Citations

Background: Patients with non-English Language Preference are at risk of adverse health outcomes, particularly at end of life and during serious illness. Medical interpreters often feel unprepared to interpret conversations about serious illness. Best practice guidelines recommend a pre-encounter huddle between clinician and interpreter to better prepare both providers. The CHECK-IN (Check in for Exchange of Clinical and Key Information) guide is a simple communication tool designed to facilitate this pre-encounter huddle. We describe the results of a pilot feasibility study of the CHECK-IN guide performed in a simulation environment. The primary objectives of the study were to evaluate the feasibility, acceptability, and preliminary performance of the CHECK-IN guide. Methods: This U.S.-based study is a single-center simulation-based nonblinded randomized pilot feasibility study. Participating clinicians were randomized to usual practice (control) vs. introduction to the CHECK-IN guide (intervention). Clinicians completed pre- and post-session surveys. Participating clinicians were evaluated using the Faculty Observation Rating Scale (FORS), Interpreter Scale (IS), and Interpreter Impact Rating Scale (IIRS). Results: Participants had a highly favorable opinion on the acceptability, appropriateness, and feasibility of the CHECK-IN tool; 91% of participants agreed/strongly agreed on the acceptability of the tool, 91% agreed/strongly agreed on the appropriateness of the tool, and 100% agreed/strongly agreed on the feasibility of the tool. There were no statistical differences between control and intervention groups for the IS, IIRS, and FORS scores. Conclusion: The CHECK-IN guide is a promising tool to guide a pre-encounter huddle between clinician and interpreter, thereby improving interpreter-mediated communication when having conversations about serious illness.

Objective: The specialty of critical care in the United States currently lacks a single, broad, unified database. We conducted a scoping review of existing established national ICU databases, describing national and international patterns of critical care delivery. Data Sources: A systematic literature search was undertaken using MEDLINE, Embase, and Web of Science search engines. Study Selection: Projects describing national critical care delivery (including any subspecialty) published in any language were included. Titles, abstracts, and full-text manuscripts were reviewed in duplicate for inclusion. Data Extraction: National database characteristics were collected, including the number and subspecialty of ICUs, the inaugural year, data entry methodology, the number of episodes of care included, and captured clinical data elements. Data Synthesis: Of 24,003 abstracts screened, 185 manuscripts were eligible for inclusion. Thirty countries were identified as having established national ICU registries: Argentina, Australia/New Zealand, Austria, Belgium, Brazil, Canada, Denmark, Ecuador, Finland, Germany, Iceland, India, Ireland, Italy, Japan, Kenya, Malaysia, Mexico, Nepal, Netherlands, Norway, Pakistan, Paraguay, Spain, Sri Lanka, Sweden, Switzerland, United Kingdom, United States and Uruguay. Data entry commonly incorporates a combination of automated data abstraction from electronic healthcare systems and manual data entry, followed by independent validation. Frequently recorded variables include patient demographics; admission vital signs and laboratory data; comorbidities; admission source and diagnoses; ICU diagnoses, treatments, and complications; illness severity scores; and clinically relevant outcomes including discharge disposition, functional status, lengths of stay, and mortality. Conclusions: Insights and experience gained from the study of mature national ICU registries may be used to guide an equivalent U.S. multidisciplinary program aimed at benchmarking, needs assessment, quality improvement, and research facilitation

Abstract Objectives Effective communication is crucial in healthcare, and for patients with a non-English language preference (NELP), professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available, prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence-based neural network translation tools, namely neural machine translation (NMT) may enable robust interpretation and translation. Materials and Methods We conducted a systematic review (SR) to evaluate the literature on NMT for this purpose. We did a comprehensive search of several databases with guidance from a professional librarian. The search was limited to the year 2000 onwards and English language. Title and abstract screening and full-text review were independently conducted by two reviewers with conflicts resolved by a third reviewer Results 2867 studies were identified with 10 studies included in the final analysis. Among these, six evaluated interpretation in real or simulated clinical settings and four examined translation of discharge materials. Google Translate and ChatGPT were assessed in several studies. Accuracy differed by language, with low-resource languages performing worse. Discussion NMT technologies in healthcare have several advantages including broad language accessibility and potential cost savings for institutions. Despite improved accuracy of these novel tools, due to possible critical errors NMT tools are not yet ready for widespread clinical use. Conclusion Future studies should focus on optimizing evaluation methods as well as how best to integrate these technologies into real-time clinical settings.

SummaryThe goal of this study was to ascertain the effect of patient body size on clinic‐ or patient‐delayed health care during the COVID‐19 pandemic and factors predicting delay. Primary survey data were collected in August 2021 from 3593 individuals living within the communities near Mayo Clinic facilities in Minnesota, Wisconsin, Arizona, and Florida. More people who identified with the higher‐weight BMI categories reported having their care appointments cancelled (62.3%) compared to those who identified with normative weight (38.0%) or underweight (47.7%; p < .001) and reported more frequently delaying their own care (67.9%) relative to those who identified with normative weight (47.0%) or underweight (62.3%; p < .001). Higher‐weight people were more likely than others to report delay was due to not knowing where to get care (UW: 5.6%, NW: 3.5%, HW: 7.0%, pchi‐square < .001), worrying about weight‐based judgement (UW: 2.1%, NW: 2.1%, HW: 4.5%, pchi‐square < .001), and fearing discrimination (UW: 2.9%, NW: 2.4%, HW: 5.5%, pchi‐square < .001). There was no difference across groups in delay due to fear of COVID‐19 exposure. Weight gain was associated with care delay in higher‐weight and normative weight groups. Having experienced more patient‐centred care pre‐COVID was associated with less care delay in the normative weight group, but more care delay in other groups. Higher‐weight people were more likely to have their care delayed by health care providers and to delay care themselves. Patient weight gain and concerns about discrimination and judgement about weight gain may have driven the delay of needed care during COVID‐19.

Abstract Rationale: Ongoing healthcare staffing shortages have prompted new care paradigms that incorporate remote care delivery to support timely detection of patient physiological deterioration through mechanisms other than clinician bedside evaluations. In partnership with institutional Information Technology, our team has developed a unique clinical informatics environment, known as the Advanced Medical Practice (AMP) platform, that incorporates data from multiple sources and platforms and supports remote observation of patients from the electronic intensive care unit(eICU). AMP takes advantage of technologies outside of vendor-supported EHRs, and its advanced user-interface design reduces information overload while incorporating real-time data from novel devices and sensors. We partnered with the hospital practice to co-develop a score within the AMP platform(Clinical Events Detection and Response[CEDAR]) that incorporates heart rate, blood pressure, respiratory rate, and SpO2 to stratify patient risk and generate deterioration alerts for the electronic ICU(eICU) team. This formative evaluation employed an implementation science approach to refine CEDAR and ensure it is acceptable to users and can be feasibly implemented in practice. Methods: The study team conducted this pilot work in the eICU at Mayo Clinic (Rochester, MN). We observed the eICU workflow and documented feedback from eICU staff in fieldnotes. eICU staff were asked to log issues in a tracking file during shifts. Issues were reviewed daily and discussed at multidisciplinary team meetings weekly. Observation and tracking data were coded to constructs in the Consolidated Framework for Implementation Research(CFIR 2.0) framework and mapped to Expert Recommendations for Implementation Change(ERIC) strategies that targeted key determinants. Results: Between 9/23/24 and 10/25/24 we completed 25 days of observations. Most identified issues related to technical/design issues like unclear vital trigger alerts (CFIR: Innovation trialability and design); staff knowledge of CEDAR functionality and attitudes about functionality, complexity, or relative advantage compared to current systems (CFIR: Inner setting access to knowledge and information and relative advantage); and difficulties incorporating CEDAR into workflow(CFIR: Inner setting/compatibility). Implementation strategies mapped to CFIR determinants included promoting adaptability of customized interface features; staging implementation scale-up with iterative improvements; providing local technical assistance; assessing for readiness and identifying facilitators and barriers; and conducting educational outreach visits. Conclusions: Formative evaluation during pilot studies are a critical step in the process of intervention development and testing. User engagement and frameworks from implementation science can identify issues of feasibility and inform strategies to ensure acceptability prior to intervention scale-up in clinical practice and readiness for conducting larger implementation studies.

Abstract Rationale: Effective communication is crucial in healthcare, especially in acute care settings, so that patients/family members can understand treatment options, and share preferences and goals of care. For those with a non-English language preference (NELP), or who have difficulty communicating in English, professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available for all languages or clinical settings prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence (AI), specifically machine translation have enabled more accurate and emotional machine interpretation and translation. Methods: We conducted a systematic review to explore the usability and usefulness of AI-assisted interpretation and translation tools for improving communication between patients and clinicians including verbal and written communication. A comprehensive search of several databases including Ovid MEDLINE(R), Ovid Embase, APA PsycInfo, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus via Elsevier was performed, resulting in 2629 studies. We included studies evaluating tools for direct patient-clinician communication. For verbal communication, we included articles assessing speech interpretation, and for written communication, we focused on discharge instruction translation. Results: A total of 18 studies were included in the final analysis. Among these, 5 assessed the use of speech interpretation and 13 examined text translation of discharge materials. 14 (78%) studies found these novel AI tools beneficial for patient care, whereas 11 (62%) studies highlighted the risks, indicating that these tools can lead to serious mistakes with potential clinical harm. 5 (28%) studies reported participant satisfaction with the use of these tools in their care. Translation and interpretation accuracy varied notably across languages, with lower accuracy scores observed in languages with limited digital resources, such as Haitian Creole and Vietnamese. Conclusion: AI-assisted interpretation and translation in healthcare has several advantages including broader language accessibility than usual resources and potential cost savings for institutions and demonstrates evidence of patient satisfaction. However, these novel tools operate using multiple phases and each stage is prone to errors which may negatively impact patient care and outcomes.. AI-based interpretation and translation show promising potential, however, it is not yet ready for widespread clinical use, so professional language interpreters should be used whenever possible. Future studies should focus on evaluating accuracy as well as how best to integrate these technologies in real-time clinical settings without compromising patient safety and quality of care.

Abstract Objectives This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS). Methods We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between November 25, 2023, and January 15, 2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use. Results A total of 47/143 providers completed the survey. Thirty-eight out of 47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). Thirty-eight out of 47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights into patient care, but almost three-quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47). Conclusion Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.