Faculty Bibliography

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.

The purpose of this article was to analyze the concept of "the fighter in the intensive care unit (ICU)" per the scientific literature and the impact this mentality has on care administered in the ICU. A literature review and a concept analysis based on Rodger's evolutionary method were performed to identify surrogate terms, antecedents, attributes, and consequences pertaining to the "fighter" in the ICU. Thirteen articles with a focus on "the fighter" were included in this analysis. There is a strong desire to remain optimistic and maintain high spirits as a coping mechanism in the face of extreme prognostic uncertainty. Themes that emerged from the literature were the need to find inner strength and persist in the face of adversity. The concept of "the fighter in the ICU" can serve as either adaptive or maladaptive coping, depending on the larger clinical picture. Patient experiences in the ICU are fraught with physical and psychological distress. How the patient and family unit cope during this anxiety-provoking time is based on the individual. Maintaining optimism and identifying as a fighter can be healthy ways to adapt to the circumstances. This concept analysis highlights the importance of holistic care and instilling hope particularly as patients may be nearing the end of life.

OBJECTIVES/OBJECTIVE:Medicare Home Health Care (HHC) services are integral to the care of homebound seriously ill older adults requiring ongoing specialized medical care. Although disparities in health outcomes are well documented in inpatient and primary care, disparities experienced by historically marginalized racial and ethnic groups underrepresented in HHC are understudied. This study aimed to examine the relationship between individual characteristics and differences in HHC health outcomes for seriously ill older adults. DESIGN/METHODS:Secondary data analysis, repeated measure. SETTING AND PARTICIPANTS/METHODS:Seriously ill older adults who received HHC in 2016 in the HHC Outcome and Assessment Information Set (OASIS). METHODS:Start of care and discharge data from the 2016 HCC OASIS were used to examine the relationship between individual characteristics and differences in HHC health outcomes identified by the Centers for Medicare and Medicaid Services as key indicators of quality in HHC, including dyspnea, pain frequency, cognitive functioning, and presence of unhealed pressure ulcer stage II or higher. A generalized ordered logit model with partial proportional odds was used for the ordinal categorical outcomes and a logistic regression was used for the binary dependent variable. RESULTS:Findings indicated that of 227,402 seriously ill individuals with an HHC episode in 2016, those from underrepresented racial and ethnic groups had between 14% and 57% higher odds of worse health outcomes compared with non-Hispanic white patients with the exception of pain frequency. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:For people living with serious illness, there are significant differences in Medicare HHC health outcomes when comparing underrepresented racial or ethnic beneficiaries with white counterparts. More research is needed to understand how health care processes such as referral patterns or time to care initiation, and structural factors such as HHC agency quality and neighborhood social deprivation are related to health differences observed in the population.

Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.

OBJECTIVES/OBJECTIVE:To evaluate the impact of a mentoring program to encourage staff-delivered sleep-promoting strategies on sleep, function, depression, and anxiety among skilled nursing facility (SNF) residents. DESIGN/METHODS:Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS/METHODS:Seventy-two residents (mean age 75 ± 15 years; 55.9% female, 41% non-Hispanic White, 35% Black, 20% Hispanic, 3% Asian) of 2 New York City urban SNFs. METHODS:. RESULTS:. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:SNF residents had improvements in sleep quality and depression with intervention, but improvements were not sustained at 3-month follow-up. The COVID-19 pandemic led to premature study termination, so full impacts remain unknown.

OBJECTIVES/OBJECTIVE:To describe the implementation of a mentored staff-delivered sleep program in nursing facilities. DESIGN/METHODS:Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS/METHODS:This program was implemented in 2 New York City nursing facilities, with partial implementation (due to COVID-19) in a third facility. METHODS:Expert mentors provided staff webinars, in-person workshops, and weekly sleep pearls via text messaging. We used the integrated Promoting Action on Research Implementation in Health Services (i-PARiHS) framework as a post hoc approach to describe key elements of the SLUMBER implementation. We measured staff participation in unit-level procedures and noted their commentary during unit workshops. RESULTS:We completed SLUMBER within 5 units across 2 facilities and held 15 leadership meetings before and during program implementation. Sessions on each unit included 3 virtual webinar presentations and 4 in-person workshops for each nursing shift, held over a period of 3 to 4 months. Staff attendance averaged >3 sessions per individual staff member. Approximately 65% of staff present on each unit participated in any given session. Text messaging was useful for engagement, educational reinforcement, and encouraging attendance. We elevated staff as experts in the care of their residents as a strategy for staff engagement and behavior change and solicited challenging cases from staff during workshops to provide strategies to address resident behavior and encourage adoption when successful. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:Engaging staff, leadership, residents, and family of nursing facilities in implementing a multicomponent sleep quality improvement program is feasible for improving nursing facilities' sleep environment. The program required gaining trust at multiple levels through presence and empathy, and reinforcement mechanisms (primarily text messages). To improve scalability, SLUMBER could evolve from an interdisciplinary investigator-based approach to internal coaches in a train-the-trainer model to effectively and sustainably implement this program to improve sleep quality for facility residents.

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.

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.

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.

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.