Data Access and Preparation
This category covers the full journey from clinical data to a ready‑to‑use dataset, with verified quality and end‑to‑end traceability.
- Variable extraction from structured data and, where applicable, from clinical text using NLP to recognise entities, handle negation and temporality and normalise terminology.
- Cohort definition and extraction at several complexity levels, applying combined rules, time windows and episodisation when required.
- OMOP digital cohorts with identification and characterisation to support comparability, reproducibility and analysis.
- Preparation and integration with cleansing, source linking and basic derivations to speed up analytics without losing lineage.
- Standardisation to open models such as OMOP and HL7 FHIR so data can be shared and reused with less effort and fewer errors.
- Shared data catalogue with clear records that state what each dataset is, how to request access and under which conditions it can be used, so teams find what they need and apply it with the right context.
- Practical enablers: rapid feasibility to estimate recruitment, cloud workspace preparation and autonomous use with standard tooling and controlled data egress.
Cross-cutting objective
To ensure consistency, traceability and reusability of information, with quality controls and data protection, and with open standards that make it easier to work within and across organisations safely and responsibly
More info
Fees
Book an appointment
If you want to learn more about the project, write to us and we’ll tell you all about it!
If you are interested in learning more or participating in this project, you can email us at omicspace@iislafe.es.
You can also call us at +34 961 41 16 92 (246607) and tell us more.
We look forward to hearing from you!
Data Access and Preparation
This category covers the full journey from clinical data to a ready‑to‑use dataset, with verified quality and end‑to‑end traceability.
- Variable extraction from structured data and, where applicable, from clinical text using NLP to recognise entities, handle negation and temporality and normalise terminology.
- Cohort definition and extraction at several complexity levels, applying combined rules, time windows and episodisation when required.
- OMOP digital cohorts with identification and characterisation to support comparability, reproducibility and analysis.
- Preparation and integration with cleansing, source linking and basic derivations to speed up analytics without losing lineage.
- Standardisation to open models such as OMOP and HL7 FHIR so data can be shared and reused with less effort and fewer errors.
- Shared data catalogue with clear records that state what each dataset is, how to request access and under which conditions it can be used, so teams find what they need and apply it with the right context.
- Practical enablers: rapid feasibility to estimate recruitment, cloud workspace preparation and autonomous use with standard tooling and controlled data egress.
Cross-cutting objective
To ensure consistency, traceability and reusability of information, with quality controls and data protection, and with open standards that make it easier to work within and across organisations safely and responsibly
More info
Fees
Book an appointment
If you want to learn more about the project, write to us and we’ll tell you all about it!
If you are interested in learning more or participating in this project, you can email us at omicspace@iislafe.es.
You can also call us at +34 961 41 16 92 (246607) and tell us more.
We look forward to hearing from you!
Data Access and Preparation
This category covers the full journey from clinical data to a ready‑to‑use dataset, with verified quality and end‑to‑end traceability.
- Variable extraction from structured data and, where applicable, from clinical text using NLP to recognise entities, handle negation and temporality and normalise terminology.
- Cohort definition and extraction at several complexity levels, applying combined rules, time windows and episodisation when required.
- OMOP digital cohorts with identification and characterisation to support comparability, reproducibility and analysis.
- Preparation and integration with cleansing, source linking and basic derivations to speed up analytics without losing lineage.
- Standardisation to open models such as OMOP and HL7 FHIR so data can be shared and reused with less effort and fewer errors.
- Shared data catalogue with clear records that state what each dataset is, how to request access and under which conditions it can be used, so teams find what they need and apply it with the right context.
- Practical enablers: rapid feasibility to estimate recruitment, cloud workspace preparation and autonomous use with standard tooling and controlled data egress.
Cross-cutting objective
To ensure consistency, traceability and reusability of information, with quality controls and data protection, and with open standards that make it easier to work within and across organisations safely and responsibly
More info
Fees
Book an appointment