ReviewAnnals of laboratory medicine2025
Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.
Review in Annals of laboratory medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Essential Data Elements for Laboratory Test Interoperability: An Expert Consensus-Based Framework.Annals of laboratory medicine · 2026Guideline
- From Detection to Decision Support: Advancing AI-Assisted Serum Quality Assessment.Annals of laboratory medicine · 2026Article
- Enhancing Glomerular Hematuria Identification in Automated Urinalysis Using a Light Gradient Boosting Machine-Based Model: A Diagnostic Accuracy Study.Annals of laboratory medicine · 2026Article
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- Uncovering the role of preventive health services utilization in protecting older adults from heat-related depressive risks.BMC geriatrics · 2026Article
- Companion Dogs and Cats as Key Reservoirs of Antimicrobial Resistance: Evidence and One Health Implications.Antibiotics (Basel, Switzerland) · 2026Review
- A Present Where AI is Enhancing Laboratory Medicine, A Future Where It Redefines Healthcare.EJIFCC · 2025Article
- Optimization of the Total Testing Process Within the Big Data-to-Big Data Loop.Annals of laboratory medicine · 2025Article
- Standardized Medical Terminology: Awareness and Application Among Members of the Korean Society for Laboratory Medicine.Annals of laboratory medicine · 2025Article
- Predictive Modeling of Central Precocious Puberty Using IGF-1 and IGFBP-3 Standard Deviation Scores.Diagnostics (Basel, Switzerland) · 2025Article
- Trends in Urinary Sodium-to-Potassium Ratios in Koreans: Analysis of KNHANES 2016-2023 Data.Nutrients · 2025Article
- Adjustment Formula for Harmonizing Triglyceride Values in the Korea National Health and Nutrition Examination Survey, 2005-2022.Annals of laboratory medicine · 2025Article
- Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning.Annals of laboratory medicine · 2025Article
- A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.Annals of laboratory medicine · 2025Article
- Five-year dementia prediction and decision support system based on real-world data.Frontiers in aging neuroscience · 2025Article
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- Content analysis and optimization suggestions for China's big data on healthcare policy from the perspective of policy tools.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, they have various constraints. Although structured, laboratory data often contain unstandardized terminology or missing information. The major challenge lies in the lack of standardization of test results in terms of metrology, which complicates comparisons across laboratories. In this review, we delve into the essential components necessary for integrating real-world laboratory data into high-quality big data, including the standardization of terminology, data formats, equations, and the harmonization and standardization of results. Moreover, we address the transference and adjustment of laboratory results, along with the certification for quality of laboratory data. By discussing these critical aspects, we seek to shed light on the challenges and opportunities inherent to utilizing real-world laboratory data within the framework of healthcare big data and artificial intelligence.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.