Barwise, Amelia K

Objectives: The objective of this derivation and validation study was to develop and validate a search strategy algorithm to detect patients who used professional interpreter services. Methods: We identified all adults who had at least one intensive care unit admission during their hospital stay across the Mayo Clinic Enterprise between 1 January 2015 and 30 June 2020. Three random subsets of 100 patients were extracted from 60,268 patients to develop the search strategy algorithm. Two physician reviewers conducted gold standard manual chart review and any discrepancies were resolved by a third reviewer. These results were compared with the search strategy algorithm each time it was refined. Sensitivity and specificity were calculated during each phase by comparing the search strategy results to the reference gold standard for both derivation cohorts and the final validation cohort. Results: The first search strategy resulted in a sensitivity of 100% and a specificity of 89%. The second revised search strategy achieved a sensitivity of 100% and a specificity of 87%. The final version of the search strategy was applied to the validation subset and sensitivity and specificity were 100% and 89%, respectively. Conclusion: We derived and validated a search strategy algorithm to assess interpreter use among hospitalized patients. Using a search strategy algorithm with high sensitivity and specificity can reduce the time required to abstract data from the electronic medical records compared with manual data abstraction.

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.

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 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.

Abstract Lifestyle interventions are pivotal for successful management of type 2 diabetes (T2D), however, the proportion of people with T2D adhering to physical activity advice has not been thoroughly studied. The purpose of this systematic review was to summarise the evidence on adherence to exercise or physical activity components in lifestyle interventions in those with T2D. We searched MEDLINE EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus on 12 November 2019. Eligible studies enrolled adults with T2D and reported the proportion of adherence to lifestyle interventions as a primary or secondary outcome. We included 11 studies (nine randomised controlled trials (RCTs) enrolling 1717 patients and two nonrandomised studies enrolling 62 patients). Only one of the studies had low risk of bias. The proportion of participants adhering to physical activity varied from 32% to 100% with a median of 58%. Adherence was higher in interventions using supervised training and lowest in interventions using remote coaching and the adherence rate in observational studies was higher compared to RCTs (92% vs. 55%; p < 0.01). Study duration, risk of bias, or participants' sex, were not associated with adherence to physical activity. The proportion of those with T2D adhering to physical activity interventions for T2D varies widely and most of the included studies had a high risk of bias. These findings have important implications for planning and power analysis of future trials and when counselling patients about lifestyle interventions including physical activity or exercise components.

Communication regarding serious illness is challenging in most circumstances. Patients with Limited English Proficiency (LEP) have unique language and cultural needs that often require collaboration with a trained medical interpreter, especially when the clinical encounter involves serious illness decision making or elucidation of patient goals, preferences, and values. Although there is mounting evidence to support interpreter/clinician huddles before a serious illness communication encounter, no current initiatives exist to operationalize this evidence. We are currently in the process of developing, evaluating, and implementing a formal interpreter/clinician huddle process to promote high quality care for patients with LEP. Our huddle guide, called the Check-In for Exchange of Clinical and Key Information (CHECK-IN), is designed to facilitate collaboration between an interpreter and clinician during a serious illness encounter by prompting exchange of relevant sociocultural and clinical information between clinicians and interpreters.

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.

Background: Little is known about the impact of socioeconomic status (SES) as a key element of social determinants of health on intensive care unit (ICU) outcomes for adults. Objective: We assessed whether a validated individual SES index termed HOUSES (HOUsing-based SocioEconomic status index) derived from housing features was associated with short-term outcomes of critical illness including ICU mortality, ICU-free days, hospital-free days, and ICU readmission. Methods: We performed a population-based cohort study of adult patients living in Olmsted County, Minnesota, admitted to 7 intensive care units at Mayo Clinic from 2011 to 2014. We compared outcomes between the lowest SES group (HOUSES quartile 1 [Q1]) and the higher SES group (HOUSES Q2-4). We stratified the cohort based on age (<50 years old and ≥50 years old). Results: Among 4134 eligible patients, 3378 (82%) patients had SES successfully measured by the HOUSES index. Baseline characteristics, severity of illness, and reason for ICU admission were similar among the different SES groups as measured by HOUSES except for larger number of intoxications and overdoses in younger patients from the lowest SES. In all adult patients, there were no overall differences in mortality, ICU-free days, hospital-free days, or ICU readmissions in patients with higher SES compared to lower SES. Among older patients (>50 years), those with higher SES (HOUSES Q2-4) compared to those with lower SES (HOUSES Q1) had lower mortality rates (hazard ratio = 0.72; 95% CI: 0.56-0.93; adjusted P = .01), increased ICU-free days (mean 1.08 days; 95% CI: 0.34-1.84; adjusted P = .004), and increased hospital-free days (mean 1.20 days; 95% CI: 0.45-1.96; adjusted P = .002). There were no differences in ICU readmission rates (OR = 0.74; 95% CI: 0.55-1.00; P = .051). Conclusion: Individual-level SES may be an important determinant or predictor of critical care outcomes in older adults. Housing-based socioeconomic status may be a useful tool for enhancing critical care research and practice.