Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.

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.

Interim decision making in seamless trial designs: An application in an adaptive dose-finding study in a rare kidney disease.

Adaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re-estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how to enable efficient decision making on the adaptations in interim analyzes, remains a key challenge in their adoption. This manuscript uses a case study of an adaptive seamless proof-of-concept (Phase 2a)/dose-finding (Phase 2b) to showcase potential adaptive features that can be implemented in trial designs at earlier development stages and the role of simulations in assessing the design operating characteristics and specifying the decision rules for the adaptations. It further outlines the elements needed to support successful interim analysis decision making on the adaptations while safeguarding study integrity, including the role of different stakeholders, interactive simulation-based tools to facilitate decision making and operational aspects requiring preplanning. The benefits of the adaptive Phase 2a/2b design chosen compared to following the traditional two separate studies (2a and 2b) paradigm are discussed. With careful planning and appreciation of their complexity and components needed for their implementation, seamless adaptive designs have the potential to yield significant savings both in terms of time and resources.

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.

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.

Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study.

Background: Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. Objectives: To support and ensure safe use of AI/ML technologies in healthcare, 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 healthcare applications; and 3) on 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 healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) 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 sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, 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/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Conclusion: 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.

Factors contributing to vaccine hesitancy and reduced vaccine confidence in rural underserved populations.

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.

Patient-Guided Talking Points to Address COVID-19 and General Vaccine Hesitancy.

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.

Influences, Barriers, and Facilitators to COVID-19 Vaccination: Cross-sectional Survey on Vaccine Hesitancy in 2 Rural States.

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.

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review.

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.

Preventing patient violence in hospitals: Applying critical decision method interviews to understand how skilled staff think and act differently.

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.

Effect of Remote Cardiac Monitoring System Design on Response Time to Critical Arrhythmias.

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.

A distributed approach to the regulation of clinical AI.

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.

Why ITT analysis is not always the answer for estimating treatment effects in clinical trials.

For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial "estimand", a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic.

Long-term outcome and risk factors for late mortality in Gram-negative bacteraemia: a retrospective cohort study.

OBJECTIVES: The long-term outcomes of patients following Gram-negative bacteraemia (GNB) are poorly understood. Here we describe a cohort of patients with GNB over a 2-year period and determine factors associated with late mortality (death between Days 31 and 365 after detection of bacteraemia). METHODS: This was a single-centre, retrospective, observational cohort study of 789 patients with confirmed Escherichia coli, Klebsiella spp. or Pseudomonas aeruginosa bacteraemia with a follow-up of 1 year. Multivariable survival analysis was used to determine risk factors for late mortality in patients who survived the initial 30-day period of infection. RESULTS: Overall, 1-year all-cause mortality was 36.2%, with 18.1% of patients dying within 30 days and 18.1% of patients suffering late mortality. An adverse antimicrobial resistance profile [hazard ratio (HR) = 1.095 per any additional antimicrobial category, 95% confidence interval (CI) 1.018-1.178; P = 0.014] and infection with P. aeruginosa (HR = 2.08, 95% CI 1.11-3.88; P = 0.022) were independent predictors of late mortality. Other significant factors included Charlson comorbidity index and length of hospitalisation after the index blood culture. CONCLUSION: Patients with GNB have a poor long-term prognosis. Risk factors for greater mortality at 1 year include co-morbidity, length of hospitalisation, and infecting organism and its resistance profile.

Preliminary Validation of a Patient-Reported Measure of the Age-Friendliness of Health Care.

BACKGROUND/OBJECTIVE: To evaluate the validity and reliability of a patient-reported measure of the "age-friendliness" of health care. DESIGN: Based on four essential domains of high-quality health care for older outpatients (Medications, Mobility, Mentation and "what Matters," i.e., the 4 M's), we drafted a five-item questionnaire for older outpatients to rate the age-friendliness of their health care. One question addressed each of the 4 M's; the fifth addressed the overall age-friendliness of their care. After feedback from healthcare professionals, quality improvement experts, and a patient-caregiver focus group, we revised the items to create the Age-Friendliness Questionnaire (AFQ). SETTING We tested the AFQ by appending it to two surveys. PARTICIPANTS: Older outpatients in Idaho during July to October 2019: Survey 1, with 23 other items, was sent to 1,257 older patients who were medically complex; Survey 2, with 35 other items, was sent to 2,873 older patients who visited outpatient primary care providers (PCPs) during the specified time period. MEASUREMENTS: Respondents rated their providers' performance using a 1 to 5 ("never" to "always") scale for each of the five items (possible AFQ scores = 5-25). RESULTS: The response rates were 41.4% and 33.3%, respectively. In Survey 1, the mean AFQ score from patients who had received care from a geriatrics consult clinic was higher than that from patients who had received their care from PCPs (19.3 vs 15.6; P < .001), and AFQ scores correlated with other quality-of-care scores. In Survey 2, AFQ scores predicted respondents' likelihood of recommending their providers to others (P < .001). The AFQ exhibited high internal reliability (interitem correlations = .49-.77; Cronbach's α = .89). CONCLUSION: The AFQ appears to be a valid and reliable measure of the age-friendliness of outpatient care for older patients, and it predicts the likelihood that they will recommend their providers to others.

Atypical presentation of COVID-19 in hospitalised older adults.

BACKGROUND: It is increasingly recognised that older patients may not present with typical symptoms of COVID-19. AIMS: This study aims to evaluate the incidence, characteristics and clinical outcome of older adults with atypical presentations of COVID-19. METHODS: A retrospective analysis of adults ≥ 65 years with confirmed COVID-19 admitted to our institution between 1 March and 24 April 2020 was performed. Patients were categorised into typical or atypical groups based on primary presenting complaint in the community. RESULTS: One hundred twenty-two patients (mean age 81 ± 8 years; 62 male) were included. Seventy-three (60%) were categorised into the typical group and 49 (40%) into the atypical group. In the atypical group, common presenting complaints were fall in 18 (36%), reduced mobility or generalised weakness in 18 (36%) and delirium in 11 (22%). Further assessment by paramedics and on admission found 32 (65%) to have typical features of COVID-19, fever being the most common, and 22 (44%) were hypoxic. This subset had worse outcomes than those in the typical group with a mortality rate of 50% versus 38%, respectively, although this was not statistically significant (P = 0.27). No significant difference in mortality or length of hospital stay between the groups was demonstrated. CONCLUSION: Older patients with atypical presentation of COVID-19 in the community are equally susceptible to poor outcomes. Early detection may improve outcomes and limit community transmission. Primary care practitioners should be vigilant and consider prompt onward referral.

Iterative heuristic design of temporal graphic displays with clinical domain experts.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data.

Impact of antibiotic timing on mortality from Gram-negative bacteraemia in an English district general hospital: the importance of getting it right every time.

OBJECTIVES: There is limited evidence that empirical antimicrobials affect patient-oriented outcomes in Gram-negative bacteraemia. We aimed to establish the impact of effective antibiotics at four consecutive timepoints on 30 day all-cause mortality and length of stay in hospital. METHODS: We performed a multivariable survival analysis on 789 patients with Escherichia coli, Klebsiella spp. and Pseudomonas aeruginosa bacteraemias. Antibiotic choices at the time of the blood culture (BC), the time of medical clerking and 24 and 48 h post-BC were reviewed. RESULTS: Patients that received ineffective empirical antibiotics at the time of the BC had higher risk of mortality before 30 days (HR = 1.68, 95% CI = 1.19-2.38, P = 0.004). Mortality was higher if an ineffective antimicrobial was continued by the clerking doctor (HR = 2.73, 95% CI = 1.58-4.73, P < 0.001) or at 24 h from the BC (HR = 1.83, 95% CI = 1.05-3.20, P = 0.033) when compared with patients who received effective therapy throughout. Hospital-onset infections, 'high inoculum' infections and elevated C-reactive protein, lactate and Charlson comorbidity index were independent predictors of mortality. Effective initial antibiotics did not statistically significantly reduce length of stay in hospital (-2.98 days, 95% CI = -6.08-0.11, P = 0.058). The primary reasons for incorrect treatment were in vitro antimicrobial resistance (48.6%), initial misdiagnosis of infection source (22.7%) and non-adherence to hospital guidelines (15.7%). CONCLUSIONS: Consecutive prescribing decisions affect mortality from Gram-negative bacteraemia.

Impact of integrated graphical display on expert and novice diagnostic performance in critical care.

OBJECTIVE: To determine the impact of a graphical information display on diagnosing circulatory shock. MATERIALS AND METHODS: This was an experimental study comparing integrated and conventional information displays. Participants were intensivists or critical care fellows (experts) and first-year medical residents (novices). RESULTS: The 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). DISCUSSION: The 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.

Organizing Audible Alarm Sounds in the Hospital: A Card-Sorting Study.

In hospitals, clinicians are presented with varied and disorganized alarm sounds from disparate devices. While there has been attention to reducing inactionable alarms to address alarm overload, little effort has focused on organizing, simplifying, or improving the informativeness of alarms. We sought to elicit nurses' tacit interpretation of alarm events to create an organizational structure to inform the design of advanced alarm sounds or integrated alert systems. We used open card sorting to evaluate nurses' perception of the relatedness of different alarm events. Seventy hospital nurses sorted 89 alarm events into groups they believed could or should be indicated by the same sound. We conducted factor analysis on a similarity matrix of frequency of alarm event pairings to interpret how strongly alarm events loaded on different alarm groups (factors). We interpreted participants' grouping rationale from their group labels and comments. Urgency of response was the most common grouping rationale. Participants also grouped: 1) monitoring-related events, 2) device-related events, and 3) events related to calls and patients. Our findings support standardization and integration of alarm sounds across devices toward a simpler and more informative hospital alarm environment.