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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.

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.