ArticlePharmacoEconomics2021
Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment.
Article in PharmacoEconomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses 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
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Multiple modes of data sharing can facilitate secondary use of sensitive health data for research.BMJ global health · 2023Pooled it
- Elevated C-reactive Protein Levels and Malignant Transformation Risk in Patients With Oral Mucosal Diseases.International dental journal · 2026Observational
- Enabling interoperability across disparate health data sources using Large Language Models.PLOS digital health · 2026Article
- Addressing Data Quality Challenges in Lung Cancer Data Within the Observational Medical Outcomes Partnership Common Data Model: Observational Study.Journal of medical Internet research · 2026Observational
- 'Crossing borders' in data standardisation: application of OMOP CDM in an international clinical trial network in precision cancer medicine.Acta oncologica (Stockholm, Sweden) · 2026Article
- Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).Critical care medicine · 2026Article
- Extended Grammar of Systematized Nomenclature of Medicine - Clinical Terms for Semantic Representation of Clinical Data: Methodological Study.Journal of medical Internet research · 2026Article
- Evaluating Frailty Index Integrity: Insights from an International Network Study.medRxiv : the preprint server for health sciences · 2026Article
- Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library.The patient · 2025Review
- Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies.Journal of glaucoma · 2025Article
- Conversion of Sensitive Data to the Observational Medical Outcomes Partnership Common Data Model: Protocol for the Development and Use of Carrot.JMIR research protocols · 2025Article
- Exploring Metadata Catalogs in Health Care Data Ecosystems: Taxonomy Development Study.JMIR formative research · 2025Article
- PIONEER big data platform for prostate cancer: lessons for advancing future real-world evidence research.Nature reviews. Urology · 2025Review
- Building a healthcare data warehouse: considerations, opportunities, and challenges.Frontiers in digital health · 2025Review
- Perspective review: Will generative AI make common data models obsolete in future analyses of distributed data networks?Therapeutic advances in drug safety · 2025Review
- Article
- FAIR foundations of a novel indicator vault for non-communicable diseases in the European Union: feasibility study for effective contextualisation of indicators.Frontiers in digital health · 2025Article
- Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning.Journal of medical Internet research · 2024Article
- Mesh-associated pain syndrome: predictors for continence and prolapse mesh removal surgery in a single centre.BMC women's health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.
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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.