Formal and informal hospital emergency management practices: Managing for safety and performance amid crisis.

BACKGROUND: Although formal preparedness for unexpected crises has long been a concern of health care policy and delivery, many hospitals struggled to manage staff and equipment shortages, precarious finances, and supply chain disruptions among other difficulties during the COVID-19 pandemic. Our purpose was to analyze how hospitals used formal and informal emergency management practices to maintain safe and high-quality care while responding to crisis. METHODS: We conducted a qualitative study based on 26 interviews with hospital leaders and emergency managers from 12 U.S. hospitals purposively sampled to vary along geographic location, urban/rural delineation, size, resource availability, system membership, teaching status, and performance levels among other characteristics. RESULTS: In order to manage staff, space, supplies, and systems related challenges, hospitals engaged formal and informal practices around planning, teaming, and exchanging resources and information.Relying solely only on formal or informal practices proved inadequate, especially when prespecified plans, the incident command structure, and existing contracts and communication platforms failed to support resilient response. We identified emergent capabilities - imaginative planning, recombinant teaming, and transformational exchange - through which hospitals achieved harmonious interplay between the formal and informal practices of emergency management that supported safe care and resilience amid crisis. CONCLUSION: Managing emergent challenges for and amid crisis calls for health care delivery organizations to engage creative planning processes, enable motivated workers with diverse skill sets to team up, and establish rich inter- and intra-organizational partnerships that support vital exchange.

Implementation of a Continuous Patient Monitoring System in the Hospital Setting: A Qualitative Study.

BACKGROUND: Technology can improve care delivery, patient outcomes, and staff satisfaction, but integration into the clinical workflow remains challenging. To contribute to this knowledge area, this study examined the implementation continuum of a contact-free, continuous monitoring system (CFCM) in an inpatient setting. CFCM monitors vital signs and uses the information to alert clinicians of important changes, enabling early detection of patient deterioration. METHODS: Data were collected throughout the entire implementation continuum at a community teaching hospital. Throughout the study, 3 group and 24 individual interviews and five process observations were conducted. Postimplementation alarm response data were collected. Analysis was conducted using triangulation of information sources and two-coder consensus. RESULTS: Preimplementation perceived barriers were alarm fatigue, questions about accuracy and trust, impact on patient experience, and challenges to the status quo. Stakeholders identified the value of CFCM as preventing deterioration and benefitting patients who are not good candidates for telemetry. Educational materials addressed each barrier and emphasized the shared CFCM values. Mean alarm response times were below the desired target of two minutes. Postimplementation interview analysis themes revealed lessened concerns of alarm fatigue and improved trust in CFCM than anticipated. Postimplementation challenges included insufficient training for secondary users and impact on patient experience. CONCLUSION: In addition to understanding the preimplementation anticipated barriers to implementation and establishing shared value before implementation, future recommendations include studying strategies for optimal tailoring of education to each user group, identifying and reinforcing positive process changes after implementation, and including patient experience as the overarching element in frameworks for digital tool implementation.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

An analysis of the structure and content of dashboards used to monitor patient safety in the inpatient setting.

The objective of this study is to review and compare patient safety dashboards used by hospitals and identify similarities and differences in their design, format, and scope. We reviewed design features of electronic copies of patient safety dashboards from a representative sample of 10 hospitals. The results show great heterogeneity in the format, presentation, and scope of patient safety dashboards. Hospitals varied in their use of performance indicators (targets, trends, and benchmarks), style of color coding, and timeframe for the displayed metrics. The average number of metrics per dashboard display was 28, with a wide range from 7 to 84. Given the large variation in dashboard design, there is a need for future work to assess which approaches are associated with the best outcomes, and how specific elements contribute to usability, to help customize dashboards to meet the needs of different clinical, and operational stakeholders.

Moving Forward from COVID-19: Organizational Dimensions of Effective Hospital Emergency Management.

Federal investment in emergency preparedness has increased notably since the 9/11 attacks, yet it is unclear if and how US hospital readiness has changed in the 20 years since then. In particular, understanding effective aspects of hospital emergency management programs is essential to improve healthcare systems' readiness for future disasters. The authors of this article examined the state of US hospital emergency management, focusing on the following question: During the COVID-19 pandemic, what aspects of hospital emergency management, including program components and organizational characteristics, were most effective in supporting and improving emergency preparedness and response? We conducted semistructured interviews of emergency managers and leaders at 12 urban and rural hospitals across the country. Through qualitative analysis of content derived from examination of transcripts from our interviews, we identified 7 dimensions of effective healthcare emergency management: (1) identify capable leaders; (2) assure robust institutional support; (3) design effective, tiered communications systems; (4) embrace the hospital incident command system to delineate roles and responsibilities; (5) actively promote collaboration and team building; (6) appreciate the necessity of training and exercises; and (7) balance structure and flexibility. These dimensions represent the unique and critical intersection of organizational factors and emergency management program characteristics at the core of hospital emergency preparedness and response. Extending these findings, we provide several recommendations for hospitals to better develop and sustain what we call a response culture in supporting effective emergency management.

Assessing hospital electronic health record vendor performance across publicly reported quality measures.

OBJECTIVE: Little is known regarding variation among electronic health record (EHR) vendors in quality performance. This issue is compounded by selection effects in which high-quality hospitals coalesce to a subset of market leading vendors. We measured hospital performance, stratified by EHR vendor, across 4 quality metrics. MATERIALS AND METHODS: We used data on 1272 hospitals in 2018 across 4 quality measures: Leapfrog Computerized Provider Order Entry/EHR Evaluation, Centers for Medicare and Medicaid Services Hospital Compare Star Ratings, Hospital-Acquired Condition (HAC) score, and Hospital Readmission Reduction Program (HRRP) ratio. We examined score distributions and used multivariable regression to evaluate the association between vendor and score, recovering partial R2 to assess the proportion of quality variation explained by vendor. RESULTS: We found significant variation across and within EHR vendors. The largest vendor, vendor A, had the highest mean score on the Leapfrog Computerized Provider Order Entry/EHR Evaluation and HRRP ratio, vendor G had the highest Hospital Compare score, and vendor F had the highest HAC score. In adjusted models, no vendor was significantly associated with higher performance on more than 2 measures. EHR vendor explained between 1.2% (HAC) and 7.6 (HRRP) of the variation in quality performance. DISCUSSION: No EHR vendor was associated with higher quality across all measures, and the 2 largest vendors were not associated with the highest scores. Only a small fraction of quality variation was explained by EHR vendor choice. CONCLUSIONS: Top performance on quality measures can be achieved with any EHR vendor; much of quality performance is driven by the hospital and how it uses the EHR.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.

BACKGROUND: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. METHODS: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. RESULTS: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. CONCLUSIONS: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. TRIAL REGISTRATION: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

The potential of artificial intelligence to improve patient safety: a scoping review.

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

OpenSpace: Changing the Narrative of Public Dissemination in Astronomical Visualization from What to How.

We present the development of an open-source software called OpenSpace that bridges the gap between scientific discoveries and public dissemination and thus paves the way for the next generation of science communication and data exploration. We describe how the platform enables interactive presentations of dynamic and time-varying processes by domain experts to the general public. The concepts are demonstrated through four cases: Image acquisitions of the New Horizons and Rosetta spacecraft, the dissemination of space weather phenomena, and the display of high-resolution planetary images. Each case has been presented at public events with great success. These cases highlight the details of data acquisition, rather than presenting the final results, showing the audience the value of supporting the efforts of the scientific discovery.

Qualitative Study to Understand Ordering of CT Angiography to Diagnose Pulmonary Embolism in the Emergency Room Setting.

PURPOSE: To better understand the decision making behind the ordering of CT pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) in the emergency department. METHODS: We conducted semistructured interviews with our institution's emergency medicine (EM) providers and radiologists who read CTPAs performed in the emergency department. We employed the Theoretical Domains Framework-a formal, structured approach used to better understand the motivations and beliefs of physicians surrounding a complex medical decision making-to categorize the themes that arose from our interviews. RESULTS: EM providers were identified as the main drivers of CTPA ordering. Both EM and radiologist groups perceived the radiologist's role as more limited. Experience- and gestalt-based heuristics were the most important factors driving this decision and more important, in many cases, than established algorithms for CTPA ordering. There were contrasting views on the value of d-dimer in the suspected PE workup, with EM providers finding this test less useful than radiologists. EM provider and radiologist suggestions for improving the appropriateness of CTPA ordering consisted of making this process more arduous and incorporating d-dimer tests and prediction rules into a decision support tool. CONCLUSION: EM providers were the main drivers of CTPA ordering, and there was a marginalized role for the radiologist. Experience- and gestalt-based heuristics were the main influencers of CTPA ordering. Our findings suggest that a more nuanced intervention than simply including a d-dimer and a prediction score in each preimaging workup may be necessary to curb overordering of CTPA in patients suspected of PE.

New measure of the dissipation region in collisionless magnetic reconnection.

A new measure to identify a small-scale dissipation region in collisionless magnetic reconnection is proposed. The energy transfer from the electromagnetic field to plasmas in the electron's rest frame is formulated as a Lorentz-invariant scalar quantity. The measure is tested by two-dimensional particle-in-cell simulations in typical configurations: symmetric and asymmetric reconnection, with and without the guide field. The innermost region surrounding the reconnection site is accurately located in all cases. We further discuss implications for nonideal MHD dissipation.

Anomalous flow deflection at earth's low-Alfvén-Mach-Number bow shock.

Earth's magnetosphere is an obstacle to the supersonic solar wind and the bow shock is formed in the front side of it. In ordinary hydrodynamics, the flow decelerated at the shock is diverted around the obstacle symmetrically about the Earth-Sun line, which is indeed observed in the magnetosheath most of the time. Here we show a case under a very low-density solar wind in which duskward flow was observed in the dawnside magnetosheath. A Rankine-Hugoniot test shows that the magnetic effect is crucial for this "wrong flow" to appear. A full three-dimensional magnetohydrodynamics (MHD) simulation of the situation confirming this interpretation and earlier simulations is also performed. It is illustrated that in addition to the "wrong flow" feature, various peculiar characteristics appear in the global picture of the MHD flow interaction with the obstacle.