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.

Modeling Task Scheduling Decisions of Emergency Department Physicians.

OBJECTIVE: This study evaluated task-scheduling decisions in the context of emergency departments by comparing patterns of emergency physicians' task-scheduling models across levels of experience. BACKGROUND: Task attributes (priority, difficulty, salience, and engagement) influence task-scheduling decisions. However, it is unclear how attributes interact to affect decisions, especially in complex contexts. An existing model of task scheduling, strategic task overload management-no priority (STOM-NP), found that an equal weighting of attributes can predict task-scheduling behavior. Alternatively, mathematical modeling estimated that priority alone could make similar predictions as STOM-NP in a parsimonious manner. Experience level may also influence scheduling decisions. METHOD: An experimental design methodology shortened a judgment analysis approach to compare a priori task-scheduling decision strategies. Emergency physicians with two levels of experience rank-ordered 10 sets of 3 tasks varying on 4 task attributes in this complex environment. RESULTS: Bayesian statistics were used to identify best-fit decision strategies. STOM-NP and priority-only provided the best model fits. STOM-NP fit the lower-experienced physicians best, whereas priority-only-using only one cue-fit the higher-experienced physicians best. CONCLUSION: Models of decision strategies for task-scheduling decisions were extended to complex environments. Experts' level of experience influenced task-scheduling decisions, where the scheduling decisions of more-experienced experts was consistent with a more frugal decision process. Findings have implications for training and evaluation. APPLICATION: We assessed models of cues that influence task-scheduling decisions, including a parsimonious model for task priority only. We provided a sample approach for shortening methods for understanding decisions.

Understanding Emergency Care Delivery Through Computer Simulation Modeling.

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges.

Understanding older adults' perceptions of and attitudes towards exergames.

PURPOSE: Maintaining physical activity is a key component of successful aging and has benefits for both physical and cognitive functioning in the older adult population. One promising method for engaging in physical activity is through exergames, which are video games designed to promote exercise. Exergames have the potential to be used by a wide range of people, including older adults, in a variety of settings, such as at home, in community living environments, or senior centers. However, exergames have not been designed for older adults (e.g., with respect to their attitudes, needs). Thus, older adults may not adopt these systems if they perceive them as not useful or relevant to them. METHOD: Twenty older adults (aged 60-79) interacted with two exergames, and were then interviewed about their perceptions of the system's ease of use and usefulness, as well as their general attitudes towards the system. RESULTS: Participants identified the potential for exergames' usefulness for various goals, such as to increase their physical activity. However, they also reported negative attitudes concerning the system, including perceiving barriers to system use. Overall, participants said they would use the system in the future and recommend it to other people at their age for improving health, despite these use challenges. CONCLUSION: The older adults were open to adopting exergames, which could provide opportunities to increase physical activity. Given the participants' overall positive perceptions of the usefulness of exergames, designers must address the perceived challenges of using these systems. Understanding barriers and facilitators for older adults' use of exergames can guide design, training, and adoption of these systems.

The Effect of Incorrect Reliability Information on Expectations, Perceptions, and Use of Automation.

OBJECTIVE: We examined how providing artificially high or low statements about automation reliability affected expectations, perceptions, and use of automation over time. BACKGROUND: One common method of introducing automation is providing explicit statements about the automation's capabilities. Research is needed to understand how expectations from such introductions affect perceptions and use of automation. METHOD: Explicit-statement introductions were manipulated to set higher-than (90%), same-as (75%), or lower-than (60%) levels of expectations in a dual-task scenario with 75% reliable automation. Two experiments were conducted to assess expectations, perceptions, compliance, reliance, and task performance over (a) 2 days and (b) 4 days. RESULTS: The baseline assessments showed initial expectations of automation reliability matched introduced levels of expectation. For the duration of each experiment, the lower-than groups' perceptions were lower than the actual automation reliability. However, the higher-than groups' perceptions were no different from actual automation reliability after Day 1 in either study. There were few differences between groups for automation use, which generally stayed the same or increased with experience using the system. CONCLUSION: Introductory statements describing artificially low automation reliability have a long-lasting impact on perceptions about automation performance. Statements including incorrect automation reliability do not appear to affect use of automation. APPLICATION: Introductions should be designed according to desired outcomes for expectations, perceptions, and use of the automation. Low expectations have long-lasting effects.

How Do Older Adults Manage Osteoarthritis Pain? The Need for a Person-Centered Disease Model.

In the United States, chronic pain affects at least 116 million Americans, differentially impacting older adults. One of the leading causes of pain for older adults is osteoarthritis. This disease affects approximately 14% of the United States population and can cause disability and mobility problems, in addition to having a high cost for the healthcare system. The methods individuals use to manage their pain are contingent upon their model of the disease (e.g., their beliefs about osteoarthritis pain management). The purpose of the present investigation was to: 1) understand what variables older adults with osteoarthritis believe impact pain, and 2) understand current approaches for self-management of osteoarthritis pain. We conducted structured interviews with eight older adults who have osteoarthritis. The interviews revealed current approaches in pain management, as well as gaps in knowledge. We propose an expansion of the idea of a general disease model for pain management that is patient-centered, allowing for personal customization of factors for reducing pain and increasing successful pain-management.