ANCHOR: A Structured, Analysis-Ready Dataset of Orthopedic Surgery and Perioperative Care from a Tertiary Academic Hospital in China

Orthopedic diseases impose a substantial clinical burden and are associated with high surgical volumes; perioperative management is therefore crucial to patient outcomes and healthcare quality. With advancing hospital informatization, large amounts of EMR and surgery/anesthesia data have accumulated. However, orthopedic surgical data are often fragmented across multiple operational systems (e.g., discharge summaries, anesthesia information systems, and nursing systems), with inconsistent variable definitions, heterogeneous coding schemes, and non-uniform data structures, which limits their systematic use in real-world research and quality improvement.This dataset was curated from the inpatient information system and surgery/anesthesia-related systems of The First Affiliated Hospital of the University of Science and Technology of China (Anhui Provincial Hospital). Key variables for orthopedic inpatient surgeries and perioperative care were integrated, structurally extracted, and standardized. The current release covers routine clinical practice data since 2016, with planned annual or periodic updates. Data are organized around two core entities—inpatient encounters and surgery events—and include discharge front-page records (demographics, admission/discharge information, primary and secondary diagnoses, treatments, and discharge outcomes) as well as surgery/anesthesia variables (procedure codes and names, operative time and duration, anesthesia modality, surgeon/anesthesia-related fields, incision/wound healing status, and postoperative assessment indicators). This design supports longitudinal analyses of perioperative processes and outcomes.For standardization and quality control, core fields were harmonized through unified naming, normalized coding, and structural consistency checks. Duplicate records, outliers, and missingness patterns were systematically assessed and flagged. A companion data dictionary documents variable definitions, allowable values, coding meanings, and measurement units, improving interpretability and reusability.The dataset was approved by the institutional ethics committee and fully de-identified prior to release, retaining no direct or indirect personal identifiers. As a single-center real-world clinical dataset with a clear structure and a high level of standardization, it can support perioperative risk assessment and predictive modeling, complication surveillance and quality assurance, analyses of healthcare utilization and resource allocation, and optimization of clinical pathways and management strategies in orthopedic surgery.