Satisfaction of Older Patients With Emergency Department Care: Psychometric Properties and Construct Validity of the Consumer Emergency Care Satisfaction Scale.

BACKGROUND: Patient satisfaction is an important indicator of quality of care, but its measurement remains challenging. The Consumer Emergency Care Satisfaction Scale (CECSS) was developed to measure patient satisfaction in the emergency department (ED). Although this is a valid and reliable tool, several aspects of the CECSS need to be improved, including the definition, dimension, and scoring of scales. PURPOSE: The purpose of this study was to examine the construct validity of the CECSS and make suggestions on how to improve the tool to measure overall satisfaction with ED care. METHODS: We administered 2 surveys to older adults who presented with a fall to the ED and used electronic health record data to examine construct validity of the CECSS and ceiling effects. RESULTS: Using several criteria, we improved construct validity of the CECSS, reduced ceiling effects, and standardized scoring. CONCLUSION: We addressed several methodological issues with the CECSS and provided recommendations for improvement.

Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer?

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.

Usability of a Human Factors-based Clinical Decision Support in the Emergency Department: Lessons Learned for Design and Implementation.

OBJECTIVE: To evaluate the usability and use of human factors (HF)-based clinical decision support (CDS) implemented in the emergency department (ED). BACKGROUND: Clinical decision support can improve patient safety; however, the acceptance and use of CDS has faced challenges. Following a human-centered design process, we designed a CDS to support pulmonary embolism (PE) diagnosis in the ED. We demonstrated high usability of the CDS during scenario-based usability testing. We implemented the HF-based CDS in one ED in December 2018. METHOD: We conducted a survey of ED physicians to evaluate the usability and use of the HF-based CDS. We distributed the survey via Qualtrics, a web-based survey platform. We compared the computer system usability questionnaire scores of the CDS between those collected in the usability testing to use of the CDS in the real environment. We asked physicians about their acceptance and use of the CDS, barriers to using the CDS, and areas for improvement. RESULTS: Forty-seven physicians (56%) completed the survey. Physicians agreed that diagnosing PE is a major problem and risk scores can support the PE diagnostic process. Usability of the CDS was reported as high, both in the experimental setting and the real clinical setting. However, use of the CDS was low. We identified several barriers to the CDS use in the clinical environment, in particular a lack of workflow integration. CONCLUSION: Design of CDS should be a continuous process and focus on the technology's usability in the context of the broad work system and clinician workflow.

Older Adult Patients and Care Partners as Knowledge Brokers in Fragmented Health Care.

OBJECTIVE: To describe older adult patients' and care partners' knowledge broker roles during emergency department (ED) visits. BACKGROUND: Older adult patients are vulnerable to communication and coordination challenges during an ED visit, which can be exacerbated by the time and resource constrained ED environment. Yet, as a constant throughout the patient journey, patients and care partners can act as an information conduit, or knowledge broker, between fragmented care systems to attain high-quality, safe care. METHODS: Participants included 14 older adult patients (≥ 65 years old) and their care partners (e.g., spouse, adult child) who presented to the ED after having experienced a fall. Human factors researchers collected observation data from patients, care partners and clinician interactions during the patient's ED visit. We used an inductive content analysis to determine the role of patients and care partners as knowledge brokers. RESULTS: We found that patients and care partners act as knowledge brokers by providing information about diagnostic testing, medications, the patient's health history, and care accommodations at the disposition location. Patients and care partners filled the role of knowledge broker proactively (i.e. offer information) and reactively (i.e. are asked to provide information by clinicians or staff), within-ED work system and across work systems (e.g., between the ED and hospital), and in anticipation of future knowledge brokering. CONCLUSION: Patients and care partners, acting as knowledge brokers, often fill gaps in communication and participate in care coordination that assists in mitigating health care fragmentation.

Role network measures to assess healthcare team adaptation to complex situations: the case of venous thromboembolism prophylaxis.

Hospitals are complex environments that rely on clinicians working together to provide appropriate care to patients. These clinical teams adapt their interactions to meet changing situational needs. Venous thromboembolism (VTE) prophylaxis is a complex process that occurs throughout a patient's hospitalisation, presenting five stages with different levels of complexity: admission, interruption, re-initiation, initiation, and transfer. The objective of our study is to understand how the VTE prophylaxis team adapts as the complexity in the process changes; we do this by using social network analysis (SNA) measures. We interviewed 45 clinicians representing 9 different cases, creating 43 role networks. The role networks were analysed using SNA measures to understand team changes between low and high complexity stages. When comparing low and high complexity stages, we found two team adaptation mechanisms: (1) relative increase in the number of people, team activities, and interactions within the team, or (2) relative increase in discussion among the team, reflected by an increase in reciprocity.   Practitioner Summary: The reason for this study was to quantify team adaptation to complexity in a process using social network analysis (SNA). The VTE prophylaxis team adapted to complexity by two different mechanisms, by increasing the roles, activities, and interactions among the team or by increasing the two-way communication and discussion throughout the team. We demonstrated the ability for SNA to identify adaptation within a team.

Workflow analysis of breast cancer treatment decision-making: challenges and opportunities for informatics to support patient-centered cancer care.

Objective: Decision support can improve shared decision-making for breast cancer treatment, but workflow barriers have hindered widespread use of these tools. The goal of this study was to understand the workflow among breast cancer teams of clinicians, patients, and their family caregivers when making treatment decisions and identify design guidelines for informatics tools to better support treatment decision-making. Materials and Methods: We conducted observations of breast cancer clinicians during routine clinical care from February to August 2022. Guided by the work system model, a human factors engineering model that describes the elements of work, we recorded all aspects of clinician workflow using a tablet and smart pencil. Observation notes were transcribed and uploaded into Dedoose. Two researchers inductively coded the observations. We identified themes relevant to the design of decision support that we classified into the 4 components of workflow (ie, flow of information, tasks, tools and technologies, and people). Results: We conducted 20 observations of breast cancer clinicians (total: 79 hours). We identified 10 themes related to workflow that present challenges and opportunities for decision support design. We identified approximately 48 different decisions discussed during breast cancer visits. These decisions were often interdependent and involved collaboration across the large cancer treatment team. Numerous patient-specific factors (eg, work, hobbies, family situation) were discussed when making treatment decisions as well as complex risk and clinical information. Patients were frequently asked to remember and relay information across the large cancer team. Discussion and Conclusion: Based on these findings, we proposed design guidelines for informatics tools to support the complex workflows involved in breast cancer care. These guidelines should inform the design of informatics solutions to better support breast cancer decision-making and improve patient-centered cancer care.

The decision aid is the easy part: workflow challenges of shared decision making in cancer care.

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.

Post-implementation usability evaluation of a human factors-based clinical decision support for pulmonary embolism (PE) diagnosis (Dx): PE Dx Study Part 1.

While there is promise for health IT, such as Clinical Decision Support (CDS), to improve patient safety and clinician efficiency, poor usability has hindered widespread use of these tools. Human Factors (HF) principles and methods remain the gold standard for health IT design; however, there is limited information on how HF methods and principles influence CDS usability "in the wild". In this study, we explore the usability of an HF-based CDS used in the clinical environment; the CDS was designed according to a human-centered design process, which is described in Carayon et al. (2020). In this study, we interviewed 12 emergency medicine physicians, identifying 294 excerpts of barriers and facilitators of the CDS. Sixty-eight percent of excerpts related to the HF principles applied in the human-centered design of the CDS. The remaining 32% of excerpts related to 18 inductively-created categories, which highlight gaps in the CDS design process. Several barriers were related to the physical environment and organization work system elements as well as physicians' broader workflow in the emergency department (e.g., teamwork). This study expands our understanding of the usability outcomes of HF-based CDS "in the wild". We demonstrate the value of HF principles in the usability of CDS and identify areas for improvement to future human-centered design of CDS. The relationship between these usability outcomes and the HCD process is explored in an accompanying Part 2 manuscript.

Retrospective analysis of the human-centered design process used to develop a clinical decision support in the emergency department: PE Dx Study Part 2.

With the growing implementation and use of health IT such as Clinical Decision Support (CDS), there is increasing attention on the potential negative impact of these technologies on patients (e.g., medication errors) and clinicians (e.g., increased workload, decreased job satisfaction, burnout). Human-Centered Design (HCD) and Human Factors (HF) principles are recommended to improve the usability of health IT and reduce its negative impact on patients and clinicians; however, challenges persist. The objective of this study is to understand how an HCD process influences the usability of health IT. We conducted a systematic retrospective analysis of the HCD process used in the design of a CDS for pulmonary embolism diagnosis in the emergency department (ED). Guided by the usability outcomes (e.g., barriers and facilitators) of the CDS use "in the wild" (see Part 1 of this research in the accompanying manuscript), we performed deductive content analysis of 17 documents (e.g., design session transcripts) produced during the HCD process. We describe if and how the design team considered the barriers and facilitators during the HCD process. We identified 7 design outcomes of the HCD process, for instance designing a workaround and making a design change to the CDS. We identify gaps in the current HCD process and demonstrate the need for a continuous health IT design process.

Understanding Patient and Clinician Reported Nonroutine Events in Ambulatory Surgery.

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.

Adapting Cognitive Task Analysis Methods for Use in a Large Sample Simulation Study of High-Risk Healthcare Events.

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.

Decision-Making During High-Risk Events: A Systematic Literature Review.

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.

Evaluating the Usability of an Emergency Department After Visit Summary: Staged Heuristic Evaluation.

BACKGROUND: Heuristic evaluations, while commonly used, may inadequately capture the severity of identified usability issues. In the domain of health care, usability issues can pose different levels of risk to patients. Incorporating diverse expertise (eg, clinical and patient) in the heuristic evaluation process can help assess and address potential negative impacts on patient safety that may otherwise go unnoticed. One document that should be highly usable for patients-with the potential to prevent adverse outcomes-is the after visit summary (AVS). The AVS is the document given to a patient upon discharge from the emergency department (ED), which contains instructions on how to manage symptoms, medications, and follow-up care. OBJECTIVE: This study aims to assess a multistage method for integrating diverse expertise (ie, clinical, an older adult care partner, and health IT) with human factors engineering (HFE) expertise in the usability evaluation of the patient-facing ED AVS. METHODS: We conducted a three-staged heuristic evaluation of an ED AVS using heuristics developed for use in evaluating patient-facing documentation. In stage 1, HFE experts reviewed the AVS to identify usability issues. In stage 2, 6 experts of varying expertise (ie, emergency medicine physicians, ED nurses, geriatricians, transitional care nurses, and an older adult care partner) rated each previously identified usability issue on its potential impact on patient comprehension and patient safety. Finally, in stage 3, an IT expert reviewed each usability issue to identify the likelihood of successfully addressing the issue. RESULTS: In stage 1, we identified 60 usability issues that violated a total of 108 heuristics. In stage 2, 18 additional usability issues that violated 27 heuristics were identified by the study experts. Impact ratings ranged from all experts rating the issue as "no impact" to 5 out of 6 experts rating the issue as having a "large negative impact." On average, the older adult care partner representative rated usability issues as being more significant more of the time. In stage 3, 31 usability issues were rated by an IT professional as "impossible to address," 21 as "maybe," and 24 as "can be addressed." CONCLUSIONS: Integrating diverse expertise when evaluating usability is important when patient safety is at stake. The non-HFE experts, included in stage 2 of our evaluation, identified 23% (18/78) of all the usability issues and, depending on their expertise, rated those issues as having differing impacts on patient comprehension and safety. Our findings suggest that, to conduct a comprehensive heuristic evaluation, expertise from all the contexts in which the AVS is used must be considered. Combining those findings with ratings from an IT expert, usability issues can be strategically addressed through redesign. Thus, a 3-staged heuristic evaluation method offers a framework for integrating context-specific expertise efficiently, while providing practical insights to guide human-centered design.

A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery.

In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g. diagnostic accuracy) as well as reduce the burden on clinicians (e.g. documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.

Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.

Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls. We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing. Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.

Moving toward a sociotechnical systems approach to continuous health information technology design: the path forward for improving electronic health record usability and reducing clinician burnout.

Based on our analysis of descriptions provided by four EHR vendors on their EHR usability efforts, we provide three recommendations aimed at improving the usability of health information technology and reducing clinician burnout. First, EHR vendors need to dedicate increased attention to the design of the entire sociotechnical (work) system, including the EHR technology and its usability as well as the interactions of the technology with other system elements. Second, EHR vendors need to deepen and broaden their understanding of the work of clinicians and care teams by using diverse and mixed method. Third, in collaboration with health care organizations, EHR vendors should engage in cycles of continuous design and learning in order to improve the usability of health IT.

Workflow integration analysis of a human factors-based clinical decision support in the emergency department.

Numerous challenges with the implementation, acceptance, and use of health IT are related to poor usability and a lack of integration of the technologies into clinical workflow, and have, therefore, limited the potential of these technologies to improve patient safety. We propose a definition and conceptual model of health IT workflow integration. Using interviews of 12 emergency department (ED) physicians, we identify 134 excerpts of barriers and facilitators to workflow integration of a human factors (HF)-based clinical decision support (CDS) implemented in the ED. Using data on these 134 barriers and facilitators, we distinguish 25 components of workflow integration of the CDS, which are described according to four dimensions of workflow integration: time, flow, scope of patient journey, and level. The proposed definition and conceptual model of workflow integration can be used to inform health IT design; this is the purpose of the proposed checklist that can help to ensure consideration of workflow integration during the development of health IT.

Usability barriers and facilitators of a human factors engineering-based clinical decision support technology for diagnosing pulmonary embolism.

BACKGROUND: Health IT, such as clinical decision support (CDS), has the potential to improve patient safety. However, poor usability of health IT continues to be a major concern. Human factors engineering (HFE) approaches are recommended to improve the usability of health IT. Limited evidence exists on the actual impact of HFE methods and principles on the usability of health IT. OBJECTIVE: To identify and describe the usability barriers and facilitators of an HFE-based CDS prior to implementation in the emergency department (ED). METHODS: We conducted debrief interviews with 32 emergency medicine physicians as a part of a scenario-based simulation study evaluating the usability of the HFE-based CDS. We performed a deductive content analysis of the interviews using the usability criteria of Scapin and Bastien as a framework. RESULTS: We identified 271 occurrences of usability barriers (94) and facilitators (177) of the HFE-based CDS. For instance, we found a facilitator relating to the usability criteria prompting as the PE Dx helps the physician order diagnostic tests following the risk assessment. We found the most facilitators relating to the criteria, minimal actions, e.g. as the PE Dx automatically populates vitals signs (e.g., heart rate) from the chart into the CDS. The majority of the usability barriers related to the usability criteria, compatibility (i.e., workflow integration), which was not explicitly considered in the HFE design of the CDS. For example, the CDS did not support resident and attending physician teamwork in the PE diagnostic process. CONCLUSION: The systematic use of HFE principles in the design of CDS improves the usability of these technologies. In order to further reduce usability barriers, workflow integration should be explicitly considered in the design of health IT.

Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.

OBJECTIVE: In this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway. MATERIALS AND METHODS: We conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS). RESULTS: Emergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx. CONCLUSIONS: This simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety.

Physician Perceptions of Disposition Decision-making for Older Adults in the Emergency Department: A Preliminary Analysis.

Disposition decision-making in the emergency department (ED) is critical to patient safety and quality of care. Disposition decision-making has particularly important implications for older adults who comprise a significant portion of ED visits annually and are vulnerable to suboptimal outcomes throughout ED care transitions. We conducted a secondary inductive content analysis of interviews with ED physicians (N= 11) to explore their perceptions of who they involve in disposition decision-making and what information they use to make disposition decisions for older adults. ED physicians cited 7 roles (5 types of clinicians, patients and families) and 11 information types, both clinical (e.g. test/lab results) and non-clinical (e.g. family's preference). Our preliminary findings represent a key first step toward the development of interventions that promote patient safety and quality of care for older adults in the ED by supporting the cognitive and communicative aspects of disposition decision-making.