in research focus, methodology, and decision-making descriptions. We identified five main themes: (1) decision-making strategy, (2) time pressure, (3) stress, (4) uncertainty, and (5) errors. Recognition-primed decision-making (RPD) strategies were reported in all studies that analyzed this aspect. Analytical strategies were also prominent, appearing more frequently in contexts with less time pressure and explicit training to generate multiple explanations. Practitioner experience, time pressure, stress, and uncertainty were major influencing factors. Professionals must adapt to the time available, types of uncertainty, and individual skills when making decisions in high-risk situations. Improved understanding of these decisional factors can inform evidence-based enhancements to training, technology, and process design.

Patient handoffs involve the transition of information and responsibility for care from one health care provider to another. They occur frequently during a patient's perioperative care continuum, potentially introducing communication errors that could result in harmful, even fatal consequences. The perioperative environment poses distinct challenges to team communication and patient safety, which in turn leaves the surgical patient uniquely vulnerable to adverse events.The best way to achieve safe, coordinated handoffs throughout the perioperative continuum has yet to be established. However, a variety of theoretical principles, methods, and interventions have been used successfully in operative and nonoperative contexts among multiple disciplines. Informed by a literature review, the authors describe a conceptual framework for the development, implementation, and sustainment of a multimodal perioperative handoff improvement bundle. The conceptual framework presented here begins with overarching objectives for patient-centered handoff improvement efforts. The article outlines theoretical principles that could be used to guide and inform future multimodal interventions, as well as health care system factors to consider. Further, the authors propose employing data-driven quality improvement and research methodologies to conduct, measure, achieve, and sustain long-term success. Finally, this report describes essential evidence-based interventional components to employ.Future efforts to improve handoff safety in the perioperative environment will require a comprehensive evidence-based approach. The authors believe the conceptual framework presented here outlines essential components for success. It integrates proven theoretical frameworks, consideration of system factors, data-driven iterative methods, and synergistic patient-centered interventions.

Delivering high-quality, patient-centered cancer care remains a challenge. Both the National Academy of Medicine and the American Society of Clinical Oncology recommend shared decision making to improve patient-centered care, but widespread adoption of shared decision making into clinical care has been limited. Shared decision making is a process in which a patient and the patient's health-care professional weigh the risks and benefits of different options and come to a joint decision on the best course of action for that patient on the basis of their values, preferences, and goals for care. Patients who engage in shared decision making report higher quality of care, whereas patients who are less involved in these decisions have statistically significantly higher decisional regret and are less satisfied. Decision aids can improve shared decision making-for example, by eliciting patient values and preferences that can then be shared with clinicians and by providing patients with information that may influence their decisions. However, integrating decision aids into the workflows of routine care is challenging. In this commentary, we explore 3 workflow-related barriers to shared decision making: the who, when, and how of decision aid implementation in clinical practice. We introduce readers to human factors engineering and demonstrate its potential value to decision aid design through a case study of breast cancer surgical treatment decision making. By better employing the methods and principles of human factors engineering, we can improve decision aid integration, shared decision making, and ultimately patient-centered cancer outcomes.

BACKGROUND:Electronic health record (EHR) system transitions are challenging for healthcare organizations. High-volume, safety-critical tasks like barcode medication administration (BCMA) should be evaluated, yet standards for ensuring safety during transition have not been established. OBJECTIVE:Identify risks in common and problem-prone medication tasks to inform safe transition between BCMA systems and establish benchmarks for future system changes. DESIGN:Staff nurses completed simulation-based usability testing in the legacy system (R1) and new system pre- (R2) and post-go-live (R3). Tasks included (1) Hold/Administer, (2) IV Fluids, (3) PRN Pain, (4) Insulin, (5) Downtime/PRN, and (6) Messaging. Audiovisual recordings of task performance were systematically analyzed for time, navigation, and errors. The System Usability Scale measured perceived usability and satisfaction. Post-simulation interviews captured nurses' qualitative comments and perceptions of the systems. PARTICIPANTS:Fifteen staff nurses completed 2-3-h simulation sessions. Eleven completed both R1 and R2, and seven completed all three rounds. Clinical experience ranged from novice (< 1 year) to experienced (> 10 years). Practice settings included adult and pediatric patient populations in ICU, stepdown, and acute care departments. MAIN MEASURES:Task completion rates/times, safety and non-safety-related use errors (interaction difficulties), and user satisfaction. KEY RESULTS:Overall success rates remained relatively stable in all tasks except two: IV Fluids task success increased substantially (R1: 17%, R2: 54%, R3: 100%) and Downtime/PRN task success decreased (R1: 92%, R2: 64%, R3: 22%). Among the seven nurses who completed all rounds, overall safety-related errors decreased 53% from R1 to R3 and 50% from R2 to R3, and average task times for successfully completed tasks decreased 22% from R1 to R3 and 38% from R2 to R3. CONCLUSIONS:Usability testing is a reasonable approach to compare different BCMA tasks to anticipate transition problems and establish benchmarks with which to monitor and evaluate system changes going forward.