Decoding disparities: evaluating automatic speech recognition system performance in transcribing Black and White patient verbal communication with nurses in home healthcare.

Objectives: As artificial intelligence evolves, integrating speech processing into home healthcare (HHC) workflows is increasingly feasible. Audio-recorded communications enhance risk identification models, with automatic speech recognition (ASR) systems as a key component. This study evaluates the transcription accuracy and equity of 4 ASR systems-Amazon Web Services (AWS) General, AWS Medical, Whisper, and Wave2Vec-in transcribing patient-nurse communication in US HHC, focusing on their ability in accurate transcription of speech from Black and White English-speaking patients.

Obstetric Emergency Supply Chain Dynamics and Information Flow Among Obstetric Emergency Supply Chain Employees: Key Informant Interview Study.

Background: For the past several decades, the Ethiopian Ministry of Health has worked to decrease the maternal mortality ratio (MMR)-the number of pregnant women dying per 100,000 live births. However, with the most recently reported MMR of 267, Ethiopia still ranks high in the MMR globally and needs additional interventions to lower the MMR to achieve the sustainable development goal of 70. One factor contributing to the current MMR is the frequent stockouts of critical medications and supplies needed to treat obstetric emergencies.

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Objectives: Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench.

Returning value from the All of Us Research Program to PhD-level nursing students using ChatGPT as programming support: results from a mixed-methods experimental feasibility study.

Objective: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.

Materials And Methods: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.

Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework.

Background:  Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

Objective:  This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care.

Advancing Data Science Competencies for Nursing PhD Students.

Given the evolving importance of data science approaches in nursing research, we developed a 3-credit, 15-week course that is integrated into the second year PhD curriculum at Columbia University School of Nursing. As a complement to didactic content, the students address a research question of their choice using a big data source, Jupyter Notebook, and R programming language. The course evolved over time with generative AI tools being added in 2023.

Percutaneous radiofrequency ablation of ovarian cancer metastasis in the spleen: a therapeutic option to consider.

Splenic metastasis are rare clinical entities developing in less than 1% of all metastatic cancers and usually in the setting of disseminated disease. To date, splenectomy is traditionally the first line therapy in patient with splenic metastasis, however non-surgical therapies have been reported. Here we described the case of a 57-year-old patient with splenic metastasis from ovarian cancer successfully treated by percutaneous radiofrequency ablation.

User-Centered Design of a Preference-Driven Patient Activation Tool for Optimizing Depression Treatment in Integrated Primary Care Settings (The Transform DepCare Study).

Background: Few patient engagement tools incorporate the complex patient experiences, contexts, and workflows that limit depression treatment implementation.

Objective: Describe a user-centered design (UCD) process for operationalizing a preference-driven patient activation tool.

Design: Informed by UCD and behavior change/implementation science principles, we designed a preference-driven patient activation prototype for engaging patients in depression treatment.

Perspectives on the role of industry in informatics research and authorship.

Objectives: Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship.

Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study.

Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results.

Enhancing ACLF prediction by integrating sarcopenia assessment and frailty in liver transplant candidates on the waiting list.

Background & Aims: Malnutrition, sarcopenia, and frailty are prevalent in cirrhosis. We aimed to assess the correlation between assessment tools for malnutrition, sarcopenia, and frailty in patients on the liver transplant (LT) waiting list (WL), and to identify a predictive model for acute-on-chronic liver failure (ACLF) development.

Methods: This prospective single-center study enrolled consecutive patients with cirrhosis on the WL for LT (May 2019-November 2021).

Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles.

Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes as well as notable gaps. We also compared trends in studied conditions to their relative prevalence using insurance claims (42 million Americans).