Dong, Yue

Objectives Diagnostic error and delay is a prevalent and impactful problem. This study was part of a mixed-methods approach to understand the organizational, clinician, and patient factors contributing to diagnostic error and delay among acutely ill patients within a health system, as well as recommendations for the development of tailored, targeted, feasible, and effective interventions. Methods We did a multisite qualitative study using focus group methodology to explore the perspectives of key clinician stakeholders. We used a conceptual framework that characterized diagnostic error and delay as occurring within 1 of 3 stages of the patient’s diagnostic journey—critical information gathering, synthesis of key information, and decision making and communication. We developed our moderator guide based on the sociotechnical frameworks previously described by Holden and Singh for understanding noncognitive factors that lead to diagnostic error and delay. Deidentified focus group transcripts were coded in triplicate and to consensus over a series of meetings. A final coded data set was then uploaded into NVivo software. The data were then analyzed to generate overarching themes and categories. Results We recruited a total of 64 participants across 4 sites from emergency departments, hospital floor, and intensive care unit settings into 11 focus groups. Clinicians perceive that diverse organizational, communication and coordination, individual clinician, and patient factors interact to impede the process of making timely and accurate diagnoses. Conclusions This study highlights the complex sociotechnical system within which individual clinicians operate and the contributions of systems, processes, and institutional factors to diagnostic error and delay.

OBJECTIVES: To determine whether the “Checklist for Early Recognition and Treatment of Acute Illness and Injury” decision support tool during ICU admission and rounding is associated with improvements in nonadherence to evidence-based daily care processes and outcomes in variably resourced ICUs. DESIGN, SETTINGS, PATIENTS: This before-after study was performed in 34 ICUs (15 countries) from 2013 to 2017. Data were collected for 3 months before and 6 months after Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation. INTERVENTIONS: Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation using remote simulation training. MEASUREMENTS AND MAIN RESULTS: The coprimary outcomes, modified from the original protocol before data analysis, were nonadherence to 10 basic care processes and ICU and hospital length of stay. There were 1,447 patients in the preimplementation phase and 2,809 patients in the postimplementation phase. After adjusting for center effect, Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation was associated with reduced nonadherence to care processes (adjusted incidence rate ratio [95% CI]): deep vein thrombosis prophylaxis (0.74 [0.68–0.81), peptic ulcer prophylaxis (0.46 [0.38–0.57]), spontaneous breathing trial (0.81 [0.76–0.86]), family conferences (0.86 [0.81–0.92]), and daily assessment for the need of central venous catheters (0.85 [0.81–0.90]), urinary catheters (0.84 [0.80–0.88]), antimicrobials (0.66 [0.62–0.71]), and sedation (0.62 [0.57–0.67]). Analyses adjusted for baseline characteristics showed associations of Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation with decreased ICU length of stay (adjusted ratio of geometric means [95% CI]) 0.86 [0.80–0.92]), hospital length of stay (0.92 [0.85–0.97]), and hospital mortality (adjusted odds ratio [95% CI], 0.81 (0.69–0.95). CONCLUSIONS: A quality-improvement intervention with remote simulation training to implement a decision support tool was associated with decreased nonadherence to daily care processes, shorter length of stay, and decreased mortality.

AbstractObjective 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.

Objectives: 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.

Abstract 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.

ABSTRACT Background Implementation of checklists has been shown to be effective in improving patient safety. This study aims to evaluate the effectiveness of implementation of a checklist for daily care processes into clinical practice of pediatric intensive care units (PICUs) with limited resources. Methods Prospective before–after study in eight PICUs from China, Congo, Croatia, Fiji, and India after implementation of a daily checklist into the ICU rounds. Results Seven hundred and thirty-five patients from eight centers were enrolled between 2015 and 2017. Baseline stage had 292 patients and post-implementation 443. The ICU length of stay post-implementation decreased significantly [9.4 (4–15.5) vs. 7.3 (3.4–13.4) days, p = 0.01], with a nominal improvement in the hospital length of stay [15.4 (8.4–25) vs. 12.6 (7.5–24.4) days, p = 0.055]. The hospital mortality and ICU mortality between baseline group and post-implementation group did not show a significant difference, 14.4% vs. 11.3%; p = 0.22 for each. There was a variable impact of checklist implementation on adherence to various processes of care recommendations. A decreased exposure in days was noticed for; mechanical ventilation from 42.6% to 33.8%, p < 0.01; central line from 31.3% to 25.3%, p < 0.01; and urinary catheter from 30.6% to 24.4%, p < 0.01. Although there was an increased utilization of antimicrobials (89.9–93.2%, p < 0.01). Conclusions Checklists for the treatment of acute illness and injury in the PICU setting marginally impacted the outcome and processes of care. The intervention led to increasing adherence with guidelines in multiple ICU processes and led to decreased length of stay.