Donna Marie Fick

Introduction: Since many hospitalized older adults have delirium, assessment of baseline cognitive function often requires proxy‐based measures. While the 16‐item Informant Questionnaire on Cognitive Decline in Elderly (IQCODE) specifies cutoff scores for mild cognitive impairment (MCI) and dementia, the 8‐item Informant Interview to Differentiate Aging and Dementia (AD8) provides only a single cutoff for “cognitive impairment,” limiting its usefulness and precision. Our aim was to develop a crosswalk between the IQCODE and AD8 to identify cutoff scores on the AD8 for MCI and dementia. Methods: The IQCODE and AD8 were administered to proxies of hospitalized older adults. We used two methods: equipercentile, which matches percentiles between IQCODE and AD8 score distributions, and item response theory (IRT), which matches estimated latent traits of the two measures, to create a crosswalk of equivalent scores and identify AD8 cutoff scores for MCI and dementia. Results: A total of 264 hospitalized older adults (mean age 79.5 years, 55% women) and their proxies were recruited. Equipercentile and IRT methods yielded a crosswalk linking AD8 scores from 0 to 8 with IQCODE scores ranging from 1.0 to 5.0. An AD8 score of 2, corresponding to an IQCODE score of 3.2, was identified as the cutoff for MCI, while an AD8 score of 4, corresponding to an IQCODE score of 3.5, was identified as the cutoff for dementia. Discussion: Our crosswalk equates AD8 and IQCODE scores and provides cutoff values for MCI and dementia for the AD8, enhancing its clinical utility in acute care. By establishing empirically derived thresholds, this study strengthens comparability across proxy‐based cognitive measures and expands the AD8's usefulness for distinguishing MCI from dementia. In acute settings, where delirium and time constraints limit direct testing, the AD8 offers a brief, reliable option for measuring baseline cognition and supporting early recognition of cognitive decline.

Background: There is growing interest in using digital health technologies to deliver Cognitive Stimulation Therapy (CST) for people with dementia, given the potential to improve access and outcomes. However, evidence for the effectiveness of delivering CST using digital technologies (“digital CST”) remain limited, making it difficult to determine best practices. This study systematically reviews and evaluates the efficacy of digital CST on cognitive and psychosocial outcomes in people with dementia. Methods: We conducted a comprehensive literature search in CINAHL, Cochrane Library, Embase, MEDLINE, PubMed, and Web of Science. We included randomized controlled trials and quasi-experimental studies that examined the effects of digital CST in people with dementia. We assessed methodological quality using the Cochrane Risk of Bias tool (RoB 2) and ROBINS-I. We applied a DerSimonian and Laird random-effects model to estimate pooled standardized mean differences (SMDs). Results: Eighteen studies comprising 756 participants met inclusion criteria. Digital CST significantly improved cognitive function (k = 559; SMD = 0.33; 95% CI: 0.04–0.62; p = 0.03; I ² = 61.39%) and semantic fluency (k = 181; SMD = 0.43; 95% CI: 0.13–0.73; p < 0.001; I ² = 0.00%). It also significantly reduced depressive symptoms (k = 273; SMD = –0.66; 95% CI: –1.12 to –0.20; p = 0.01; I ² = 68.94%). The greatest cognitive improvements were observed when interventions were delivered biweekly in 45-minute sessions over fewer than eight weeks. Conclusions: This systematic review and meta-analysis demonstrates that digital CST is effective in enhancing cognitive performance and supporting aspects of psychosocial functioning in people with dementia. Improvements were observed particularly in global cognition, semantic fluency, and depressive symptoms, indicating that digital CST offers a beneficial non-pharmacological option within dementia care. Further research is warranted to explore its applicability across different dementia stages and to determine the sustainability of these effects over time. Prospero registration: CRD420250653224.

Dementia is a global public health concern, with approximately 55 million individuals worldwide currently living with it. Individual cognitive stimulation therapy (iCST) has been shown to improve quality of life for persons living with dementia (PLwDs). However, providing iCST at scale remains a serious challenge, specifically as it can add considerable burden on care partners. This project focuses on a voice assistant (VA) to support care partners in delivering iCST. We developed a VA prototype and conducted a qualitative study with care partners (N=5). Our preliminary findings show that using a VA to deliver iCST is feasible and acceptable. We have also identified design requirements for the VA to effectively provide iCST, including need for personalization reflecting dementia severity and individual interests, collaboration between care partners and PLwDs, and accessible interactions to minimize frustration and distress. These findings can inform the future design of inclusive and accessible VAs.

We created a comprehensive, four-part, in-person, interactive community education series to teach informal, unpaid care partners of persons living with dementia (PLWD) how to support their PLWD, negotiate common day-to-day challenges, and navigate predictable situations that arise as the disease progresses over time. The purpose of this qualitative study was to explore the series impact on participant knowledge, care practices, and perceptions of caregiving. Inspired by the U.S. Medicare Cognitive Assessment and Care Plan Service visit and the 4Ms of the Age-Friendly Health Systems Framework, our curriculum focused on (1) expanding knowledge about the disease and disease progression, (2) developing skills to better connect and work with PLWD, (3) self-care for care partners, and (4) sharing resources to support future planning. The program was delivered in three communities in Central Pennsylvania (one rural, one small, and one medium metropolitan) and was attended by 146 individuals. Both session and final qualitative evaluation data were assessed using thematic analysis and five major categories emerged: lessons learned, activating new knowledge, impact and changes, assuming responsibility is challenging, and I need more help. Ongoing education, emotional support, and access to quality assistance for long-term planning are all needed and require sustained support.

Background: Generative artificial intelligence (AI) and large language models, such as OpenAI’s ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied. Objective: This exploratory study aims to evaluate and optimize ChatGPT’s capabilities in administering and interpreting the Sour Seven Questionnaire, an informant-based delirium assessment tool. Specifically, the objectives were to train ChatGPT-3.5 and ChatGPT-4 to understand and correctly apply the Sour Seven Questionnaire to clinical vignettes using prompt engineering, assess the performance of these AI models in identifying and scoring delirium symptoms against scores from human experts, and refine and enhance the models’ interpretation and reporting accuracy through iterative prompt optimization. Methods: We used prompt engineering to train ChatGPT-3.5 and ChatGPT-4 models on the Sour Seven Questionnaire, a tool for assessing delirium through caregiver input. Prompt engineering is a methodology used to enhance the AI’s processing of inputs by meticulously structuring the prompts to improve accuracy and consistency in outputs. In this study, prompt engineering involved creating specific, structured commands that guided the AI models in understanding and applying the assessment tool’s criteria accurately to clinical vignettes. This approach also included designing prompts to explicitly instruct the AI on how to format its responses, ensuring they were consistent with clinical documentation standards. Results: Both ChatGPT models demonstrated promising proficiency in applying the Sour Seven Questionnaire to the vignettes, despite initial inconsistencies and errors. Performance notably improved through iterative prompt engineering, enhancing the models’ capacity to detect delirium symptoms and assign scores. Prompt optimizations included adjusting the scoring methodology to accept only definitive “Yes” or “No” responses, revising the evaluation prompt to mandate responses in a tabular format, and guiding the models to adhere to the 2 recommended actions specified in the Sour Seven Questionnaire. Conclusions: Our findings provide preliminary evidence supporting the potential utility of AI models such as ChatGPT in administering standardized clinical assessment tools. The results highlight the significance of context-specific training and prompt engineering in harnessing the full potential of these AI models for health care applications. Despite the encouraging results, broader generalizability and further validation in real-world settings warrant additional research.

Aims: To synthesise the evidence on and to compare the diagnostic accuracy of the Nu‐DESC and CAM in detecting postoperative delirium among hospitalised patients.Design: Systematic review and diagnostic meta‐analysis.Data Sources: The PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, ProQuest Dissertations and Theses A&I, and PsycINFO databases were systematically searched from their inception to February 10, 2023.Results: In total, 10 (n = 1950) and seven (n = 830) reports were included for the Nu‐DESC and CAM, respectively. For Nu‐DESC and CAM, the pooled sensitivities were 0.69 and 0.65, respectively, while the summary specificities were 0.99 for Nu‐DESC and 0.92 for CAM. The pooled specificity differed significantly between the two tools (p < 0.001), despite comparable pooled sensitivities. The duration of stay in the intensive care unit significantly moderated the summary specificity of Nu‐DESC (B = −0.0003, p = 0.009). Regarding CAM, the percentage of female participants showed a positive correlation with its pooled sensitivity (B = 0.005, p = 0.02). Furthermore, studies where clinical specialists served as assessors demonstrated a higher summary sensitivity than those assessed by nurses (0.87 vs. 0.25, p = 0.01).Conclusion: The sensitivities of the Nu‐DESC and CAM for detecting postoperative delirium did not achieve optimal levels. Therefore, developing more accurate tools to detect postoperative delirium by integrating features from related risk factors or incorporating technology‐based algorithms to enhance the screening capability is warranted.Reporting Method: The study has adhered to PRISMA‐DTA guideline.Patient or Public Contribution: No patient or public contribution.Trial Registration: The study protocol has been registered on PROSPERO (CRD42023398961)

Background: Whether cognitive and functional recovery in skilled nursing facilities (SNF) following hospitalization differs by delirium and Alzheimer's disease related dementias (ADRD) has not been examined. Objective: To compare change in cognition and function among short‐stay SNF patients with delirium, ADRD, or both. Design: Retrospective cohort study using claims data from 2011 to 2013. Setting: Centers for Medicare and Medicaid certified SNFs. Participants: A total of 740,838 older adults newly admitted to a short‐stay SNF without prevalent ADRD who had at least two assessments of cognition and function. Measurements: Incident delirium was measured by the Minimum Data Set (MDS) Confusion Assessment Method and ICD‐9 codes, and incident ADRD by ICD‐9 codes and MDS diagnoses. Cognitive improvement was a better or maximum score on the MDS Brief Interview for Mental Status, and functional recovery was a better or maximum score on the MDS Activities of Daily Living Scale. Results: Within 30 days of SNF admission, the rate of cognitive improvement in patients with both delirium/ADRD was half that of patients with neither delirium/ADRD (HR = 0.45, 95% CI:0.43, 0.46). The ADRD‐only and delirium‐only groups also were 43% less likely to have improved cognition or function compared to those with neither delirium/ADRD (HR = 0.57, 95% CI:0.56, 0.58 and HR = 0.57, 95% CI:0.55, 0.60, respectively). Functional improvement was less likely in patients with both delirium/ADRD, as well (HR = 0.85, 95% CI:0.83, 0.87). The ADRD only and delirium only groups were also less likely to improve in function (HR = 0.93, 95% CI:0.92, 0.94 and HR = 0.92, 95% CI:0.90, 0.93, respectively) compared to those with neither delirium/ADRD. Conclusions: Among older adults without dementia admitted to SNF for post‐acute care following hospitalization, a positive screen for delirium and a new diagnosis of ADRD, within 7 days of SNF admission, were both significantly associated with worse cognitive and functional recovery. Patients with both delirium and new ADRD had the worst cognitive and functional recovery.