Data quality for safer and more personalized perioperative care: a scoping review

Abstract

Background: The exponential growth of perioperative data generated by monitors, electronic health records (EHRs), and wearable devices (WD) represents a significant promise for improving risk assessment, preventing complications, and personalizing perioperative care. Perioperative care produces a wide range of data types from diverse sources (e.g., intraoperative monitors, EHRs, and WD) that can be analyzed using machine learning (ML) techniques. The use of data-driven techniques to big data from perioperative medicine is being extended to different settings of perioperative care, including risk prediction, intraoperative monitoring, complication reduction, and decision support. However, the quality of these data often remains uncertain, potentially limiting the effectiveness of even the most advanced models.

Objective: This scoping review maps the current literature on perioperative data quality. It explores common quality challenges (such as missing, inaccurate, or non-standardized data) and highlights tools, frameworks, and methodologies, from harmonization standards to ML-based imputation techniques. We address the challenges of ensuring adequate data collection, data accuracy, and consistency. We emphasize the importance of data standardization and harmonization through common models to facilitate interaction and integration among different hospitals, systems, and countries. Such efforts aim to enhance external validation and bridge the translational gap from bench to bedside.

Design: We included English-language publications that addressed perioperative data quality issues. We searched PubMed and reviewed the reference lists of relevant articles. Two independent reviewers selected studies and extracted data. Our analysis focused on four key topics: data accuracy, handling of missing data, standardization, and harmonization.

Results: Of the 342 publications, many highlight that perioperative data derive from multiple sources, including intraoperative monitors, ICU systems, EHRs, registries, and WD. Missing values, artifacts, and uneven documentation were common challenges. Studies reported that using advanced filtering and imputation algorithms, standard vocabularies (like SNOMED CT and LOINC), and common data models (CDMs, such as OMOP) improved data sharing and use. Initiatives like the Multicenter Perioperative Outcomes Group (MPOG) demonstrated how harmonized datasets could drive multi-institutional quality improvement and research.

Conclusions: This review focuses on perioperative data quality; we translate technical methods into practical strategies for data-driven perioperative care. It highlights the strong link between data quality and improved perioperative care. Achieving the diffusion of reliable and standardized data calls for strategic efforts on regulatory alignment, staff training, and the development of large collaborative networks. As perioperative medicine evolves, high-quality data will serve as the foundation for reliable predictive modeling, safer anesthesia management, and more patient-centered approaches.

Keywords

Data quality / perioperative care / machine learning / data standardization / data harmonization / predictive modeling / interoperability

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