Barwise, Amelia K

Introduction Patient dehumanization when caring for critically ill patients in the intensive care unit (ICU) is commonplace. The Get to Know Me board (GTKMB) has been identified as a valuable tool for promoting humanized care and communication in the ICU; however, its implementation is highly variable. This study aimed to identify the barriers and facilitators influencing consistent implementation of the GTKMB in the ICU. Materials and methods We conducted multidisciplinary focus groups via teleconference with ICU clinicians at a large quaternary care academic medical center. A thematic content analysis was performed to identify key themes and concepts related to GTKMB implementation. Results Thirty-eight clinicians participated across six focus groups, including 10 nurses, 7 physicians, 6 advanced practice providers, 5 rehabilitation therapists (allied health clinicians), 1 respiratory therapist and 1 social worker. Major themes related to barriers and facilitators of GTKMB implementation included, (1) identifying appropriate patients and optimal timing for completing the GTKMB, (2) logistical challenges such as access to the board, visibility and unclear responsibility for completion, (3) the importance of clinician role modeling supporting the board completion, and (4) opportunities to improve the design and functionality of the board itself. Conclusion Although clinicians recognize the GTKMB as a meaningful tool to support humanized care in the ICU, inconsistent implementation limits its potential impact. This study highlights several key barriers and facilitators to its use. Future efforts should focus on targeted quality improvement initiatives to enhance consistent completion and integration of the GTKMB into ICU practice.

The intensive care unit (ICU) is a highly disease-focused environment where patients are often perceived primarily through their diagnoses rather than as individuals. The Get-To-Know-Me Board (GTKMB) was introduced as a humanizing tool to highlight patients’ personal attributes and foster person-centered care. Despite being found valuable in supporting humanized caring and fostering communication, completion rates at our institution remained low. Through a stakeholder-driven quality improvement initiative, we tested sequential interventions including reminders and checklists, visual magnets, and educational pamphlets to improve and increase the use of the GTKMB. These measures produced modest gains, with reminders and checklists yielding the most notable improvement. Barriers to using the GKTMB included unclear ownership, timing, and inconsistent integration into workflow. Our experience underscores both the promise and limitations of humanizing interventions in critical care. Sustainable implementation requires multidisciplinary buy-in and integration into daily routines. Whereas humanizing tools such as the GTKMB should be evaluated for their impact on meaningful patient outcomes, such outcomes are not clearly established; therefore, we embark on the necessary first step of a method of implementation.

Abstract The use of antibiotics in the hospice setting is complicated by differing patient goals, elevated side effects, and limited information regarding effectiveness. Here, we describe a case and apply the four-quadrant approach (4QA). This includes analyzing the case through the lenses of (1) medical indications related to improving function and reducing symptoms; (2) patient preferences, which vary among patients from complete avoidance of antibiotics to a therapeutic dosage to treat infection; (3) quality-of-life factors that include managing infectious symptoms versus antibiotic side effects; and (4) contextual features, which include antimicrobial resistance concerns and cost considerations. Antibiotics may be beneficial in some cases and should not be denied to patients enrolled in hospice care. More robust evidence-based information about outcomes is needed to inform discussions at the time of enrollment. Applying the 4QA to a case can help determine the best approach for each individual patient.

Abstract Effective communication in the intensive care unit (ICU) is essential, particularly for patients with non-English language preference, yet timely access to professional interpreters remains limited. While artificial intelligence (AI)-based translation tools have been explored in outpatient and nonacute care settings, studies evaluating their use in acute care, environments such as the ICU remain limited. To address this gap, we developed AI-TransLATE (AI-enhanced Transition to Language-Agnostic Transcultural Engagement), a speech-based translation tool designed for multilingual communication in critical care settings. This study aimed to assess the interpretation quality of AI-TransLATE across four languages—Spanish, Chinese, Arabic, and Turkish—using scripted ICU scenarios. We created ICU communication scripts and recorded bilingual research team members simulating clinical interactions. Two independent bilingual evaluators assessed interpretation quality using a 5-point Likert scale across fluency, adequacy, meaning preservation, and severity of errors. Clarity and cultural appropriateness were also rated. Percentage agreement was used to assess interrater agreement. AI-TransLATE achieved acceptable composite scores (≥16/20) across all languages. Spanish and Turkish performed consistently well; Chinese and Arabic showed variability due to omissions and terminology errors. AI-TransLATE shows promise as a clinical communication tool, but further evaluation in real-world, unscripted ICU settings is needed.

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.