Gregory Lynn Alexander

This study updated an existing health information technology maturity and staging model for nursing homes to support alignment with value-based care. Using a five-round Delphi process (November 2023–July 2024), 22 national long-term care health information technology experts reviewed and refined a validated model of 183 items across 27 content areas. Consensus-based revisions resulted in a streamlined model with 142 items across 21 content areas organised into four domains: resident care, nursing care, clinical support, and administrative activities. Most items (86%) were assigned to Stage 4 or higher, indicating readiness for bi-directional data exchange and resident-centred data use. The revised model provides an expert-informed, policy-relevant framework to guide nursing home health information technology development and value-based care readiness by 2030.

The 2025 American College of Medical Informatics (ACMI) Symposium, themed Charting the Future of ACMI, provided a forum for fellows to reflect on the 2020-2025 ACMI Strategic Plan and define future priorities. Discussions centered on: (1) mobilizing ACMI’s expertise to guide the responsible use of artificial intelligence (AI) in medical informatics, (2) advancing informatics education and mentorship across career stages, and (3) strengthening local, national, and global interdisciplinary partnerships. Proposed actions include establishing an AI Task Force, developing an online Education Resource Library, expanding structured mentorship, and cultivating strategic partnerships with relevant informatics, engineering, and clinical organizations. Fellows also identified cross-cutting challenges, including financial pressures, competition with alternative convening spaces, and the need to engage emerging leaders. By addressing these challenges while advancing its strategic imperatives, ACMI aims to serve as a trusted compass for the informatics community.

Objective: To clarify the concept of sustainability in the nursing workforce and identify its attributes, antecedents, and potential consequences. Background: While the term “nursing workforce sustainability” has been used to emphasize the critical implications of ensuring the efficiency of healthcare systems worldwide, a delineated understanding of the concept remains ambiguous in extant literature, causing uncertainty about its conceptual underpinnings. Methods: Rodger’s evolutionary approach was applied. Results: The authors identified “nursing workforce sustainability” as an evolving concept with four interrelated attributes: recruitment, retention, regulation, and recognition. The antecedents included demographic, occupational, and structural issues. Consequences include policy and systemic advancements, a diverse and inclusive workforce, enhanced workforce well-being, and improved patient safety and organizational effectiveness. Conclusions: A conceptual definition and framework of nursing workforce sustainability have been developed, offering groundwork for future research and policy development in workforce planning and management.

Background: Breast cancer is the most prevalent and costly cancer. Oral endocrine therapy (OET) improves survival rates and quality of life while reducing recurrence, mortality, morbidity, and medical costs. However, adherence to OET is challenging because OET is prescribed for 5–10 years. Determinants of OET nonadherence (NA) among women aged 65 and older remain poorly characterized. Existing studies are limited, often focusing on small, single‐site samples and focusing on patient‐level rather than multi‐level determinants. Despite the unique needs of older women, research on OET‐NA remains scarce. Objective: This study identified multi‐level determinants of OET‐NA in older women using ecological systems theory and the World Health Organization's five‐dimension model. Methods: A descriptive, correlational secondary data analysis was conducted using the 2019 Surveillance‐Epidemiology‐End‐Results (SEER) Medicare database, which includes more than 9 million cancer cases in the United States. Result: OET‐NA was significantly affected by (a) patient‐related factors of ethnicity (i.e., Black [AOR 1.55; 95% CI 1.34–1.78; p < 0.001]) and psychological issues (i.e., depression [OR 1.40; 95% CI 1.27–1.54; p < 0.001]), (b) socioeconomic‐related factors of marital status (i.e., divorced [OR 1.17; 95% CI 1.04–1.32; p ≤ 0.01]), and lifestyle (i.e., tobacco use [OR 1.41; 95% CI 1.22–1.63; p < 0.001]), (c) therapy‐related factors of switching OET medications (OR 2.72; 95% CI 2.41–3.07; p < 0.001), (d) condition‐related factors of comorbidities (i.e., obesity [OR 1.13; 95% CI 1.03–1.23; p < 0.01]), and (e) characteristics of the healthcare team and health system‐related factors (i.e., group practice type [OR 1.26; 95% CI 1.01–1.56; p < 0.05]). Conclusion: OET‐NA was associated with multi‐level determinants, including being Black, having depression, being divorced, using tobacco, switching OET medications, having obesity, and receiving care in group practices. Identifying these determinants is a critical first step toward developing and testing interventions to improve OET‐NA and enhance survival and quality of life.

Background: Large language models have a huge positive impact on various disciplines, including healthcare. As family caregivers are an essential part of the healthcare system, they need support and can benefit from the technology. However, there is no consensus on reliable and valid measures to evaluate large language models. Objective: This study aims to review the literature on the evaluation measures of large language models for caregivers. Methods: We conducted a scoping review guided by Arksey and O’Malley methodology and the PRISMA-ScR checklist. A literature search on PubMed, EMBASE, CINAHL, and PsycINFO, from 2018 through July 2024, was carried out. An additional rapid review was conducted for the recent literature update from July 2024 through November 2025. Results: All 10 final publications that met the inclusion criteria out of 1812 focused on ChatGPT, whereas three of them also addressed other large language models, such as Google Bard and Bing AI. The most commonly assessed core conceptual components of evaluation measures were accuracy, reliability, readability, and comprehensiveness. Overall, the included studies reported that large language models’ responses were somewhat accurate and reliable and mixed results in readability and comprehensiveness. The final 14 publications from a rapid review offered additional evidence on ChatGPT-centrism. Conclusions: This review provides a comprehensive overview of the measures for evaluating large language models and highlights the need for their improvement using reliable and valid measures. The findings guide the direction of future research and practice to maximize the benefits through continuous quality improvement.

Purpose: Communication breakdowns among healthcare teams contribute substantially to preventable adverse events in nursing homes. Although text messaging platforms are increasingly used to support care coordination, little is known about how these technologies are embedded within care team communication networks. The purpose of this study was to characterize patterns of text-message communication among nursing home care teams and to examine how network structures reflect underlying social system dynamics. Methods: Social network analysis was applied to text messages (n=5,092) linked to (n=585) nursing home-to-hospital resident transfers over 5 years (2015-2020). Message metadata and content were used to construct communication networks and to calculate network measures, including density, centralization (in-degree and out-degree), and reciprocity. Networks were qualitatively classified into communication models based on shared structural characteristics and interpreted using Rogers’ Diffusion of Innovations theory. All analysis and visualization were conducted using Python (3.13.9). Results: Three distinct communication models were identified. The Integrated model exhibited high message volume, high connectivity, and low hierarchy, consistent with mature adoption of text messaging as a general collaboration tool. The Hub-and-Spoke model showed moderate message volume with centralized information flow, reflecting protocol-driven, hierarchical communication. The Siloed model demonstrated high message volume but low role diversity, indicating niche use within specific professional roles rather than facility-wide coordination. Across models, individuals occupying central or bridging positions appeared to function as informal opinion leaders, influencing communication through frequent interaction rather than formal authority. Conclusions: The structure and quality of communication networks shape how information flows, influence is exercised through electronic messaging, and innovations are integrated into care processes. Social network analysis offers a rigorous approach for evaluating implementation and guiding strategies to support effective, team-based communication in nursing home settings. Future work should examine whether increasingly integrated communication networks are associated with improved resident outcomes.

Background and Objectives: The Age-Friendly Health Systems model has emerged as a geriatric care model focusing on the 4Ms: what matters, mobility, mentation, and medications. Representation of the 4Ms in interdisciplinary communication could have implications for outcomes including avoidable nursing home-to-hospital transfers. The objective of this article was to explore the association between the 4Ms found in text messages and avoidable transfer of residents with and without Alzheimer’s Disease and Related Dementias (ADRD). Research Design and Methods: The researchers merged data from two primary datasets: (a) text messages (n = 30,066) between nursing home (NH) healthcare workers from an HIPAA-compliant messaging platform and (b) resident transfer data (n = 3,687) from NHs (n = 16) that participated in a 5-year (2016–2020) Centers for Medicare and Medicaid Services demonstration project. They used natural language processing to extract 4M terms from text messages and fit generalized linear mixed models to estimate avoidable NH-to-hospital transfer, controlling for resident characteristics, NH characteristics, and the 4Ms. Results: Merged data contained 1,031 observations grouped by temporal proximity to the transfer date. Cardiopulmonary resuscitation status, late-stage ADRD, NH bed size, and location were associated with avoidable NH-to-hospital transfer of residents. Text messages containing terms representing mentation and mobility were also associated with avoidable NH-to-hospital transfers. Discussion and Implications: These results suggest that natural language processing can be used to identify components of age-friendly care in unstructured data. Association between resident level and nursing home factors and avoidable transfers should be considered as nursing homes implement strategies to reduce avoidable transfer of residents with ADRD.

Background: Daily preferences are a reflection of how adults wish to have their needs and values addressed, contributing to joy and satisfaction in their daily lives. Clinical settings often regard older adults as a uniform group, neglecting the diversity within this population, which results in a shortfall of person-centered care that overlooks their distinct daily care preferences. At the heart of person-centered care lies the imperative to comprehend and integrate these preferences into the care process. Recognizing and addressing gender differences in older adults is critical to customizing care plans, thereby optimizing quality of life and well-being for individuals. This study addresses the need to understand the diverse daily care preferences of adults, particularly among older populations, who represent a growing demographic with unique needs and interests. Objective: This study aims to identify and analyze the key themes and daily care preferences from unstructured adult text narratives with a focus on uncovering gender-specific variations. Methods: This study used 4350 deidentified, unstructured textual data from MyDirectives (MyDirectives, Inc), an interactive online platform. Advanced topic modeling techniques were used to extract meaningful themes, and gender-specific term frequency and distribution were examined to identify gender differences in these elements. Results: The study sample included 2883 women (mean age 63.02, SD 13.69 years) and 1467 men (mean age 67.07, SD 11.73 years). Our analysis identified six major themes: (1) “entertainment” (12.14%, 528/4350), (2) “music” (10.39%, 452/4350), (3) “personal interests and memories” (38.18%, 1661/4350), (4) “intimate relationships” (14.92%, 649/4350), (5) “natural comforts” (16.18%, 704/4350), and (6) “emotional, cultural, and spiritual foundations” (8.18%, 356/4350). Gender differences were evident: women were more likely to express preferences for “personal interests and memories” (40.7% vs 33.3%), “natural comforts” (18.4% vs 11.9%), and “emotional and spiritual foundations” (9.3% vs 6.1%) than men. Men expressed stronger preferences for “entertainment” (18.1% vs 9.1%) and “music” (16.8% vs 7.2%). Common terms across all participants included “dog,” “love,” “friends,” and “book.” Notably, the study revealed significant gender differences in daily care preferences, especially regarding familial relationships and entertainment choices. Conclusions: The findings underscore the importance of recognizing individual daily care preferences in person-centered care, particularly regarding gender. Understanding these preferences is crucial for improving care quality and patient satisfaction, thereby enhancing the overall quality of life for adults receiving care across our health care system.