Noa Segall

Can We Make Postoperative Patient Handovers Safer? A Systematic Review of the Literature

Postoperative patient handovers are fraught with technical and communication errors and may negatively impact patient safety. We systematically reviewed the literature on handover of care from the operating room to postanesthesia or intensive care units and summarized process and communication recommendations based on these findings. From >500 papers, we identified 31 dealing with postoperative handovers. Twenty-four included recommendations for structuring the handover process or information transfer. Several recommendations were broadly supported, including (1) standardize processes (e.g., through the use of checklists and protocols); (2) complete urgent clinical tasks before the information transfer; (3) allow only patient-specific discussions during verbal handovers; (4) require that all relevant team members be present; and (5) provide training in team skills and communication. Only 4 of the studies developed an intervention and formally assessed its impact on different process measures. All 4 interventions improved metrics of effectiveness, efficiency, and perceived teamwork. Most of the papers were cross-sectional studies that identified barriers to safe, effective postoperative handovers including the incomplete transfer of information and other communication issues, inconsistent or incomplete teams, absent or inefficient execution of clinical tasks, and poor standardization. An association between poor-quality handovers and adverse events was also demonstrated. More innovative research is needed to define optimal patient handovers and to determine the effect of handover quality on patient outcomes.

Effects of physical workload on cognitive task performance and situation awareness

Sixteen participants performed a military operations simulation directing loading of helicopters to weight capacity within an allotted timeframe and subject to a set of decision rules. The participants stood, walked or jogged on a treadmill while performing the simulated cognitive task. Task performance was measured in terms of helicopter loading rate and accuracy. Situation awareness (SA) was measured using a simulation freeze technique and SA queries. Subjective workload was measured using the NASA-TLX. Results indicated a general trend of decreasing SA with increasing physical workload for perceptual knowledge, comprehension and overall SA. Results also revealed higher subjective workload during jogging than during the walking and standing conditions. However, the physical workload manipulations did not appear to affect cognitive task performance. This study has practical implications for defining physical and cognitive workloads in specific dynamic, complex work environments to support operator SA and performance.

Using multiple cognitive task analysis methods for supervisory control interface design in high-throughput biological screening processes

Cognitive task analysis (CTA) approaches are currently needed in many domains to provide explicit guidance on redesigning existing systems. This study used goal-directed task analysis (GDTA) along with abstraction hierarchy (AH) modeling to characterize the knowledge structure of biopharmacologists in planning, executing and analyzing the results of high-throughput organic compound screening operations, as well as the lab automation and equipment used in these operations. It was hypothesized that combining the results of the GDTA and AH models would provide a better understanding of complex system operator needs and how they may be addressed by existing technologies, as well as facilitate identification of automation and system interface design limitations. We used comparisons of the GDTA and AH models along with taxonomies of usability heuristics and types of automation in order to formulate interface design and automation functionality recommendations for existing software applications used in biological screening experiments. The proposed methodology yielded useful recommendations for improving custom supervisory control applications that led to prototypes of interface redesigns. The approach was validated through an expert usability evaluation of the redesigns and was shown to be applicable to the life sciences domain.

Handovers from the OR to the ICU.

The case was long and difficult—a redo sternotomy and coronary artery bypass grafting procedure on a fragile 82-year-old patient. While you are pushing the bed down the hallway, you move cautiously toward the intensive care unit (ICU) because the patient is hemodynamically unstable and receiving high doses of inotropes and intra-aortic balloon pump support. Upon rounding a corner, equipment temporarily being stored in the hallway forces you to swerve forcefully disconnecting the helium tubing from the balloon pump. Alarms chiming, you quickly make it to your assigned ICU bed space to find the receiving ICU nurse absent. She left the bedside to look for a missing pressure cable. You handover the bag-mask system to the respiratory therapist, and she asks whether you had any problems with intubation or ventilation. You want to tell her that intubation was difficult, but you notice that the arterial pressure is very low. “Please don’t disconnect the a-line yet,” you ask the

Workload State Classification With Automation During Simulated Air Traffic Control

Real-time operator workload assessment and state classification may be useful for decisions about when and how to dynamically apply automation to information processing functions in aviation systems. This research examined multiple cognitive workload measures, including secondary task performance and physiological (cardiac) measures, as inputs to a neural network for operator functional state classification during a simulated air traffic control (ATC) task. Twenty-five participants performed a low-fidelity simulation under manual control or 1 of 4 different forms of automation. Traffic volume was either low (3 aircraft) or high (7 aircraft). Participants also performed a secondary (gauge) monitoring task. Results demonstrated significant effects of traffic volume (workload) on aircraft clearances (p < .01) and trajectory conflicts (p < .01), secondary task performance (p < .01), and subjective ratings of task workload (p < .01). The form of ATC automation affected the number of aircraft collisions (p < .05), secondary task performance (p < .01), and heart rate (HR; p < .01). However, heart rate and heart rate variability measures were not sensitive to the traffic manipulation. Neural network models of controller workload (defined in terms of traffic volume) were developed using the secondary task performance and simple heart rate measure as inputs. The best workload classification accuracy using a genetic algorithm (across all forms of ATC automation) was 64%, comparable to prior work. Additional neural network models of workload for each mode of ATC automation revealed substantial variability in predictive accuracy, based on the characteristics of the automation. Secondary task performance was a highly sensitive indicator of ATC workload, whereas the heart rate measure appeared to operate as a more global indicator of workload. A limited range of cardiac response might be sufficient for the demands of the brain in ATC. The results have applicability to design of future adaptive systems integrating neural-network-based workload state classifiers for multiple forms of automation.

In situ simulated cardiac arrest exercises to detect system vulnerabilities.

Introduction Sudden cardiac arrest is the leading cause of death in the United States. Despite new therapies, progress in this area has been slow, and outcomes remain poor even in the hospital setting, where providers, drugs, and devices are readily available. This is partly attributed to the quality of resuscitation, which is an important determinant of survival for patients who experience cardiac arrest. Systems problems, such as deficiencies in the physical space or equipment design, hospital-level policies, work culture, and poor leadership and teamwork, are now known to contribute significantly to the quality of resuscitation provided. Methods We describe an in situ simulation-based quality improvement program that was designed to continuously monitor the cardiac arrest response process for hazards and defects and to detect opportunities for system optimization. Results A total of 72 simulated unannounced cardiac arrest exercises were conducted between October 2010 and September 2013 at various locations throughout our medical center and at different times of the day. We detected several environmental, human-machine interface, culture, and policy hazards and defects. We used the Systems Engineering Initiative for Patient Safety (SEIPS) model to understand the structure, processes, and outcomes related to the hospital’s emergency response system. Multidisciplinary solutions were crafted for each of the hazards detected, and the simulation program was used to iteratively test the redesigned processes before implementation in real clinical settings. Conclusions We describe an ongoing program that uses in situ simulation to identify and mitigate latent hazards and defects in the hospital emergency response system. The SEIPS model provides a framework for describing and analyzing the structure, processes, and outcomes related to these events.

Coding and Visualizing Eye Tracking Data in Simulated Anesthesia Care

Eye tracking can be a valuable tool for collecting data about perception and attention in task performance, but its use in human factors research has been limited. This may be due to the fact that the coding and visualization of eye tracking data can be difficult and time-consuming. In this paper we introduce a video-coding application for coding and analyzing eye tracking data. We discuss various methods for visualizing these data for the purposes of identifying patterns or trends that can then be more formally analyzed. We also present several visualization examples from the simulated anesthesia care environment.

User, robot and automation evaluations in high-throughput biological screening processes

This paper introduces high-throughput screening of biological samples in life sciences, as a domain for analysis of human-robot interaction (HRI) and development of usable human interface design principles. High-throughput screening (HTS) processes involve use of robotics and highly automated analytical measurement devices to transport and chemically evaluate biological compounds for potential use as drug derivatives. Humans act as supervisory controllers in HTS processes by performing test planning and device programming prior to experiments, systems monitoring, and real-time process intervention and error correction to maintain experiment safety and output. Process errors are infrequent but can be costly. Two forms of cognitive task analysis were applied to a highly automated HTS process to address different classes of errors, including goal-directed task analysis to describe critical operator decisions and information requirements and abstraction hierarchy modeling to represent HTS process devices and automation integrated in screening lines. The outcomes of the analyses were used as bases for generating supervisory control interface design recommendations to improve existing system usefulness and usability.

Standardized assessment for evaluation of team skills: validity and feasibility.

Introduction The authors developed a Standardized Assessment for Evaluation of Team Skills (SAFE-TeamS) in which actors portray health care team members in simulated challenging teamwork scenarios. Participants are scored on scenario-specific ideal behaviors associated with assistance, conflict resolution, communication, assertion, and situation assessment. This research sought to provide evidence of the validity and feasibility of SAFE-TeamS as a tool to support the advancement of science related to team skills training. Methods Thirty-eight medical and nursing students were assessed using SAFE-TeamS before and after team skills training. The SAFE-TeamS pretraining and posttraining scores were compared, and participants were surveyed. Generalizability analysis was used to estimate the variance in scores associated with the following: examinee, scenario, rater, pretraining/posttraining, examinee type, rater type (actor-live vs. external rater–videotape), actor team, and scenario order. Results The SAFE-TeamS scores reflected improvement after training and were sensitive to individual differences. Score variance due to rater was low. Variance due to scenario was moderate. Estimates of relative reliability for 2 raters and 8 scenarios ranged from 0.6 to 0.7. With fixed scenarios and raters, 2 raters and 2 scenarios, reliability is greater than 0.8. Raters believed SAFE-TeamS assessed relevant team skills. Examinees’ responses were mixed. Conclusions The SAFE-TeamS was sensitive to individual differences and team skill training, providing evidence for validity. It is not clear whether different scenarios measure different skills and whether the scenarios cover the necessary breadth of skills. Use of multiple scenarios will support assessment across a broader range of skills. Future research is required to determine whether assessments using SAFE-TeamS will translate to performance in clinical practice.

Patient Load Effects on Response Time to Critical Arrhythmias in Cardiac Telemetry: A Randomized Trial

Objectives: Remotely monitored patients may be at risk for a delayed response to critical arrhythmias if the telemetry watchers who monitor them are subject to an excessive patient load. There are no guidelines or studies regarding the appropriate number of patients that a single watcher may safely and effectively monitor. Our objective was to determine the impact of increasing the number of patients monitored on response time to simulated cardiac arrest. Design: Randomized trial. Setting: Laboratory-based experiment. Subjects: Forty-two remote telemetry technicians and nurses from cardiac units. Interventions: Number of patients monitored in a simulation of cardiac telemetry monitoring work. Measurements and Main Results: We carried out a study to compare response times to ventricular fibrillation across five patient loads: 16, 24, 32, 40, and 48 patients. The simulation replicated the work of telemetry watchers using a combination of real recorded patient electrocardiogram signals and a simulated patient experiencing ventricular fibrillation. Study participants were assigned to one of the five patient loads and completed a 4-hour monitoring session, during which they performed tasks—including event documentation and phone calls to report events—similar to real monitoring work. When the simulated patient sustained ventricular fibrillation, the time required to report this arrhythmia was recorded. As patient loads increased, there was a statistically significant increase in response times to the ventricular fibrillation. In addition, frequency of failure to meet a response time goal of less than 20 seconds was significantly higher in the 48-patient condition than in all other conditions. Task performance decreased as patient load increased. Conclusions: As participants monitored more patients in a laboratory setting, their performance with respect to recognizing critical and noncritical events declined. This study has implications for the design of remote telemetry work and other patient monitoring tasks in critical and intermediate care units.