#HealthcareInnovation

citizenportal.ai

Peer mentoring and community networks win funding to boost living donor pipeline

Three prize winners—DonorForward (Columbia), NKDO's Living Link Network, and NYU’s CADENCE—won awards to scale community activation, peer mentorship and navigation programs aimed at increasing living donor referrals and retention.

citizenportal.ai

KidneyConnective proposes a national transplant‑center eligibility database to reduce donor attrition

KidneyConnective presented a proposal to build the nation's first transplant‑center eligibility criteria database and a matching engine that routes patient–donor pairs to centers most likely to list them, aiming to salvage donors lost after rejection at the wrong center.

citizenportal.ai

Clinical pilots target early identification, rural access and older donors to expand living donation

Prize winners included clinical innovations: VCU’s KFRE‑triggered donor identification, Dartmouth’s rural telehealth navigation, and MedStar Georgetown’s pathway to evaluate donors aged 65+, each proposing pilots and metrics for completion and follow‑up.

healthcaretoday.com

Bedfordshire patient has sight saved in pioneering AI-assisted brain surgery

The first use of artificial intelligence to support a neurosurgeon during live surgery has successfully protected the sight of a patient having a brain tumour removed.

dlvr.it

Implementation of the Personalized Automated Reading Delivery System as a Component of Precision Medical Education: Prospective Crossover Study

Background: Precision medical education (PME) is an emerging concept that aims to improve physician education by tailoring curricula specifically to each learner. The basic framework behind PME requires collecting data on each individual learner, identifying gaps in knowledge, and then applying direct educational interventions. However, logistical difficulties in the delivery of such interventions hinder the implementation of PME in existing academic programs. Objective: We developed the Personalized Automated Reading Delivery System (PARDS) software application to support a potential PME intervention, aiming to increase the volume of relevant scientific literature read by resident physicians. The PARDS analyzes surgical case lists for the following day, selects relevant scientific articles according to predetermined keywords, and delivers each article via email to an anesthesiology resident physician. This technology may assist in the application of personalized educational interventions, and we evaluated the #feasibility of implementing the PARDS in a population of medical learners. Methods: From July 2018 to December 2018, a pilot prospective crossover study was performed with N=10 postgraduate year 2 (PGY2) anesthesiology residents in an academic medical center (response rate 10/10, 100%). Residents were randomly assigned to the PARDS intervention group for 2 of the 4 months of the study period. The primary outcomes were organizational viability (evaluated by successful delivery of selected articles by the PARDS, required faculty hours, and total expenses) and acceptability in the study population (assessed by the number of reported weekly reading hours). An analysis using a linear mixed model for repeating measures was performed to compare weekly reading hours between intervention and nonintervention conditions. Results: Total reported faculty time involvement for the study period was 24 hours. The cost estimate for implementation ranged from US $2360 to US $8360. The mean difference of recorded reading hours by residents while using the PARDS vs not using the PARDS for months 1, 2, 3, and 4 of the study was 2.56 (95% CI −0.03 to 5.15; =.053), 0.44 (95% CI −2.15 to 3.03; =.74), 0.99 (95% CI −1.38 to 3.36; =.41), and 3.7 (95% CI 1.34 to 6.08; =.002), respectively. Conclusions: The PARDS was successfully implemented in an academic medical center. Calculated costs indicate that the PARDS is an inexpensive intervention compared to contemporary alternatives. The effect on resident reading requires further research.

www.vinetur.com

Review Finds PEth Strengthens Non-invasive Testing for Alcohol-Related Liver Disease

The paper says objective measures of recent drinking can sharpen diagnosis, improve prognosis estimates, reduce reliance on liver biopsy.

dlvr.it

Agreement Between a Mobile Self-Administered Comprehensive Geriatric Assessment Screening Tool and Geriatrician-Administered Assessment: Cross-Sectional #feasibility Study

Background: Comprehensive geriatric assessment (CGA) is a widely recommended, multidimensional approach for guiding clinical decision-making and management in older adults. However, its implementation remains limited by a shortage of geriatric specialists, the complexity of geriatric care, and the increasing demands of an aging population. Mobile self-administered tools offer a potential strategy to expand access to multidimensional geriatric evaluation, particularly in resource-limited settings. Objective: This study aimed to evaluate the #feasibility and agreement of a mobile self-administered CGA screening tool compared with geriatrician-administered assessments among inpatient and outpatient older adults in Brazil. Methods: This cross-sectional study included 80 older adults recruited from inpatient and outpatient geriatric clinics in Brasília, Brazil. Participants completed a mobile CGA, which incorporated validated instruments covering functional status (Vulnerable Elders Survey–13 [VES-13]), cognition (Cognitive Change Questionnaire–8 [CCQ-8]), depressive symptoms (5-item Geriatric Depression Scale [GDS-5]), nutritional status (Mini Nutritional Assessment [MNA]), frailty (G8 screening tool), social support (Gijón Scale), falls, and vision and hearing. Within 48 hours, a geriatrician independently performed a geriatrician-administered CGA using the same instruments. Agreement between the 2 assessment methods was evaluated using Wilcoxon signed-rank tests, Spearman correlation coefficients, intraclass correlation coefficients (ICCs), and Cohen κ coefficients for categorical variables. Results: Participants had a mean age of 70 (SD 7) years; 58.8% (47/80) were female, and 65% (52/80) had ≤8 years of education. The mean completion time for the self-administered CGA was 18.5 (SD 7.5) minutes. Most participants rated the tool as easy or very easy to use (65/80, 81.3%) and reported satisfaction with the assessment process (n=79, 98.8%). High concordance was observed between the self-administered and geriatrician-administered CGA versions for most domains. Cohen κ coefficients demonstrated strong agreement for falls (κ=0.878;

dlvr.it

A Clinician-Centered Evaluation Framework for Large Language Models in Patient Education: Integrating the Technology Acceptance Model and Medical Condition Regard Scale

Inadequate postcare patient education contributes to preventable readmissions and adverse outcomes that disproportionately affect medically complex, high-need communities. Large language models (LLMs) show promise for generating personalized, plain-language patient education at scale. However, existing LLM evaluation frameworks prioritize technical accuracy over patient accessibility and alignment with health literacy, and few explicitly account for the attitudinal influences that clinician evaluators may introduce into the rating process. In this viewpoint, we introduce an evaluation framework that pairs the Technology Acceptance Model (TAM) with the Medical Condition Regard Scale (MCRS). We call it the TAM-MCRS LLM evaluation framework, a novel clinician-centered approach for comparing which LLMs produce the highest-quality postcare patient education across accuracy, appropriateness, clarity, and completeness. We intend for this framework to be used to evaluate LLM-generated patient education outputs through a 2-arm design that pairs an expert clinician panel with automated assessment methods, allowing for interarm comparison using clinical vignettes while accounting for measured evaluator attitudinal variance. The framework was developed through the National Institutes of Health artificial intelligence (#AI)/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Clinicians Leading Ingenuity IN AI Quality (CLINAQ) fellowship program, in partnership with Ochsner Health and Xavier University of Louisiana. The TAM-MCRS framework integrates two theoretical lenses. TAM maps perceived usefulness onto accuracy and completeness, and perceived ease of use onto clarity and appropriateness. Clinicians rate each output with a TAM-based questionnaire, and we then administer the MCRS as a postscoring attitudinal covariate to see whether their regard for the conditions represented in the vignettes influences those ratings. Together, the 2 lenses are intended to produce evidence that is objective, theoretically grounded, clinically realistic, and disparity-responsive. Implications for clinician informaticists, health system governance, and responsible AI deployment are discussed. This viewpoint reflects the authors’ position and is written for clinician informaticists, health system AI governance leaders, implementation scientists, and investigators evaluating LLM-generated patient education.

dlvr.it

BioPharma (+ Google) Corporate Venture Capital Investment Trends: Who is leading in 2025?

Get instant access to a concise analysis of 22 BioPharma (+ Google) CVCs and their portfolios. In minutes, you’ll see where strategic capital is flowing across Technology and Therapy Areas—and how 2025 trends compare to 2023. What’s inside: Clear mapping of CVC focus by Technology and Therapy Areas Trend shifts vs. 2023 to validate your thesis Actionable insights to spot momentum early How it works: Fill out the form to receive your report immediately. Questions or feedback? [email protected] LucidQuest empowers BioPharma executives and Life Sciences investors with up-to-date analysis. Stay tuned for 2025 deep dives on Cell & Gene Therapy, Digital Health, Oncology & Hematology, and more.

healthcaretoday.com

MHRA partners with Manchester NHS on health innovation sandbox

Promising healthcare innovations will be tested in real NHS settings under the new partnership between MHRA and Manchester University NHS Trust.