Open Payments Dataset
The Open Payments Dataset is a public database listing payments made by pharmaceutical, biomedical, or medical device companies to healthcare professionals and hospitals. Made available by the American government, this dataset promotes the transparency of financial relationships in the medical sector.
Approximately 30 million records in CSV format
Open access, published by the CMS (Centers for Medicare & Medicaid Services) under an open license for transparency purposes
Description
This dataset accurately documents:
- Amounts paid by companies to doctors, researchers or institutions
- The type of payment (consulting, meals, travel, royalties...)
- The nature of the product or device concerned
- Beneficiaries' identification data (name, specialty, institution)
Each transaction is dated, traced and categorized, which allows for a detailed analysis of financial interactions between the healthcare industry and its medical partners.
What is this dataset for?
Open Payments Dataset is used to:
- Analysis of financial flows between industry and health professionals
- The detection of potential conflicts of interest
- Monitoring transparency in the funding of research or medical practices
- Training AI models to classify transactions or predict risks of financial dependence
- Academic research in public health, regulation or health economics
Can it be enriched or improved?
Yes, depending on the use case:
- By cross-referencing data with scientific publications or clinical trials
- By standardizing beneficiary identifiers to facilitate groupings
- By analysing the evolution of funding over time by specialty or company
- By visualizing influence networks based on payment flows
🔗 Source: Open Payments Dataset
Frequently Asked Questions
Can we identify the doctors or hospitals concerned?
Yes, the dataset contains the names, specialties and structures of the beneficiaries, as well as the details of the issuing companies.
Is it possible to filter by payment type or by product?
Yes, each line contains specific categories that allow you to filter by type of expense, product concerned or company.
What precautions should be taken for ethical use?
Data must be analyzed rigorously, without individual stigma, and cross-referenced with other sources for contextualized interpretation.
