Search

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Objective: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs., Materials and Methods: A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: "ARDS", "Pneumonia", or "Normal"., Results: A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS., Discussion: The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports., Conclusion: A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Challenges with SARS-CoV-2 vaccine prioritization, access, and hesitancy have influenced vaccination uptake., Research Question: Was the impact of SARS-CoV-2 vaccine rollout on COVID-19 monthly admission and mortality trends different between Hispanic and non-Hispanic populations?, Study Design and Methods: We used interrupted time series analysis to conduct an ancillary study of the Viral Infection and Respiratory Illness Universal Study registry supplemented by electronic health record data from five participating Mayo Clinic sites in Florida, Arizona, Minnesota, and Wisconsin. We included hospitalized patients with COVID-19 admitted between April 2020 and December 2021. Our primary outcome was the impact of vaccine rollout on admission trends. Our secondary outcome was the impact of vaccine rollout on mortality trends., Results: This interrupted time series analysis includes 6,442 patients. Vaccine rollout was associated with improved monthly hospital admission trends among both Hispanic and non-Hispanic patients. Among Hispanic patients, pre-vaccine rollout, monthly admissions increased by 12.9% (95% CI, 8.1%-17.9%). Immediately after vaccine rollout, patient admissions declined by -66.3% (95% CI, -75.6% to -53.9%). Post-vaccine rollout, monthly admissions increased by 3.7% (95% CI, 0.2%-7.3%). Among non-Hispanic patients, pre-vaccine rollout, monthly admissions increased by 35.8% (95% CI, 33.4%-38.1%). Immediately after vaccine rollout, patient admissions declined by -75.2% (95% CI, -77.6% to -72.7%). Post-vaccine rollout, monthly admissions increased by 5.6% (95% CI, 4.5%-6.7%). These pre-vaccine rollout admission trends were significantly different (P < .001). Post-vaccine rollout, the change in admission trend was significantly different (P < .001). The associated beneficial impact from vaccine rollout on monthly hospital admission trends among Hispanic patients was significantly lower. The trend in monthly mortality rate was fourfold greater (worse) among Hispanic patients (8.3%; 95% CI, 3.6%-13.4%) vs non-Hispanic patients (2.2%; 95% CI, 0.6%-3.8%), but this was not shown to be related to vaccine rollout., Interpretation: SARS-CoV-2 vaccine rollout was associated with improved COVID-19 admission trends among non-Hispanic vs Hispanic patients. Vaccine rollout was not shown to influence mortality trends in either group, which were four times higher among Hispanic patients. Improved vaccine rollout may have reduced disparities in admission trends for Hispanic patients, but other factors influenced their mortality trends., Competing Interests: Financial/Nonfinancial Disclosures None declared., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Objectives: Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters., Materials and Methods: This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software., Results: We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias., Discussion: This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers., Conclusion: Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: The complement pathway is a potential target for the treatment of severe COVID-19. We evaluated the safety and efficacy of ravulizumab, a terminal complement C5 inhibitor, in patients hospitalised with severe COVID-19 requiring invasive or non-invasive mechanical ventilation., Methods: This phase 3, multicentre, open-label, randomised controlled trial (ALXN1210-COV-305) enrolled adult patients (aged ≥18 years) from 31 hospitals in France, Japan, Spain, the UK, and the USA. Eligible patients had a confirmed diagnosis of SARS-CoV-2 that required hospitalisation and either invasive or non-invasive mechanical ventilation, with severe pneumonia, acute lung injury, or acute respiratory distress syndrome confirmed by CT scan or x-ray. We randomly assigned participants (2:1) to receive intravenous ravulizumab plus best supportive care (BSC) or BSC alone using a web-based interactive response system. Randomisation was in permuted blocks of six with stratification by intubation status. Bodyweight-based intravenous doses of ravulizumab were administered on days 1, 5, 10, and 15. The primary efficacy endpoint was survival based on all-cause mortality at day 29 in the intention-to-treat (ITT) population. Safety endpoints were analysed in all randomly assigned patients in the ravulizumab plus BSC group who received at least one dose of ravulizumab, and in all randomly assigned patients in the BSC group. The trial is registered with ClinicalTrials.gov, NCT04369469, and was terminated at interim analysis due to futility., Findings: Between May 10, 2020, and Jan 13, 2021, 202 patients were enrolled in the study and randomly assigned to ravulizumab plus BSC or BSC. 201 patients were included in the ITT population (135 in the ravulizumab plus BSC group and 66 in the BSC group). The ravulizumab plus BSC group comprised 96 (71%) men and 39 (29%) women with a mean age of 63·2 years (SD 13·23); the BSC group comprised 43 (65%) men and 23 (35%) women with a mean age of 63·5 years (12·40). Most patients (113 [84%] of 135 in the ravulizumab plus BSC group and 53 [80%] of 66 in the BSC group) were on invasive mechanical ventilation at baseline. Overall survival estimates based on multiple imputation were 58% for patients receiving ravulizumab plus BSC and 60% for patients receiving BSC (Mantel-Haenszel analysis: risk difference -0·0205; 95% CI -0·1703 to 0·1293; one-sided p=0·61). In the safety population, 113 (89%) of 127 patients in the ravulizumab plus BSC group and 56 (84%) of 67 in the BSC group had a treatment-emergent adverse event. Of these events, infections and infestations (73 [57%] vs 24 [36%] patients) and vascular disorders (39 [31%] vs 12 [18%]) were observed more frequently in the ravulizumab plus BSC group than in the BSC group. Five patients had serious adverse events considered to be related to ravulizumab. These events were bacteraemia, thrombocytopenia, oesophageal haemorrhage, cryptococcal pneumonia, and pyrexia (in one patient each)., Interpretation: Addition of ravulizumab to BSC did not improve survival or other secondary outcomes. Safety findings were consistent with the known safety profile of ravulizumab in its approved indications. Despite the lack of efficacy, the study adds value for future research into complement therapeutics in critical illnesses by showing that C5 inhibition can be accomplished in severely ill patients., Funding: Alexion, AstraZeneca Rare Disease., Competing Interests: Declaration of interests DA received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease and is co-inventor on pending patent WO2021211940A1. SJP has received funding for this study, grants or contracts, consulting fees, and honoraria and travel expenses for lectures, advisory boards, and conference attendance from Alexion, AstraZeneca Rare Disease (all paid to institution). SJP also receives royalties from a patent (US-9891219-B). HSK received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease; grants or contracts (paid to institution) from the US National Institutes of Health, the US Department of Defense, the Children's Discovery Institute, and the Longer Life Foundation; consulting fees (personal) from Indemic and Guidepoint; honoraria (personal) from the University of Pittsburgh; support for attending meetings or travel (personal) from the American Thoracic Society; and has participated in an unpaid capacity in advocacy groups, boards, or committees for the American Thoracic Society and the American Society of Clinical Investigation. HSK also has stock in Moderna. BWP received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease; consulting fees (personal) from Philips; and royalties and stock options from Ambient Clinical Analytics, of which he is a member of the board. BWP is also co-inventor on a patent (held by Ambient Clinical Analytics) for the presentation of critical clinical information. MRK received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease. JLS received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease. CAP received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease; and is Chair of the Senhwa Biosciences data safety monitoring board for a COVID-19 clinical trial. PC received funding (paid to institution) for this study, honoraria (personal) for teaching lectures and participation in advisory boards, and payment for expert testimony (personal) from Alexion, AstraZeneca Rare Disease. TF received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease. DD and SK are employees of Alexion, AstraZeneca Rare Disease, own stock in AstraZeneca, and are co-inventors on pending patent WO2021211940A1. KS and SM are employees of Alexion, AstraZeneca Rare Disease and own stock in AstraZeneca. AK received funding (paid to institution) for this study from Alexion, AstraZeneca Rare Disease; consulting fees (personal) from Regeneron, Samsung, Silence Therapeutics, and Novo Nordisk; and honoraria or support for attending meetings or travel (personal) from Alexion, AstraZeneca Rare Disease, Amgen, Sobi, Celgene (a subsidiary of BMS), Biocryst, Pfizer, Roche, Novartis, and Janssen., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms., Methods: We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach., Results: Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting.Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12-1.24) to 4.5 (1.8-11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive.Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction.The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies., Conclusions: This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations., Trial Registration Number: The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display&#95;record.php?ID=CRD42020178885., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Background: To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard., Methods: We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software., Results: We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU., Conclusions: This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care., (© 2023. The Author(s).)

There is a lack of diagnostic performance measures associated with pulmonary embolism (PE). We aimed to explore the concept of the time to diagnostic certainty, which we defined as the time interval that elapses between first presentation of a patient to a confirmed PE diagnosis with computed tomography pulmonary angiogram (CT PA). This approach could be used to highlight variability in health system diagnostic performance, and to select patient outliers for structured chart review in order to identify underlying contributors to diagnostic error or delay. We performed a retrospective observational study at academic medical centers and associated community-based hospitals in one health system, examining randomly selected adult patients admitted to study sites with a diagnosis of acute saddle PE. One hundred patients were randomly selected from 340 patients discharged with saddle PE. Twenty-four patients were excluded. Among the 76 included patients, time to diagnostic certainty ranged from 1.5 to 310 hours. We found that 73/76 patients were considered to have PE present on admission (CT PA ≤ 48 hours). The proportion of patients with PE present on admission with time to diagnostic certainty of > 6 hours was 26% (19/73). The median (IQR) time to treatment (thrombolytics/anticoagulants) was 3.5 (2.5-5.1) hours among the 73 patients. The proportion of patients with PE present on admission with treatment delays of > 6 hours was 16% (12/73). Three patients acquired PE during hospitalization (CT PA > 48 hours). In this study, we developed and successfully tested the concept of time to diagnostic certainty for saddle PE.

Context: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge., Objectives: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital., Methods: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit., Results: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11-1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55-0.93]) respectively., Conclusion: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations., (Copyright © 2023 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.)

To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR)., Design: Single center randomized crossover study., Setting: Quaternary care academic hospital., Subjects: Attending and in-training critical care physicians, and advanced practice providers., Interventions: AMP., Measurements and Main Results: We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078)., Conclusions: When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Purpose: The diagnosis of pulmonary blastomycosis is usually delayed because of its non-specific presentation. We aimed to assess the extent of diagnostic delay in hospitalized patients and detect the step in the diagnostic process that requires the most improvement., Methods: Adult patients diagnosed with pulmonary blastomycosis during a hospital admission between January 2010 through November 2021 were eligible for inclusion. Patients who did not have pulmonary involvement and who were diagnosed before admission were excluded. Demographics and comorbid conditions, specifics of disease presentation, and interventions were evaluated. The timing of the diagnosis, antifungal treatment, and patient outcomes were noted. Descriptive analytical tests were performed., Results: A total of 43 patients were diagnosed with pulmonary blastomycosis during their admissions. The median age was 47 years, with 13 (30%) females. Of all patients, 29 (67%) had isolated pulmonary infection, while 14 (33%) had disseminated disease, affecting mostly skin and musculoskeletal system. The median duration between the initial symptoms and health care encounters was 4 days, and the time to hospital admission was 9 days. The median duration from the initial symptoms to the diagnosis was 20 days. Forty patients (93%) were treated with empirical antibacterials before a definitive diagnosis was made. In addition, corticosteroid treatment was empirically administered to 15 patients (35%) before the diagnosis, with indications such as suspicion of inflammatory processes or symptom relief. In 38 patients (88%), the first performed fungal diagnostic test was positive. Nineteen patients (44%) required admission to the intensive care unit, and 11 patients (26%) died during their hospital stay., Conclusion: There was a delay in diagnosis of patients with pulmonary blastomycosis, largely attributable to the lack of consideration of the etiological agent. Novel approaches to assist providers in recognizing the illness earlier and trigger evaluation are needed., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign., Methods: A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9-12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4., Results: In total, 107 direct care ICU nurses completed the survey. The majority (46%, n  = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent ( n  = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n  = 105), continuous IV medications (4.55 ± 0.73, 93%, n  = 99), laboratory results (4.60 ± 0.56, 96%, n  = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n  = 97), code status (4.40 ± 0.85, 59%, n  = 108), and ventilation status (4.35 +   0.79, 51%, n  = 108). Secondary outcomes included mean EHR satisfaction of 65 (0-100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n  = 57/107) and visualized by tasks/schedule (61%, n  = 65/107)., Conclusion: We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objective: Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI., Method: Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used., Result: The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort., Conclusions: We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings., Data Sources: We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021., Study Selection: We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point., Data Extraction: Study data were abstracted by two independent reviewers using a standardized data extraction form., Data Synthesis: Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall., Conclusions: HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time., Competing Interests: Dr. Pickering received funding from Ambient Clinical Analytics; he disclosed that he is a board member of Ambient Clinical Analytics; he received support for article research from the Agency for Healthcare Research and Quality. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objectives: This study aimed to explore clinicians' perceptions of the occurrence of and factors associated with diagnostic errors in patients evaluated during a rapid response team (RRT) activation or unplanned admission to the intensive care unit (ICU)., Methods: A multicenter prospective survey study was conducted among multiprofessional clinicians involved in the care of patients with RRT activations and/or unplanned ICU admissions (UIAs) at 2 academic hospitals and 1 community-based hospital between April 2019 and March 2020. A study investigator screened eligible patients every day. Within 24 hours of the event, a research coordinator administered the survey to clinicians, who were asked the following: whether diagnostic errors contributed to the reason for RRT/UIA, whether any new diagnosis was made after RRT/UIA, if there were any failures to communicate the diagnosis, and if involvement of specialists earlier would have benefited that patient. Patient clinical data were extracted from the electronic health record., Results: A total of 1815 patients experienced RRT activations, and 1024 patients experienced UIA. Clinicians reported that 18.2% (95/522) of patients experienced diagnostic errors, 8.0% (42/522) experienced a failure of communication, and 16.7% (87/522) may have benefitted from earlier involvement of specialists. Compared with academic settings, clinicians in the community hospital were less likely to report diagnostic errors (7.0% versus 22.8%, P = 0.002)., Conclusions: Clinicians report a high rate of diagnostic errors in patients they evaluate during RRT or UIAs., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Administrative and clinical efforts to improve hospital mortality and intensive care utilization commonly focus on patient rescue, where deteriorating patients are systematically identified and intervened upon. Patient rescue is known to depend on hospital context inclusive of technologic environment, structural features, and hospital organizational behavioral features. With widespread adoption of electronic medical records, early warning score (EWS) systems, which assign points to clinical data elements, are increasingly promoted as a tool for timely patient rescue by referencing their prediction of patient deterioration. We describe the extent to which EWS intervention studies describe the hospital environment of the intervention-details that would be critical for hospital leaders attempting to determine the real-world utility of EWSs in their own hospitals., Data Sources: We searched CINAHL, PubMed, and Scopus databases for English language EWS implementation research published between 2009 and 2021 in adult medical-surgical inpatients., Study Selection: Studies including pediatric, obstetric, psychiatric, prehospital, outpatient, step-down, or ICU patients were excluded., Data Extraction: Two investigators independently reviewed titles/abstracts for eligibility based on prespecified exclusion criteria., Data Synthesis: We identified 1,434 studies for title/abstract screening. In all, 352 studies underwent full-text review and 21 studies were summarized. The 21 studies (18 before-and-after, three randomized trials) detailed 1,107,883 patients across 54 hospitals. Twelve reported the staff composition of an EWS response team. Ten reported the proportion of surgical patients. One reported nursing ratios; none reported intensive care staffing with in-house critical-care physicians. None measured changes in bed utilization or availability. While 16 qualitatively described resources for education/technologic implementation, none estimated costs. None described workforce composition such as team stability or culture of safety in the hospitals., Conclusions: Despite hundreds of EWS-related publications, most do not report details of hospital context that would inform decisions about real-world EWS adoption. To make informed decisions about whether EWS implementation improves hospital quality, decision-makers may require alternatives such as peer networks and implementation pilots nested within local health systems., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Objectives: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review., Design: We performed a single-center diagnostic performance study., Patients and Setting: A prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units., Measurements and Main Results: During the study time, 5,384 out of 6,630 hospital admissions were eligible. During the same period, the 4TDS shock model sent 825 alerts and 632 were eligible. Among 632 hospital admissions with alerts, 287 were screened positive and 345 were negative. Among 4,752 hospital admissions without alerts, 78 were screened positive and 4,674 were negative. The area under the receiver operating characteristics curve for the 4TDS shock model was 0.86 (95% CI 0.85-0.87%). The 4TDS shock model demonstrated a sensitivity of 78.6% (95% CI 74.1-82.7%) and a specificity of 93.1% (95% CI 92.4-93.8%). The model showed a positive predictive value of 45.4% (95% CI 42.6-48.3%) and a negative predictive value of 98.4% (95% CI 98-98.6%)., Conclusions: We successfully validated an ML model to detect circulatory shock in a prospective observational study. The model used only vital signs and showed moderate performance compared to the gold standard of clinician EMR review when applied to an ICU patient cohort., (© The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team's cognitive capacity., Methods: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team's decision making., Results: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19., Conclusions: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team's cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload., (© 2022. The Author(s).)

Objective: To improve the timely diagnosis and treatment of sepsis many institutions implemented automated sepsis alerts. Poor specificity, time delays, and a lack of actionable information lead to limited adoption by bedside clinicians and no change in practice or clinical outcomes. We aimed to compare sepsis care compliance before and after a multi-year implementation of a sepsis surveillance coupled with decision support in a tertiary care center., Design: Single center before and after study., Setting: Large academic Medical Intensive Care Unit (MICU) and Emergency Department (ED)., Population: Patients 18 years of age or older admitted to *** Hospital MICU and ED from 09/4/2011 to 05/01/2018 with severe sepsis or septic shock., Interventions: Electronic medical record-based sepsis surveillance system augmented by clinical decision support and completion feedback., Measurements and Main Results: There were 1950 patients admitted to the MICU with the diagnosis of severe sepsis or septic shock during the study period. The baseline characteristics were similar before (N = 854) and after (N = 1096) implementation of sepsis surveillance. The performance of the alert was modest with a sensitivity of 79.9%, specificity of 76.9%, positive predictive value (PPV) 27.9%, and negative predictive value (NPV) 97.2%. There were 3424 unique alerts and 1131 confirmed sepsis patients after the sniffer implementation. During the study period average care bundle compliance was higher; however after taking into account improvements in compliance leading up to the intervention, there was no association between intervention and improved care bundle compliance (Odds ratio: 1.16; 95% CI: 0.71 to 1.89; p-value 0.554). Similarly, the intervention was not associated with improvement in hospital mortality (Odds ratio: 1.55; 95% CI: 0.95 to 2.52; p-value: 0.078)., Conclusions: A sepsis surveillance system incorporating decision support or completion feedback was not associated with improved sepsis care and patient outcomes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Mayo Clinic and Drs. Herasevich and Gajic have a Financial Conflict of Interest related to this research. Sepsis sniffer is patented - US 8,527,449 B2 and licensed to Ambient Clinical Analytics Inc. Dr. Pickering is on Board of Directors, Ambient Clinical Analytics. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies., (Copyright © 2021. Published by Elsevier Inc.)

Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.

Background: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care., Methods: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance., Discussion: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor., Trial Registration: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start., (© 2021. The Author(s).)

Background: The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood., Objective: This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR)., Methods: Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS., Study Eligibility Criteria: Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021., Participants: All physicians and nurses involved in inpatient and outpatient settings., Study Appraisal and Synthesis Methods: A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs., Results: Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity., Conclusion: This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization., Competing Interests: None declared., (Thieme. All rights reserved.)

Objective: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team., Materials and Methods: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team., Results: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes., Conclusions: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation., Materials and Methods: Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit., Results: The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset., Conclusions: We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic's ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models., (© The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Video recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU., Design: A 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU., Setting: The survey was conducted at Mayo Clinic in Minnesota and Florida., Results: A total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient-clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios., Conclusion: The survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU., Competing Interests: None declared., (Thieme. All rights reserved.)

A growing number of studies on coronavirus disease 2019 (COVID-19) are becoming available, but a synthesis of available data focusing on the critically ill population has not been conducted. We performed a scoping review to synthesize clinical characteristics, treatment, and clinical outcomes among critically ill patients with COVID-19. Between January 1, 2020, and May 15, 2020, we identified high-quality clinical studies describing critically ill patients with a sample size of greater than 20 patients by performing daily searches of the World Health Organization and LitCovid databases on COVID-19. Two reviewers independently reviewed all abstracts (2785 unique articles), full text (218 articles), and abstracted data (92 studies). The 92 studies included 61 from Asia, 16 from Europe, 10 from North and South America, and 5 multinational studies. Notable similarities among critically ill populations across all regions included a higher proportion of older males infected and with severe illness, high frequency of comorbidities (hypertension, diabetes, and cardiovascular disease), abnormal chest imaging findings, and death secondary to respiratory failure. Differences in regions included newly identified complications (eg, pulmonary embolism) and epidemiological risk factors (eg, obesity), less chest computed tomography performed, and increased use of invasive mechanical ventilation (70% to 100% vs 15% to 47% of intensive care unit patients) in Europe and the United States compared with Asia. Future research directions should include proof-of-mechanism studies to better understand organ injuries and large-scale collaborative clinical studies to evaluate the efficacy and safety of antivirals, antibiotics, interleukin 6 receptor blockers, and interferon. The current established predictive models require further verification in other regions outside China., (Copyright © 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis., Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin., Setting: Medical ICU of a large quaternary- care academic medical center in the United States., Patients or Subjects: Adult (> 18 year yr old), medical ICU patients were included in the study., Interventions: No additional interventions were made beyond the standard of care for this study., Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54-66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0-14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%)., Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Objective: To assess the efficacy and safety of lenzilumab in patients with severe coronavirus disease 2019 (COVID-19) pneumonia., Methods: Hospitalized patients with COVID-19 pneumonia and risk factors for poor outcomes were treated with lenzilumab 600 mg intravenously for three doses through an emergency single-use investigational new drug application. Patient characteristics, clinical and laboratory outcomes, and adverse events were recorded. We also identified a cohort of patients matched to the lenzilumab patients for age, sex, and disease severity. Study dates were March 13, 2020, to June 18, 2020. All patients were followed through hospital discharge or death., Results: Twelve patients were treated with lenzilumab; 27 patients comprised the matched control cohort (untreated). Clinical improvement, defined as improvement of at least 2 points on the 8-point ordinal clinical endpoints scale, was observed in 11 of 12 (91.7%) patients treated with lenzilumab and 22 of 27 (81.5%) untreated patients. The time to clinical improvement was significantly shorter for the lenzilumab-treated group compared with the untreated cohort with a median of 5 days versus 11 days (P=.006). Similarly, the proportion of patients with acute respiratory distress syndrome (oxygen saturation/fraction of inspired oxygen<315 mm Hg) was significantly reduced over time when treated with lenzilumab compared with untreated (P<.001). Significant improvement in inflammatory markers (C-reactive protein and interleukin 6) and markers of disease severity (absolute lymphocyte count) were observed in patients who received lenzilumab, but not in untreated patients. Cytokine analysis showed a reduction in inflammatory myeloid cells 2 days after lenzilumab treatment. There were no treatment-emergent adverse events attributable to lenzilumab., Conclusion: In high-risk COVID-19 patients with severe pneumonia, granulocyte-macrophage colony-stimulating factor neutralization with lenzilumab was safe and associated with faster improvement in clinical outcomes, including oxygenation, and greater reductions in inflammatory markers compared with a matched control cohort of patients hospitalized with severe COVID-19 pneumonia. A randomized, placebo-controlled clinical trial to validate these findings is ongoing (NCT04351152)., (Copyright © 2020. Published by Elsevier Inc.)

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVID pos ; n = 2,317) versus COVID-19-negative (COVID neg ; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVID pos over COVID neg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID pos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis., Competing Interests: TW, KM, SA, AV, SB, AP, MK, PA, ML, ZD, ES, HS, AA, RB, VS is an employee of nference and has financial interests in the company. FS, BP, JO, PB, RR, PV, ZT, SR, MM, WW, DC, GG, AW, WM, JH, AB has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies., (© 2020, Wagner et al.)

Background: Although diagnostic error (DE) is a significant problem, it remains challenging for clinicians to identify it reliably and to recognize its contribution to the clinical trajectory of their patients. The purpose of this work was to evaluate the reliability of real-time electronic health record (EHR) reviews using a search strategy for the identification of DE as a contributor to the rapid response team (RRT) activation., Objectives: Early and accurate recognition of critical illness is of paramount importance. The objective of this study was to test the feasibility and reliability of prospective, real-time EHR reviews as a means of identification of DE., Methods: We conducted this prospective observational study in June 2019 and included consecutive adult patients experiencing their first RRT activation. An EHR search strategy and a standard operating procedure were refined based on the literature and expert clinician inputs. Two physician-investigators independently reviewed eligible patient EHRs for the evidence of DE within 24 hours after RRT activation. In cases of disagreement, a secondary review of the EHR using a taxonomy approach was applied. The reviewers categorized patient experience of DE as Yes/No/Uncertain., Results: We reviewed 112 patient records. DE was identified in 15% of cases by both reviewers. Kappa agreement with the initial review was 0.23 and with the secondary review 0.65. No evidence of DE was detected in 60% of patients. In 25% of cases, the reviewers could not determine whether DE was present or absent., Conclusion: EHR review is of limited value in the real-time identification of DE in hospitalized patients. Alternative approaches are needed for research and quality improvement efforts in this field., Competing Interests: B.W.P. reports other from Ambient Clinical Analytics, outside the submitted work. In addition, B.W.P. has a patent Presentation of Critical Patient Data with royalties paid to Ambient Clinical Analytics. J.S. reports grants from the Agency for Healthcare Research and Quality and grants from the Society of Critical Care Medicine 2019 SCCM Discovery Grant Award, during the conduct of the study. Y.D. reports grants from AHRQ, during the conduct of the study., (Georg Thieme Verlag KG Stuttgart · New York.)

Objective: Despite increased use of electronic health records (EHRs), the clinical impact of system downtime is unknown., Materials and Methods: This retrospective matched cohort study evaluated the impact of EHR downtime episodes lasting more than 60 minutes over a 6-year study period. Patients age 18 years or older who underwent surgical procedures at least 60 minutes in duration with an inpatient stay exceeding 24 hours within the study period were eligible for inclusion. Out of 4115 patients exposed to 1 of 176 EHR downtime episodes, 4103 patients were matched to an unexposed cohort in a 1:1 ratio. Multivariable regression analysis, as well as trend analysis for effect of duration of downtime on outcomes, was performed., Results: Downtime-exposed patients had operating room duration 1.1 times longer (p < .001) and postoperative length of stay 1.04 times longer (p = .007) compared to unexposed patients. The 30-day mortality rates were similar between these groups (odds ratio 1.26, p > .05). In trend analysis, there was no association between duration of downtime with respect to evaluated outcomes, postoperative length of stay, and 30-day mortality., Conclusion: EHR downtime had no impact on 30-day mortality. Potential associations for increased postoperative length of stay and duration of time spent in the operating room were observed among downtime-exposed patients. No trend effect was observed with respect to duration of downtime and postoperative length of stay and 30-day mortality rates., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: To reliably improve diagnostic fidelity and identify delays using a standardized approach applied to the electronic medical records of patients with emerging critical illness., Patients and Methods: This retrospective observational study at Mayo Clinic, Rochester, Minnesota, conducted June 1, 2016, to June 30, 2017, used a standard operating procedure applied to electronic medical records to identify variations in diagnostic fidelity and/or delay in adult patients with a rapid response team evaluation, at risk for critical illness. Multivariate logistic regression analysis identified predictors and compared outcomes for those with and without varying diagnostic fidelity and/or delay., Results: The sample included 130 patients. Median age was 65 years (interquartile range, 56-76 years), and 47.0% (52 of 130) were women. Clinically significant diagnostic error or delay was agreed in 23 (17.7%) patients (κ=0.57; 95% CI, 0.40-0.74). Median age was 65.4 years (interquartile range, 60.3-74.8) and 9 of the 23 (30.1%) were female. Of those with diagnostic error or delay, 60.9% (14 of 23) died in the hospital compared with 19.6% (21 of 107) without; P <.001. Diagnostic error or delay was associated with higher Charlson comorbidity index score, cardiac arrest triage score, and do not intubate/do not resuscitate status. Adjusting for age, do not intubate/do not resuscitate status, and Charlson comorbidity index score, diagnostic error or delay was associated with increased mortality; odds ratio, 5.7; 95% CI, 2.0-17.8., Conclusion: Diagnostic errors or delays can be reliably identified and are associated with higher comorbidity burden and increased mortality.

Background: Quantitative studies have demonstrated several factors predictive of readmissions to intensive care. Clinical decision tools, derived from these factors have failed to reduce readmission rates. The purpose of this study was to qualitatively explore the experiences and perceptions of physicians and nurses to gain more insight into intensive care readmissions., Methods: Semi-structured interviews of intensive care unit (ICU) and general medicine care providers explored work routines, understanding and perceptions of the discharge process, and readmissions to intensive care. Participants included ten providers from the ICU setting, including nurses (n = 5), consultant intensivists (n = 2), critical care fellows (n = 3) and 9 providers from the general medical setting, nurses (n = 4), consulting physicians (n = 2) and senior resident physicians (n = 3). Principles of grounded theory were used to analyze the interview transcripts., Results: Nine factors within four broad themes were identified: (1) patient factors - severity-of-illness and undefined goals of care; (2) process factors - communication, transitions of care; (3) provider factors - discharge decision-making, provider experience and comfort level; (4) organizational factors - resource constraints, institutional policies., Conclusions: Severe illness predisposes ICU patients to readmission, especially when goals of care were not adequately addressed. Communication, premature discharge, and other factors, mostly unrelated to the patient were also perceived by physicians and nurses to be associated with readmissions to intensive care. Quality improvement efforts that focus on modifying or improving aspects of non-patient factors may improve outcomes for patients at risk of ICU readmission.

Background A detailed understanding of electronic health record (EHR) workflow patterns and information use is necessary to inform user-centered design of critical care information systems. While developing a longitudinal medical record visualization tool to facilitate electronic chart review (ECR) for medical intensive care unit (MICU) clinicians, we found inadequate research on clinician–EHR interactions. Objective We systematically studied EHR information use and workflow among MICU clinicians to determine the optimal selection and display of core data for a revised EHR interface. Methods We conducted a direct observational study of MICU clinicians performing ECR for unfamiliar patients during their routine daily practice at an academic medical center. Using a customized manual data collection instrument, we unobtrusively recorded the content and sequence of EHR data reviewed by clinicians. Results We performed 32 ECR observations among 24 clinicians. The median (interquartile range [IQR]) chart review duration was 9.2 (7.3–14.7) minutes, with the largest time spent reviewing clinical notes (44.4%), laboratories (13.3%), imaging studies (11.7%), and searching/scrolling (9.4%). Historical vital sign and intake/output data were never viewed in 31% and 59% of observations, respectively. Clinical notes and diagnostic reports were browsed ≥10 years in time for 60% of ECR sessions. Clinicians viewed a median of 7 clinical notes, 2.5 imaging studies, and 1.5 diagnostic studies, typically referencing a select few subtypes. Clinicians browsed a median (IQR) of 26.5 (22.5–37.25) data screens to complete their ECR, demonstrating high variability in navigation patterns and frequent back-and-forth switching between screens. Nonetheless, 47% of ECRs begin with review of clinical notes, which were also the most common navigation destination. Conclusion Electronic chart review centers around the viewing of clinical notes among MICU clinicians. Convoluted workflows and prolonged searching activities indicate room for system improvement. Using study findings, specific design recommendations to enhance usability for critical care information systems are provided., Competing Interests: Conflict of Interest: None.

Background: Electronic Health Record (EHR)-based sepsis alert systems have failed to demonstrate improvements in clinically meaningful endpoints. However, the effect of implementation barriers on the success of new sepsis alert systems is rarely explored., Objective: To test the hypothesis time to severe sepsis alert acknowledgement by critical care clinicians in the ICU setting would be reduced using an EHR-based alert acknowledgement system compared to a text paging-based system., Study Design: In one arm of this simulation study, real alerts for patients in the medical ICU were delivered to critical care clinicians through the EHR. In the other arm, simulated alerts were delivered through text paging. The primary outcome was time to alert acknowledgement. The secondary outcomes were a structured, mixed quantitative/qualitative survey and informal group interview., Results: The alert acknowledgement rate from the severe sepsis alert system was 3% ( N  = 148) and 51% ( N  = 156) from simulated severe sepsis alerts through traditional text paging. Time to alert acknowledgement from the severe sepsis alert system was median 274 min ( N  = 5) and median 2 min ( N  = 80) from text paging. The response rate from the EHR-based alert system was insufficient to compare primary measures. However, secondary measures revealed important barriers., Conclusion: Alert fatigue, interruption, human error, and information overload are barriers to alert and simulation studies in the ICU setting., Competing Interests: AWARE is patent pending (US 2010/0198622, 12/697861, PCT/US2010/022750). Drs. Herasevich, Gajic, and Pickering and Mayo Clinic have a financial conflict of interest relating to licensed technology described in this paper. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest Policies.

Clinical research often requires direct observation of clinicians performing routine tasks, but few effective data collection instruments exist. We describe the development of DOCtimer - a web-based, platform-independent timing and counting application that allows researchers to easily record numerous tracking elements on a single screen, faciltating robust data collection for direct observational research.

Background: The number of electronic health record (EHR)-based notifications continues to rise. One common method to deliver urgent and emergent notifications (alerts) is paging. Despite of wide presence of smartphones, the use of these devices for secure alerting remains a relatively new phenomenon., Methods: We compared three methods of alert delivery (pagers, EHR-based notifications, and smartphones) to determine the best method of urgent alerting in the intensive care unit (ICU) setting. ICU clinicians received randomized automated sepsis alerts: pager, EHR-based notification, or a personal smartphone/tablet device. Time to notification acknowledgement, fatigue measurement, and user preferences (structured survey) were studied., Results: Twenty three clinicians participated over the course of 3 months. A total of 48 randomized sepsis alerts were generated for 46 unique patients. Although all alerts were acknowledged, the primary outcome was confounded by technical failure of alert delivery in the smartphone/tablet arm. Median time to acknowledgment of urgent alerts was shorter by pager (102 mins) than EHR (169 mins). Secondary outcomes of fatigue measurement and user preference did not demonstrate significant differences between these notification delivery study arms., Conclusions: Technical failure of secure smartphone/tablet alert delivery presents a barrier to testing the optimal method of urgent alert delivery in the ICU setting. Results from fatigue evaluation and user preferences for alert delivery methods were similar in all arms. Further investigation is thus necessary to understand human and technical barriers to implementation of commonplace modern technology in the hospital setting.

Objective: Clinical calculators are widely used in modern clinical practice, but are not generally applied to electronic health record (EHR) systems. Important barriers to the application of these clinical calculators into existing EHR systems include the need for real-time calculation, human-calculator interaction, and data source requirements. The objective of this study was to identify, classify, and evaluate the use of available clinical calculators for clinicians in the hospital setting., Methods: Dedicated online resources with medical calculators and providers of aggregated medical information were queried for readily available clinical calculators. Calculators were mapped by clinical categories, mechanism of calculation, and the goal of calculation. Online statistics from selected Internet resources and clinician opinion were used to assess the use of clinical calculators., Results: One hundred seventy-six readily available calculators in 4 categories, 6 primary specialties, and 40 subspecialties were identified. The goals of calculation included prediction, severity, risk estimation, diagnostic, and decision-making aid. A combination of summation logic with cutoffs or rules was the most frequent mechanism of computation. Combined results, online resources, statistics, and clinician opinion identified 13 most utilized calculators., Conclusion: Although not an exhaustive list, a total of 176 validated calculators were identified, classified, and evaluated for usefulness. Most of these calculators are used for adult patients in the critical care or internal medicine settings. Thirteen of 176 clinical calculators were determined to be useful in our institution. All of these calculators have an interface for manual input., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

To identify the routine information needs of inpatient clinicians on the general wards for the development of an electronic dashboard. Survey of internal medicine and subspecialty clinicians from March 2014-July 2014 at Saint Marys Hospital in Rochester, Minnesota. An information needs assessment was generated from all unique data elements extracted from all handoff and rounding tools used by clinicians in our ICUs and general wards. An electronic survey was distributed to 104 inpatient medical providers. 89 unique data elements were identified from currently utilized handoff and rounding instruments. All data elements were present in our multipurpose ICU-based dashboard. 42 of 104 (40 %) surveys were returned. Data elements important (50/89, 56 %) and unimportant (24/89, 27 %) for routine use were identified. No significant differences in data element ranking were observed between supervisory and nonsupervisory roles. The routine information needs of general ward clinicians are a subset of data elements used routinely by ICU clinicians. Our findings suggest an electronic dashboard could be adapted from the critical care setting to the general wards with minimal modification.

Hyperglycemia control is associated with improved outcomes in patients undergoing cardiac surgery. The Surgical Care Improvement Project metric (SCIP-inf-4) was introduced as a performance measure in surgical patients and included hyperglycemia control. Compliance with the SCIP-inf-4 metric remains suboptimal. A novel real-time decision support tool (DST) with guaranteed feedback that is based on the existing electronic medical record system was developed at a tertiary academic center. Implementation of the DST increased the compliance rate with the SCIP-inf-4 from 87.3% to 96.5%. Changes in tested clinical outcomes were not observed with improved metric compliance. This new framework can serve as a backbone for development of quality control processes for other metrics. Further and, ideally, multicenter studies are required to test if implementation of electronic DSTs will translate into improved resource utilization and outcomes for patients., (©2016 American Association of Critical-Care Nurses.)