Citations

Abstract Background and Objectives To assess the efficacy of a local volunteer program, delirium optimization with volunteer engagement (DOVE), in preventing in-hospital delirium. Research Design and Methods Between February 2023 and June 2024, volunteers provided structured interventions to patients at risk for delirium, as identified by the Delirium Risk Assessment (DRA) score. Interventions are humanities-based and included sensory aide assistance, completion of “get to know me” forms, and conversation. Data were retrospectively acquired from electronic health records and compared to a control cohort from another medical unit. Delirium occurrence was captured when the nursing confusion assessment method (CAM) documentation was positive, or a clinician documented delirium diagnoses. Cohorts were matched 1:2 (DOVE vs Control) by age, gender, DRA score, month/year of admission, and length of hospital stay. Results A total of 168 patients received interventions by DOVE volunteers, of which 139 were matched with 261 controls. In-hospital delirium occurrence was lower in the DOVE cohort compared to the control cohort (46.8% vs 56.7%; p = .015). Conditional logistic regressions demonstrated a lower odds ratio (OR) for delirium occurrence, 0.55 (95% confidence interval [CI]; 0.34–0.89; p = .015) associated with DOVE intervention. Subgroup analyses of delirium occurrence among patients with DRA score of 3–4 demonstrated an OR of 0.56 (95% CI; 0.31–1.01; p = .053) between the DOVE and control cohorts. Discussion and Implications In-hospital delirium occurrence was ∼10% lower among DOVE cohort patients. This humanities-based intervention offers a feasible strategy, particularly during times of staffing shortages, and should be considered for broader implementation across healthcare institutions.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.

Abstract Objective: Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network. Methods: We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit. Results: The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility. Conclusion: This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Abstract With global aging, assessing functional status is vital for precision medicine. Electronic Health Records (EHRs), particularly unstructured data, hold abundant information on patient mobility. This study explores using Large Language Models (LLMs) to extract and standardize mobility status from unstructured EHR data (i.e., clinical notes). We annotated 600 clinical notes from three health care institutions located in southeastern Minnesota and west-central Wisconsin, focusing on expressions of mobility and associated impairment. Leveraging the open-source Llama 3 model, we tested various prompting strategies—including zero-shot, few-shot, and task decomposition—and evaluated their performance. Error analysis showed that while the model sometimes inferred impairments without explicit evidence, most errors were clinically reasonable, often reflecting borderline or ambiguous cases. While considering reasonable inference as correct, at the patient-level, Mobility Extraction achieves a micro-average accuracy of 0.952 with an F1-score of 0.962, and Impairment Classification produces a micro-average accuracy of 0.912 and an F1-score of 0.948. A local, deterministic setup improved trustworthiness by ensuring consistent outputs, safeguarding privacy, and demonstrating cross-institution generalizability. These findings highlight the feasibility of LLM-based solutions for extracting mobility functional status from unstructured EHR data, supporting both clinical applications and research.

Importance The role and safety of transcranial electrical stimulation (tES) for treating depressive disorders remain under evaluation. Objective To evaluate tES treatment in patients with major depressive disorder (MDD) and comorbid depressive conditions. Data Sources A search of MEDLINE, Embase, Cochrane, APA PsycINFO, and Scopus databases was conducted from inception to September 17, 2024. Study Selection Randomized clinical trials (RCTs) of adults with MDD, depression with psychiatric comorbidities (DPC), or depression with medical comorbidities (DMC), treated with transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), or transcranial random noise stimulation (tRNS), compared with sham or other treatments were included. Data Extraction and Synthesis Independent reviewers extracted data in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, with random-effects meta-analysis used for pooling. Main Outcomes and Measures Primary outcomes were depression severity, response and remission rates, and adverse events. Standardized mean differences (SMDs) were reported for continuous outcomes, and odds ratios (ORs) were reported for categorical outcomes. Quality of evidence (QOE) was assessed using the Grading of Recommendations Assessment, Development, and Evaluation criteria. Results The meta-analysis included 5522 participants from 114 study groups from 88 RCTs (3198 female [58.9%]; mean [range] age, 43.1 [19.4-76.9] years). Most studies (104 study groups from 79 RCTs [91.2%]) evaluated tDCS, while 7 study groups from 6 RCTs (6.1%) evaluated tACS, and 3 study groups from 3 RCTs (2.7%) evaluated tRNS. tES was associated with reduced depressive symptoms (SMD = −0.59; 95% CI, −0.83 to −0.35; low QOE) and improvement in DMC (SMD = −1.05; 95% CI, −1.67 to −0.43; low QOE) and DPC (SMD = −0.78; 95% CI, −1.27 to −0.29; low QOE) compared with MDD (SMD = −0.22; 95% CI, −0.44 to 0.01; low QOE). tDCS was associated with reduced depression in DMC (SMD = −1.05; 95% CI, −1.70 to −0.40; very low QOE) and DPC (SMD = −0.88; 95% CI, −1.40 to −0.36; low QOE) but not MDD. tACS was associated with improved MDD symptoms (SMD = −0.58; 95% CI, −0.96 to −0.20; high QOE) and response rates (OR, 2.07; 95% CI, 1.34 to 3.19; high QOE). Combined tDCS and medication was associated with reduced symptoms (SMD = −0.51; 95% CI, −0.90 to −0.13; moderate QOE) and increased response (OR, 2.25; 95% CI, 1.08 to 4.65; high QOE) in MDD. tDCS combined with psychotherapy was not associated with improvement. Subgroup analysis showed that anodal left dorsolateral prefrontal cortex DCS was associated with improved outcomes. Mild to moderate adverse events were more frequent in tES groups. Conclusions and Relevance In this systematic review and meta-analysis, tDCS was associated with improvement in depression among patients with DMC and DPC (with smaller benefits in MDD), tACS was associated with improved MDD outcomes (while tRNS had insufficient evidence) in smaller samples, and combined tDCS and medication was associated with improvement in depression. These findings suggest that tES is well-tolerated overall, with only mild to moderate adverse events, and that future research should optimize stimulation parameters and individualize tES interventions.

Background Family participation in the delivery of nonpharmacological measures has shown in past studies to prevent 17-75% of incident delirium. A scalable and sustainable method to partner with family (i.e., care partners) and support their delivery of nonpharmacologic measures is needed. Objectives To test the feasibility, acceptability, and usability of iPREPARED in a pilot randomized controlled clinical trial (RCT). iPREPARED is a mobile health technology co-designed with patients and care partners containing resources on nonpharmacological interventions to prevent delirium. Methods Hospitalized adults ( > 65 years) with > 1 delirium risk factor and a care partner (family or friend > 18yo) willing to participate were enrolled. The primary outcomes were feasibility, acceptability, and usability. Descriptive statistics are reported. Results In total, 23 dyads completed the study (median age 72 years (IQR 64, 79), 60% male, and 100% Caucasian). Dyads reported that iPREPARED was acceptable, with moderate usability. Recruitment in the emergency department was feasible whereas recruitment once hospitalized and admitted was more difficult. Conclusion The study design was feasible. iPREPARED was acceptable and usable as an intervention.

Abstract Electroconvulsive therapy (ECT) is an effective treatment for severe depression, especially in treatment-resistant cases. However, its potential cognitive side effects necessitate careful dosing to balance therapeutic benefits and cognitive stability. Recent advances in electric field (E-field) modeling offer promising avenues to optimize ECT dosing. This review synthesizes current knowledge on E-field modeling in ECT and explores its clinical applications. It examines the variability in E-field strengths and distributions induced by ECT and their impact on clinical outcomes. Additionally, the relationship between E-field strengths, neuroplasticity, and therapeutic efficacy is discussed. Translational studies of E-field-informed ECT are highlighted, emphasizing individualized optimal amplitude dosing and potential clinical applications. This review provides useful insights into how E-field modeling can improve the effectiveness of ECT while minimizing adverse effects, helping guide future research and clinical practice.

Introduction Optimal hospital bed utilization requires innovative patient care models. We studied a novel hospitalist model utilizing telemedicine to facilitate collaboration with affiliated emergency departments (EDs) and support medical triage and care of ED patients with high likelihood of hospital admission. Methods Telehospitalists based at a tertiary care facility collaborated with four community EDs in the same healthcare network between January 1, 2022, and April 30, 2023. Telehospitalists supported ED clinicians in medical care decisions and facilitated patient disposition. Emergency department length of stay (LOS) and disposition were evaluated, as were hospital LOS, 30-day readmission, and in-hospital mortality. For patients discharged from the ED, 7-day ED readmission and subsequent hospitalization were evaluated. Results Telehospitalists discussed 550 “admit-likely” patients with ED clinicians: 105 patients (19.1%) discharged from the ED and avoided admission; 322 patients (58.5%) were admitted to local or nearby community hospitals; 123 patients (22.4%) transferred to the tertiary care facility. Emergency department LOS differed significantly among disposition groups, including patients discharged home (10.2 h), admitted to local hospitals (12.6 h), and transferred to tertiary care hospitalist services (14.9 h; p < 0.001). Hospital LOS and in-hospital mortality were not significantly different among disposition groups. Patients admitted locally had lower 30-day readmission compared to those transferred to tertiary care facility (odds ratio = 0.59 [0.36, 0.99], p = 0.048). Discussion Telehospitalists as triage clinicians is an innovative approach to support local ED clinicians and patients. Telehospitalists optimized hospital bed utilization and healthcare system resources by facilitating safe discharges to home and expediting tertiary care transfers when necessary.

Abstract This is an exciting time in Geriatrics, with numerous opportunities in health care for Geriatrics specialists to innovate and lead. Geriatrics specialists should know how to implement innovative care models to lead healthcare changes in their organizations and effectively facilitate change management. We highlight a 10‐step framework that Geriatrics specialists can leverage to quickly move their ideas from development to implementation at a system level. This framework adapts concepts from business management to provide a step‐by‐step guide to move from idea generation to implementation. We provide different practical examples that a Geriatrician can correlate to in their practice, including value proposition and business canvas model. Though small components of the business canvas model may vary based on organization and program/idea‐specific needs, the outlined skills will establish Geriatrics specialists as leaders of change, a resource for education, and valued consultants to a health system that is in dire need of direction to improve the quality of care, and health outcomes, for older adults.