Herasevich, Svetlana

Abstract Background Intensive care unit (ICU) clinicians encounter frequent challenges with managing vast amounts of fragmented data while caring for multiple critically ill patients simultaneously. This may lead to increased provider cognitive load that may jeopardize patient safety. Objectives This systematic review assesses the impact of centralized multipatient dashboards on ICU clinician performance, perceptions regarding the use of these tools, and patient outcomes. Methods A literature search was conducted on February 9, 2023, using the EBSCO CINAHL, Cochrane Central Register of Controlled Trials, Embase, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Eligible studies that included ICU clinicians as participants and tested the effect of dashboards designed for use by multiple users to manage multiple patients on user performance and/or satisfaction compared with the standard practice. We narratively synthesized eligible studies following the SWiM (Synthesis Without Meta-analysis) guidelines. Studies were grouped based on dashboard type and outcomes assessed. Results The search yielded a total of 2,407 studies. Five studies met inclusion criteria and were included. Among these, three studies evaluated interactive displays in the ICU, one study assessed two dashboards in the pediatric ICU (PICU), and one study examined centralized monitor in the PICU. Most studies reported several positive outcomes, including reductions in data gathering time before rounds, a decrease in misrepresentations during multidisciplinary rounds, improved daily documentation compliance, faster decision-making, and user satisfaction. One study did not report any significant association. Conclusion The multipatient dashboards were associated with improved ICU clinician performance and were positively perceived in most of the included studies. The risk of bias was high, and the certainty of evidence was very low, due to inconsistencies, imprecision, indirectness in the outcome measure, and methodological limitations. Designing and evaluating multipatient tools using robust research methodologies is an important focus for future research.

Abstract Background The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences.

Objective: Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment—ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting. Methods: The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction. Results: Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients. Conclusions: This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use. Recommendations: We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Abstract Rationale Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (<24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870–0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828–0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782–0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831–0.914). Conclusions A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.

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.

OBJECTIVES: 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.