Objective Surveillance algorithms that predict patient decompensation are increasingly integrated with clinical workflows to help identify patients at risk of in-hospital deterioration. This scoping review aimed to identify the design features of the information displays, the types of algorithm that drive the display, and the effect of these displays on process and patient outcomes.Materials and methods The scoping review followed Arksey and O'Malley's framework. Five databases were searched with dates between January 1, 2009 and January 26, 2022. Inclusion criteria were: participants-clinicians in inpatient settings; concepts-intervention as deterioration information displays that leveraged automated AI algorithms; comparison as usual care or alternative displays; outcomes as clinical, workflow process, and usability outcomes; and context as simulated or real-world in-hospital settings in any country. Screening, full-text review, and data extraction were reviewed independently by 2 researchers in each step. Display categories were identified inductively through consensus.Results Of 14 575 articles, 64 were included in the review, describing 61 unique displays. Forty-one displays were designed for specific deteriorations (eg, sepsis), 24 provided simple alerts (ie, text-based prompts without relevant patient data), 48 leveraged well-accepted score-based algorithms, and 47 included nurses as the target users. Only 1 out of the 10 randomized controlled trials reported a significant effect on the primary outcome.Conclusions Despite significant advancements in surveillance algorithms, most information displays continue to leverage well-understood, well-accepted score-based algorithms. Users' trust, algorithmic transparency, and workflow integration are significant hurdles to adopting new algorithms into effective decision support tools.

Objective Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases.Material and Methods We developed cases mimicking sepsis situations. De-identified data, synthesized physician notes, and EWS representing deterioration risk were displayed in a simulated EHR for analysis. Twelve clinicians participated in semi-structured interviews to ascertain perspectives across four domains: (1) Familiarity with and understanding of artificial intelligence (AI), prediction models and risk scores; (2) Clinical reasoning processes; (3) Impression and response to EWS; and (4) Interface design. Transcripts were coded and analyzed using content and thematic analysis.Results Analysis revealed clinicians have experience but limited AI and prediction/risk modeling understanding. Case assessments were primarily based on clinical data. EWS went unmentioned during initial case analysis; although when prompted to comment on it, they discussed it in subsequent cases. Clinicians were unsure how to interpret or apply the EWS, and desired evidence on its derivation and validation. Design recommendations centered around EWS display in multi-patient lists for triage, and EWS trends within the patient record. Themes included a "Trust but Verify" approach to AI and early warning information, dichotomy that EWS is helpful for triage yet has disproportional signal-to-high noise ratio, and action driven by clinical judgment, not the EWS.Conclusions Clinicians were unsure of how to apply EWS, acted on clinical data, desired score composition and validation information, and felt EWS was most useful when embedded in multi-patient views. Systems providing interactive visualization may facilitate EWS transparency and increase confidence in AI-generated information.

Background Artificial intelligence (AI) is a branch of computer science that uses advanced computational methods, such as machine learning (ML), to calculate and predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving health care, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in health care management. Objective We aimed to support and ensure safe use of AI/ML technologies in health care; thus, the team worked to better understand (1) patient information and training needs, (2) the factors that influence patients’ perceived value and trust in AI/ML health care applications, and (3) how best to support safe and appropriate use of AI/ML-enabled devices and applications among people living with diabetes. Methods To understand general patient perspectives and information needs related to the use of AI/ML in health care, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=41) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and subthemes. Inductive approaches were used to link data to key concepts, including preferred patient-provider interactions and patient perceptions of trust, accuracy, value, assurances, and information transparency. Results Key summary themes and recommendations focused on (1) patient preferences for AI/ML-enabled device and application information, (2) patient and provider AI/ML-related device and application training needs, (3) factors contributing to patient and provider trust in AI/ML-enabled devices and applications, and (4) AI/ML-related device and application functionality and safety considerations. A number of participants (patients and providers) made recommendations to improve device functionality to guide information and labeling mandates (eg, link to online video resources and provide access to 24/7 live in-person or virtual emergency support). Other patient recommendations included (1) providing access to practice devices, (2) providing connections to local supports and reputable community resources, and (3) simplifying the display and alert limits. Conclusions Recommendations from both patients and providers could be used by federal oversight agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.

Introduction Early identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians' decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools. Methods and analysis We will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool's user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats. Ethics and dissemination Ethics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Vaccination remains one of the most effective ways to limit spread of disease. Waning public confidence in COVID-19 vaccines has resulted in reduced vaccination rates. In fact, despite vaccine availability, many individuals choose to delay COVID-19 vaccination resulting in suboptimal herd immunity and increased viral mutations. A number of qualitative and quantitative studies have been conducted to identify, understand, and address modifiable barriers and factors contributing to COVID-19 vaccine hesitancy among individuals with access to vaccine. Vaccine confidence may be improved through targeted patient–provider discussion. More patients are turning to pharmacists to receive their vaccinations across the lifespan. The primary goal of this commentary is to share evidence-based, patient talking points, tailored by practicing pharmacists, to better communicate and address factors contributing to vaccine hesitancy and reduced vaccine confidence.

Vaccination remains one of the most effective ways to limit the spread of infectious diseases, and reduce mortality and morbidity in rural areas. Waning public confidence in vaccines, especially the COVID-19 vaccine, remains a cause for concern. A number of individuals in the US and worldwide remain complacent, choosing not to be vaccinated and/or delay COVID-19 vaccination, resulting in suboptimal herd immunity. The primary goal of this study is to identify modifiable factors contributing to COVID-19 vaccine hesitancy among vaccine-eligible individuals with access to vaccines in two under-resourced rural states, Alaska and Idaho. This qualitative study used semi-structured interviews with providers and focus groups with community participants in Alaska and Idaho. A moderator’s guide was used to facilitate interviews and focus groups conducted and recorded using Zoom and transcribed verbatim. Thematic, qualitative analysis was conducted using QDA Miner. Themes and subthemes that emerged were labeled, categorized, and compared to previously described determinants of general vaccine hesitancy: established contextual, individual and/or social influences, vaccine and vaccination-specific concerns. Themes (n = 9) and sub-themes (n = 51) identified during the qualitative analysis highlighted a factor’s contributing to COVID-19 vaccine hesitancy and poor vaccine uptake. Relevant influenceable factors were grouped into three main categories: confidence, complacency, and convenience. Vaccines are effective public health interventions to promote health and prevent diseases in rural areas. Practical solutions to engage healthcare providers, researchers, vaccine advocates, vaccine manufacturers, and other partners in local communities are needed to increase public trust in immunization systems to achieve community immunity.

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number of applications of clinical AI is increasing, which, amplified by the need for adaptations to account for the heterogeneity of local health systems and inevitable data drift, creates a fundamental challenge for regulators. Our opinion is that, at scale, the incumbent model of centralized regulation of clinical AI will not ensure the safety, efficacy, and equity of implemented systems. We propose a hybrid model of regulation, where centralized regulation would only be required for applications of clinical AI where the inference is entirely automated without clinician review, have a high potential to negatively impact the health of patients and for algorithms that are to be applied at national scale by design. This amalgam of centralized and decentralized regulation we refer to as a distributed approach to the regulation of clinical AI and highlight the benefits as well as the pre-requisites and challenges involved.

Aims: Our aims were to understand how hospital staff who are skilled at managing aggressive patients recognize and respond to patient aggression and to compare the approaches of skilled staff to the experiences of staff who were recently involved in incidents of patient violence. Background: Violence from patients toward staff is prevalent and increasing. There is a need for greater understanding of effective approaches to managing patient aggression in a wide variety of hospital settings. Methods: We conducted grounded theory qualitative research applying Critical Decision Method interviews at two hospitals. Skilled staff and incident-involved staff were asked to describe experiences involving aggressive patients and the data were analyzed qualitatively. Results: Our interviews (N = 23) identified positive approaches and challenges to managing aggressive patients. Positive approaches included: maintaining empathy for the patient, allowing the patient time and space, exhibiting a calm demeanor, not taking things personally, and implementing strategies to build trust. Challenges included: inadequate psychiatric resources, balancing priorities between patients with urgent physical needs and those exhibiting difficult behaviors, and perceiving pressure to de-escalate situations quickly. Incident-involved staff were more likely to describe the challenges listed above and a limited tolerance for patients whose behavior they perceived as unjustified or detracting from other patients' care. Conclusion: The Critical Decision Method proved valuable for highlighting nuanced understandings of skilled staff that sometimes contrasted with perceptions of incident-involved staff. Our findings support investigation of novel approaches to training such as peer coaching and improving empathy through increased understanding of mental illnesses and addiction.

Background Vaccination remains one of the most effective ways to limit the spread of infectious diseases such as that caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. Unfortunately, vaccination hesitancy continues to be a threat to national and global health. Further research is necessary to determine the modifiable and nonmodifiable factors contributing to COVID-19 vaccine hesitancy in under-resourced, underserved, and at-risk rural and urban communities. Objective This study aimed to identify, understand, and address modifiable barriers and factors contributing to COVID-19 vaccine hesitancy among vaccine-eligible individuals with access to the vaccine in Alaska and Idaho. Methods An electronic survey based on the World Health Organization (WHO) Strategic Advisory Group on Experts (SAGE) on Immunization survey tool and investigators’ previous work was created and distributed in June 2021 and July 2021. To be eligible to participate in the survey, individuals had to be ≥18 years of age and reside in Alaska or Idaho. Responses were grouped into 4 mutually exclusive cohorts for data analysis and reporting based on intentions to be vaccinated. Respondent characteristics and vaccine influences between cohorts were compared using Chi-square tests and ANOVA. Descriptive statistics were also used. Results There were data from 736 usable surveys with 40 respondents who did not intend to be vaccinated, 27 unsure of their intentions, 8 who intended to be fully vaccinated with no doses received, and 661 fully vaccinated or who intended to be vaccinated with 1 dose received. There were significant differences in characteristics and influences between those who were COVID-19 vaccine-hesitant and those who had been vaccinated. Concerns related to possible side effects, enough information on long-term side effects, and enough information that is specific to the respondent’s health conditions were seen in those who did not intend to be fully vaccinated and unsure about vaccination. In all cohorts except those who did not intend to be fully vaccinated, more information about how well the vaccine works was a likely facilitator to vaccination. Conclusions These survey results from 2 rural states indicate that recognition of individual characteristics may influence vaccine choices. However, these individual characteristics represent only a starting point to delivering tailored messages that should come from trusted sources to address vaccination barriers.

Introduction In many hospitals across the country, electrocardiograms of multiple at-risk patients are monitored remotely by telemetry monitor watchers in a central location. However, there is limited evidence regarding best practices for designing these cardiac monitoring systems to ensure prompt detection and response to life-threatening events. To identify factors that may affect monitoring efficiency, we simulated critical arrhythmias in inpatient units with different monitoring systems and compared their efficiency in communicating the arrhythmias to a first responder. Methods This was a multicenter cross-sectional in situ simulation study. Simulation participants were monitor watchers and first responders (usually nurses) in 2 inpatient units in each of 3 hospitals. Manipulated variables included: (1) number of communication nodes between monitor watchers and first responders; (2) central monitoring station location-on or off the patient care unit; (3) monitor watchers' workload; (4) nurses' workload; and (5) participants' experience. Results We performed 62 arrhythmia simulations to measure response times of monitor watchers and 128 arrhythmia simulations to measure response times in patient care units. We found that systems in which an intermediary between monitor watchers and nurses communicated critical events had faster response times to simulated arrhythmias than systems in which monitor watchers communicated directly with nurses. Responses were also faster in units colocated with central monitoring stations than in those located remotely. As the perceived workload of nurses increased, response latency also increased. Experience did not affect response times. Conclusions Although limited in our ability to isolate the effects of these factors from extraneous factors on central monitoring system efficiency, our study provides a roadmap for using in situ arrhythmia simulations to assess and improve monitoring performance.

Davis, Michael J. DO, MPH; Essenmacher, Michael DO; Marii, Anas MD; Faucher, Eileen PhD; Wright, Melanie C. PhD Author Information

Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score. PMID: 30121744

OBJECTIVETo determine the impact of a graphical information display on diagnosing circulatory shock.MATERIALS AND METHODSThis was an experimental study comparing integrated and conventional information displays. Participants were intensivists or critical care fellows (experts) and first-year medical residents (novices).RESULTSThe integrated display was associated with higher performance (87% vs 82%; P < .001), less time (2.9 vs 3.5 min; P = .008), and more accurate etiology (67% vs 54%; P = .048) compared to the conventional display. When stratified by experience, novice physicians using the integrated display had higher performance (86% vs 69%; P < .001), less time (2.9 vs 3.7 min; P = .03), and more accurate etiology (65% vs 42%; P = .02); expert physicians using the integrated display had nonsignificantly improved performance (87% vs 82%; P = .09), time (2.9 vs 3.3; P = .28), and etiology (69% vs 67%; P = .81).DISCUSSIONThe integrated display appeared to support efficient information processing, which resulted in more rapid and accurate circulatory shock diagnosis. Evidence more strongly supported a difference for novices, suggesting that graphical displays may help reduce expert-novice performance gaps.

Aimed at teaching assistants who work closely with children with special educational needs, The Effective Teaching Assistant: A Practical Guide to Supporting Achievement for Pupils with SEND is a practical and accessible resource tailored precisely for teaching assistants’ specific needs, which explores both the opportunities and limitations presented by their role. Each chapter provides both training activities and teaching resources designed to assist TAs/HLTAs in reflecting on their own experience while enhancing current practice. The chapters address key topics including SEND and inclusive teaching Multi-sensory teaching Supporting differentiation or adaptive teaching. Supplemented with checklists and useful diagrams, this text is essential reading for teaching assistants, students and practitioners. It is particularly relevant for students working in undergraduate, post graduate and professional development programmes.