Notes

4 immunoglobulin treatment for gentle Guillain-Barré syndrome: a global observational examine.
A learning health system (LHS) must improve care in ways that are meaningful to patients, integrating patient-centered outcomes (PCOs) into core infrastructure. PCOs are common following cancer treatment, such as urinary incontinence (UI) following prostatectomy. However, PCOs are not systematically recorded because they can only be described by the patient, are subjective and captured as unstructured text in the electronic health record (EHR). Therefore, PCOs pose significant challenges for phenotyping patients. Here, we present a natural language processing (NLP) approach for phenotyping patients with UI to classify their disease into severity subtypes, which can increase opportunities to provide precision-based therapy and promote a value-based delivery system.

Patients undergoing prostate cancer treatment from 2008 to 2018 were identified at an academic medical center. Using a hybrid NLP pipeline that combines rule-based and deep learning methodologies, we classified positive UI cases as mild, moderating patients based on indication and severity of PCOs is essential to advance a patient centered LHS. EHRs contain valuable information on PCOs and by using NLP methods, it is feasible to accurately and efficiently phenotype PCO severity. Phenotyping must extend beyond the identification of disease to provide classification of disease severity that can be used to guide treatment and inform shared decision-making. Our methods demonstrate a path to a patient centered LHS that could advance precision medicine.
Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping.

We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype defcomplex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.
Multiple-choice question (MCQ) creation is an infrequently used active-learning strategy. Previous studies demonstrated that medical students find value in the process, but have minimal training, which may limit potential learning benefits. We therefore developed a process for question-creation that required students to complete in-depth training, in addition to collaborative question-writing and editing.

We created a question-writing workshop consisting of three components (1) training in MCQ writing utilizing NBME online modules, a practice MCQ-writing session, and a training session, (2) writing MCQs independently after choosing topics from an institutionally generated blueprint, and (3) reviewing and editing MCQs via an in-person session. To understand students' perceptions, we held two four-student focus groups and recorded/transcribed the data. learn more We iteratively reviewed the transcripts to generate a codebook and corresponding themes. We used the focus group data to generate a survey with Likert-scale o integrate content and compare concepts; students' engagement suggests that they learned from this question-writing activity.
Hispanics are the largest minority group in the US at 18% of the population, of which Puerto Ricans are the second largest subgroup. Puerto Ricans have poorer health status than other US Hispanic and non-Hispanic populations. Thus, health care providers need to know about and distinguish the health care problems of Puerto Ricans to improve their health. Although there are some published curricula addressing how to provide health care to Hispanic populations, none address the specific needs of Puerto Ricans.

We developed a 60-minute interactive workshop consisting of a PowerPoint presentation and case discussion aimed at increasing health care providers' knowledge and understanding of the historical perspective that led to Puerto Rican identity, health issues and disparities, and the health care access problems of mainland and islander Puerto Ricans. Evaluation consisted of pre- and postworkshop questionnaires.

There were a total of 64 participants with diverse ethnoracial identities including medical students, residents, faculty, physicians, researchers, administrators, and students/faculty from nursing, occupational therapy, genetic counseling, biomedical sciences, and social work programs. A comparison of pre- and postworkshop data showed a statistically significant increase in participants' confidence in meeting all learning objectives. Participants positively commented on the interactive nature of the workshop, the case discussion, and the historical perspective provided.

With the increasing migration of Puerto Ricans to the US mainland this module can uniquely improve the preparation of current and future health care providers to provide competent care to Puerto Rican patients.
With the increasing migration of Puerto Ricans to the US mainland this module can uniquely improve the preparation of current and future health care providers to provide competent care to Puerto Rican patients.
Cognitive load theory (CLT) views working memory as the primary bottleneck for learning, as it is limited in both capacity and retention. CLT delineates three types of activities that impose on working memory intrinsic load, germane load, and extraneous load. These three constructs have practical ramifications for direct teaching, learning environments, and curricular design. CLT could help educators across health professions improve quality of teaching, especially in demanding and unpredictable workplace environments. However, few educational resources exist to familiarize clinical workplace educators with CLT.

We developed a 2-hour workshop focused on CLT's core concepts and practical applications, targeted at health professions' workplace educators. It featured large-group, small-group, and individual reflective activities. An end-of-workshop survey was administered, and a follow-up survey was sent to participants 2 months after the workshop.

A total of 134 educators attended the first two offerings of the workshop in two different states.
Here's my website: https://www.selleckchem.com/products/cabotegravir-gsk744-gsk1265744.html