Citations

Abstract Honoring a patient’s wishes for end-of-life care can be challenging if the patient loses decision-making capacity and the identified alternate decision-maker will not respect the patient’s wishes. This article discusses how to proceed ethically and legally when the alternate decision-maker and care team disagree about respecting a patient’s end-of-life preferences.

Background Individuals with intellectual and developmental disabilities experience substantial disparities across the cancer care continuum, including delayed diagnosis, limited access to supportive resources, and heightened psychosocial distress. These challenges are intensified in high-acuity settings where care models are not adapted to their developmental and cognitive needs. Methods We describe an interdisciplinary, patient-centered approach to the care of a 34-year-old patient with developmental delay and metastatic adenocarcinoma. Key strategies emphasized education, preparation, and communication tailored to her cognitive abilities. Collaboration with her family informed effective strategies to reduce distress and improve compliance. Results Tailored interventions – including simplified communication, visual supports, and continuity of providers – improved the patient’s coping, reduced distress, and enabled completion of comprehensive oncologic evaluation and treatment. Conclusions This case demonstrates that structured, interdisciplinary, patient-centered approaches to care can mitigate disparities and support equitable, compassionate care for individuals with intellectual and developmental disabilities facing serious illness.

Background. Although solid organ transplant prolongs survival in end-stage disease, recipients face high symptom burden and complex decisions at multiple junctures. Palliative care (PC) may address these needs, but referrals in transplant programs are infrequent, often reactive, and the benefits remain unclear. Methods. We retrospectively studied 12 676 heart, liver, lung, and kidney transplants across 3 Mayo Clinic sites (2018–2024). PC encounters were classified as pretransplant (≤1 y before admission), peritransplant (during hospitalization), or posttransplant (≤1 y after discharge). Results. Only 8.3% engaged PC, with patterns varying by organ and timing. Heart programs demonstrated the highest overall engagement, primarily pre- and peritransplant, whereas lung and kidney referrals occurred more often after discharge. Earlier timing coincided with longer survival, whereas peri- and posttransplant encounters occurred closer to death. In heart transplantation, pre- and peritransplant PC were observed in recipients with lower procedural volume and fewer short-term readmissions. Liver and lung cohorts displayed variable patterns. Kidney referrals occurred infrequently and were concentrated among recipients with higher morbidity. Timing of inpatient consultation showed a strong positive correlation with hospital length of stay, and pretransplant PC coincided with higher rates of goals-of-care discussions and fewer hospital interventions. Adapting screening criteria to focus on high-risk recipients, PC was associated with fewer short-term readmissions for heart and lung recipients. Conclusions. PC among solid organ transplant was infrequent and varied. Referral timing often aligned with patient morbidity and mortality. Structured, prospective validation of screening methods may identify patients most likely to benefit from palliative involvement.

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.

Palliative care (PC) has been shown to improve quality of life (QoL), reduce symptom burden, and align care with patient goals in those with serious illnesses. However, its integration into the emergency department (ED), where many patients with advanced illness present, remains underexplored and inconsistently applied. This narrative review evaluates several recent studies on ED-based palliative care, aiming to clarify whether the early benefits in symptom management and QoL are associated with reduced healthcare utilization and cost. A comprehensive literature search identified five new key studies with significant methodological differences, including variations in patient populations, outcome measures, and study designs. Some studies demonstrated short-term symptom relief, yet only one showed significant improvements in QoL at long-term follow-up. For healthcare utilization, still only one of these new studies reported substantial cost savings, underscoring the need for further research to validate financial benefits. We suggest how future work should be designed to explore both clinical and economic outcomes in tandem to establish a clear value proposition for ED-based palliative care. By addressing this lack of cohesion, we can determine the merit of ED-palliative care integrations with the goal of improving patient care and optimizing resource utilization.