Faculty Bibliography

INTRODUCTION/BACKGROUND:This study aimed to assess emergency nurses' perceived barriers toward engaging patients in serious illness conversations. METHODS:Using a mixed-method (quant + QUAL) convergent design, we pooled data on the emergency nurses who underwent the End-of-Life Nursing Education Consortium training across 33 emergency departments. Data were extracted from the End-of-Life Nursing Education Consortium post-training questionnaire, comprising a 5-item survey and 1 open-ended question. Our quantitative analysis employed a cross-sectional design to assess the proportion of emergency nurses who report that they will encounter barriers in engaging seriously ill patients in serious illness conversations in the emergency department. Our qualitative analysis used conceptual content analysis to generate themes and meaning units of the perceived barriers and possible solutions toward having serious illness conversations in the emergency department. RESULTS:A total of 2176 emergency nurses responded to the survey. Results from the quantitative analysis showed that 1473 (67.7%) emergency nurses reported that they will encounter barriers while engaging in serious illness conversations. Three thematic barriers-human factors, time constraints, and challenges in the emergency department work environment-emerged from the content analysis. Some of the subthemes included the perceived difficulty of serious illness conversations, delay in daily throughput, and lack of privacy in the emergency department. The potential solutions extracted included the need for continued training, the provision of dedicated emergency nurses to handle serious illness conversations, and the creation of dedicated spaces for serious illness conversations. DISCUSSION/CONCLUSIONS:Emergency nurses may encounter barriers while engaging in serious illness conversations. Institutional-level policies may be required in creating a palliative care-friendly emergency department work environment.

PURPOSE/UNASSIGNED:Utilizing a participatory approach, we sought to co-design a 12-week Green Activity Program (GAP) with Hispanic/Latino individuals living with memory challenges and their care partners, local outdoor professionals, and healthcare providers. METHODS/UNASSIGNED:Participants were recruited via convenience and snowball sampling in the Bronx, New York with Hispanic/Latino persons living with memory challenges and care partners, outdoor activity professionals, and interdisciplinary healthcare providers/dementia experts. Co-design occurred iteratively with 5 focus groups and 4 individual interviews lasting 30-90 min and focused on program and research design. Sessions were recorded and transcribed. Utilizing directed content analysis data was coded using a priori codes program design and research design. RESULTS/UNASSIGNED:were identified including community-based organizations and primary care. CONCLUSION/UNASSIGNED:Co-design was a successful form of engagement for people living with memory challenges that enabled participants to help design key elements of the GAP and research design. Our processes, findings, and recommendations for tailoring co-design to engage Hispanic/Latino people living with memory challenges can inform the development of other programs for this population.

BACKGROUND AND OBJECTIVES/OBJECTIVE:The coronavirus disease 2019 (COVID-19) pandemic severely disrupted hospice care, yet there is little research regarding how widespread disruptions affected clinician and family decision-making. We aimed to understand how the pandemic affected structures, processes, and outcomes of end-of-life care. RESEARCH DESIGN AND METHODS/METHODS:Retrospective narrative chart review of electronic health records of 61 patients referred and admitted to hospice from 3 New York City geriatrics practices who died between March 1, 2020, and March 31, 2021. We linked longitudinal, unstructured medical, and hospice electronic health record notes to create a real-time, multiperspective trajectory of patients' interactions with providers using directed content analysis. RESULTS:Most patients had dementia and were enrolled in hospice for 11 days. Care processes were shaped by structural factors (staffing, supplies, and governmental/institutional policies), and outcomes were prioritized by care teams and families (protecting safety, maintaining high-touch care, honoring patient values, and supporting patients emotionally and spiritually). Processes used to achieve these outcomes were decision-making, care delivery, supporting a "good death," and emotional and spiritual support. DISCUSSION AND IMPLICATIONS/CONCLUSIONS:Care processes were negotiated throughout the end of life, with clinicians and families making in-the-moment decisions. Some adaptations were effective but also placed extraordinary pressure on paid and family caregivers. Healthcare teams' and families' goals to meet patients' end-of-life priorities can be supported by ongoing assessment of patient goals and process changes needed to support them, stronger structural supports for paid and family caregivers, incentivizing relationships across primary care and hospice teams, and extending social work and spiritual care.

CONTEXT/BACKGROUND:The critical role of hospice and palliative care in response to the COVID-19 pandemic is well recognized, but there is limited evidence to guide healthcare leadership through future crises. OBJECTIVES/OBJECTIVE:Our goal was to support future organizational resilience by exploring hospice and palliative team members' perspectives on crisis leadership during the COVID-19 pandemic in New York City (NYC). METHODS:This qualitative descriptive study used individual, semi-structured interviews of purposively sampled interdisciplinary team members. Enrollment sites were two large NYC metro hospice care organizations and one outpatient palliative care practice. We asked participants to complete a demographic form and a 45-60 minute interview. We used descriptive statistics and thematic analysis, respectively, for data analysis. We triangulated the data by presenting preliminary study findings to a group of clinicians (n=21) from one of the referring organizations. RESULTS:Participants (n=30) were professionally diverse (e.g., nurses, physicians, social workers, chaplains, administrators), experienced (mean=17 years; 10 years in hospice), and highly educated (83% ≥ master's degree). About half (n=15) self-identified as white, non-Hispanic, and nearly half (n=13) self-identified as being from a racial/ethnic minoritized group. Two (n=2) did not wish to self-identify. We identified four themes that reflected challenges and adaptive responses to providing care during a crisis: Stay Open and Stay Safe; Act Flexibly; Lead Adaptively; and Create a Culture of Solidarity. CONCLUSION/CONCLUSIONS:While additional work is indicated, findings offer direction for a crisis management playbook to guide leadership in hospice, palliative care, and other healthcare settings in future crises.

IMPORTANCE/UNASSIGNED:Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. OBJECTIVE/UNASSIGNED:Evaluate ChatGPT and LlaMA-2 performance in extracting MMSE and CDR scores, including their associated dates. METHODS/UNASSIGNED:Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. RESULTS/UNASSIGNED:For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. CONCLUSIONS/UNASSIGNED:In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

BACKGROUND/UNASSIGNED:Patients with head and neck cancer (HNC) often experience high symptom burden leading to lower quality of life (QoL). OBJECTIVE/UNASSIGNED:This study aims to conceptually model optimal cutpoint by examining where total number of patient-reported symptoms exceeds patients' coping capacity, leading to a decline in QoL in patients with HNC. METHODS/UNASSIGNED:Secondary data analysis of 105 individuals with HNC enrolled in a clinical usefulness study of the NYU Electronic Patient Visit Assessment (ePVA)©, a digital patient-reported symptom measure. Patients completed ePVA and European Organization for Research and Treatment of Cancer (EORTC©) QLQ-C30 v3.0. The total number of patient-reported symptoms was the sum of symptoms as identified by the ePVA questionnaire. Analysis of variance (ANOVA) was used to define optimal cutpoint. RESULTS/UNASSIGNED:<.0001). CONCLUSIONS/UNASSIGNED:In HNC, defining optimal cutpoints in the total number of patient-reported symptoms is feasible. IMPLICATIONS FOR PRACTICE/UNASSIGNED:Cutpoints in the total number of patient-reported symptoms may identify patients experiencing a high symptom burden from HNC. FOUNDATIONAL/UNASSIGNED:Using optimal cutpoints of the total number of patient-reported symptoms may help effectively align clinical resources with patients' symptom burden.

BACKGROUND:Medicare-funded home healthcare (HHC) delivers skilled nursing, therapy, and related services through visits to the patient's home. Nearly one-third (31%) of HHC patients have diagnosed dementia, but little is currently known regarding how HHC utilization and care delivery differ for persons living with dementia (PLwD). METHODS:We drew on linked 2012-2018 Health and Retirement Study and Medicare claims for a national cohort of 1 940 community-living older adults. We described differences in HHC admission, length of stay, and referral source by patient dementia status and used weighted, multivariable logistic and negative binomial models to estimate the relationship between dementia and HHC visit type and intensity while adjusting for sociodemographic characteristics, health and functional status, and geographic/community factors. RESULTS:PLwD had twice the odds of using HHC during a 2-year observation period, compared to those without dementia (odds ratio [OR]: 2.03; p < .001). They were more likely to be referred to HHC without a preceding hospitalization (49.4% vs 32.1%; p < .001) and incurred a greater number of HHC episodes (1.4 vs 1.0; p < .001) and a longer median HHC length of stay (55.8 days vs 40.0 days; p < .001). Among post-acute HHC patients, PLwD had twice the odds of receiving social work services (unadjusted odds ratio [aOR]: 2.15; p = .008) and 3 times the odds of receiving speech-language pathology services (aOR: 2.92; p = .002). CONCLUSIONS:Findings highlight HHC's importance as a care setting for community-living PLwD and indicate the need to identify care delivery patterns associated with positive outcomes for PLwD and design tailored HHC clinical pathways for this patient subpopulation.

BACKGROUND:the perspectives of older adults undergoing the TAVR process have never been synthesized. OBJECTIVES/OBJECTIVE:To contextualize the perspectives and experiences of older adults undergoing TAVR. METHODS:Four electronic databases were searched in April 2023. Articles were included if a qualitative methodology was used to assess the perceptions of older adults (>65 years old) undergoing or recovering from TAVR. RESULTS:Out of 4619 articles screened, 12 articles met the criteria, representing 353 individuals from 10 countries. Relevant themes included the need for an individualized care plan, caregiver and family support, communication and education, persistent psychosocial and physical symptoms, and the unique recovery journey. CONCLUSION/CONCLUSIONS:Older adults with AS undergoing TAVR generally perceive their procedure positively. Improved interdisciplinary and holistic management, open communication, symptom assessment, support, and education is needed.

Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.