Preventable vitamin D deficiency (VDD) is a global health concern. The prevention, early detection, and treatment of vitamin D deficiency aligning with serum 25-hydroxyvitamin D concentration recommendations of 40–60 ng/mL (100–150 nmol/L), provided by an international panel of 48 vitamin D researchers, would result in significant health benefits and cost savings to individuals and society. However, research shows that healthcare professionals lack knowledge and confidence in best practices with respect to vitamin D. A vitamin D toolkit was developed that included a model for decision-making support, e-tools, and accompanying resources and was implemented using an online, asynchronous learning management system. This pre-test, post-test, and follow-up survey study design aimed to increase nurses’ and dietitians’ levels of knowledge and confidence regarding vitamin D, aid in their translation of evidence into spheres of practice and influence, and help them identify translation barriers. The completion of the toolkit increased the participants’ (n = 119) knowledge from 31% to 65% (p < 0.001) and their confidence from 2.0 to 3.3 (p < 0.001) on a scale of 1–5. Respondents reported using the model (100%) as a framework to successfully guide the translation of vitamin D knowledge into their sphere of influence or practice (94%) and identifying translation barriers. The toolkit should be included in interdisciplinary continuing education, research/quality improvement initiatives, healthcare policy, and institutions of higher learning to increase the movement of research into practice.

Patients with chronic renal failure (CRF) are at high risk of being readmitted to hospitals within 30 days. Routinely collected electronic health record (EHR) data may enable hospitals to predict CRF readmission and target interventions to increase quality and reduce readmissions. We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center. Categorizing three routinely collected variables (creatinine, B-type natriuretic peptide, and length of stay) increased readmission prediction by 30% compared with paper-based methods as measured by C-statistic (AUC). Marginal effects analysis using the final multivariate model provided patient-specific risk scores from 0% to 44.3%. These findings support the use of routinely collected EHR data for effectively stratifying readmission risk for patients with CRF. Generic readmission risk tools may be evidence-based but are designed for general populations and may not account for unique traits of specific patient populations—such as those with CRF. Routinely collected EHR data are a rapid, more efficient strategy for risk stratifying and strategically targeting care. Earlier risk stratification and reallocation of clinician effort may reduce readmissions. Testing this risk model in additional populations and settings is warranted.

This project piloted an educational intervention focused on use and management of EHR data by Doctor of Nursing Practice students in quality improvement initiatives. Recommendations from academic and clinical nursing promote the integration of EHR data findings into practice. Nursing's general lack of understanding about how to use and manage data is a barrier to using EHR data to guide quality improvement initiatives. Doctor of Nursing Practice students at a hospital-affiliated university participated in a pre-test, training, and post-test through an online learning management system. Training content and assessments focused on data and planning for its use in quality improvement initiatives. Sixteen students experienced a median of 17.6% increase in scores after completing the post-test. There was a statistically significant increase in scores between the pre-test and post-test (P = .0006). These results suggest educational content included in the Doctor of Nursing Practice Quality Improvement Toolkit increases knowledge about use and management of EHR data. Future considerations include use for educating a variety of students and healthcare staff.

Aim: In oncology trials, blinded independent central review (BICR) is the standard for treatment response classification. Real-world data methodologies that align with BICR may reduce misclassification in real-world evidence (RWE) studies and enhance reproducibility, increasing value of RWE. We aimed to develop and validate a novel real-world data-based methodology – real-world Lugano (rwLugano) – for assessing lymphoma response to align with clinical trials. Materials & methods: We conducted a retrospective, multisite chart abstraction study using Cardinal Health Practice Research Network (PRN) sites to identify adults with diffuse large B-cell lymphoma initiating first-line (1L) therapy from 1 January 2015, through 31 December 2022, in US community oncology. Sites collected patient characteristics and PET/CT scans at baseline and first response. Two radiologists independently classified responses; a medical oncologist adjudicated discordances. Results: We compared initial treatment responses using three methods: physician-charted from electronic health records, rwLugano-derived per Lugano 2014 and BICR-adjudicated per Lugano 2014. Agreement was assessed via percentage concordance, kappa (κ), and multivariable generalized linear mixed modeling for assigning complete response (CR). Among 178 patients, CR rates were 63.5% (physician-charted), 81.5% (rwLugano) and 83.1% (BICR). Compared with BICR, rwLugano showed higher agreement (87.9%, κ = 0.52) than physician-charted (77.0%, κ = 0.40). The generalized linear mixed modeling analyses identified clinical factors associated with concordance: for physician charted assessments, greater numbers of extranodal sites increased agreement with BICR (OR 1.92), while MYC mutation (OR 0.38) and anemia (OR 0.37) reduced agreement. For rwLugano, nonprivate insurance was associated with higher agreement (odds ratio [OR]: 4.40), whereas MYC mutation reduced agreement (OR: 0.26). Conclusion: rwLugano improves real-world lymphoma response classification, aligning with BICR and supporting more accurate, reproducible RWE for clinical and regulatory decision-making. Using methods BICR and rwLugano may provide opportunities to minimize outcome misclassification and improve comparability of clinical trial and clinical practice approaches.