TriNetX Journey: Challenges in Building a sustainable global data and analytics platform for clinical trials and research
We look back at the decade of effort to build, operate and grow the TriNetX strategic platform found in research informatics portfolios of many healthcare organizations and academic medical centers world-wide.
Improving Acute Kidney Injury Prediction and Risk Factor Analysis with Personalized Transfer Learning
Acute kidney injury (AKI) is a life-threatening clinical syndrome prevalent in hospitalized patients (10% to 15% affected), especially among critically ill patients (>50% affected), and has exceeded the annual incidence of myocardial infarction. AKI patients are at much higher risk for developing poor long-term outcomes including incident and progressive chronic kidney disease, cardiovascular disease, and death.
High-dimensional data from procedural recordings are increasingly being leveraged by institutions for quality, safety, and efficiency. With advances in technology enabling high fidelity recordings and large scale analytics, the possible applications for this data continue to expand. Institutions must consider their data strategy and the ethical, legal, privacy, and insurance implications of recording clinical procedures.
Several editors and authors of clinical research informatics textbooks and chapters will present developed topics in the CRI field and new topics in CRI compared to earlier editions of the books. Presenters will talk about the 3rd edition of Springer Clinical Research Informatics, the CRI chapter in the 5th edition of Springer Biomedical Informatics, and the CRI chapter in the 8th edition of Health Informatics.
Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges.