Resources
Understand healthcare data and learn to use it.
Understanding health data warehouses
From collection to analysis of clinical data, a guide designed for clinicians.
Manual data collection in the age of the electronic health record
Manual collection remains the norm in clinical research. Clinical data warehouses offer an alternative that is still largely underused.
Defining your variables well: the key to reliable data collection
Concept, temporal anchor, time window, and aggregate function — four dimensions for unambiguous variables.
Clinical data warehouses: leveraging data already collected
Care data is already in your hospital software. A data warehouse makes it usable for research.
From raw data to usable data
Warehouse data is rarely ready to use. Data quality is a massive cumulative investment — but a lasting one.
How is this data organized
Tables, relationships, joins — understanding data structure to communicate with a data scientist.
Speaking the same language: medical terminologies
ICD-10, LOINC, SNOMED CT, ATC… Why these terminologies exist, what they cover, and how they enable data comparison across hospitals.
Designing a research project on a data warehouse
From research question to publication: the steps, the roles, and where Linkr fits in.
Using the Study Designer
Learn how to design a complete research protocol with the Study Designer, step by step.
Create and structure a protocol
Getting started with the Study Designer: create a project, fill in general information, context, objectives, and data sources.
Define the study population
Build inclusion and exclusion criteria with the logic tree and the eight criterion types.
Variables, concept sets, and anchors
Create concept sets, define temporal anchors, and configure variables with their collection time windows.
Finalize and export the protocol
Sample size, analysis plan, timeline, regulatory, references, and Word/Excel/Markdown export.
Learn programming
Resources to get started with SQL, R, and Python.
Introduction to SQL
SQL basics: SELECT, WHERE, JOIN, GROUP BY. Additional resources to go further.
Introduction to R
Getting started with R: syntax, tidyverse, visualization. Additional resources.
Introduction to Python
Getting started with Python: syntax, pandas, matplotlib. Additional resources.
Artificial intelligence in healthcare
Understand and apply artificial intelligence to clinical data.
OMOP
The OMOP CDM standard and the OHDSI network: understand the model, its tables, its vocabularies, and practice with interactive tutorials.
Introduction to OMOP and OHDSI
History of the OMOP project, birth of OHDSI, worldwide adoption, European and international projects.
Medical terminologies and OMOP vocabularies
ICD-10, LOINC, SNOMED CT, ATC, RxNorm — and the OMOP standardized vocabulary system.
CDM data tables
PERSON, VISIT_OCCURRENCE, CONDITION_OCCURRENCE, MEASUREMENT… Explore the clinical tables of the OMOP model.
OMOP — Beginner level
Essential tables: person, visit_occurrence, condition_occurrence, measurement.
OMOP — Intermediate level
Vocabularies, concept_relationship, multi-table queries.
OMOP — Advanced level
Cohorts, drug_era, condition_era, Atlas, population studies.
FHIR
The FHIR standard for health data interoperability.
Public databases
Discover freely accessible healthcare databases for research and learning.
Overview of public healthcare databases
A tour of freely accessible databases for research: MIMIC, AmsterdamUMCdb, HiRID, SICdb and more.
The MIMIC database
Introduction to MIMIC-IV, how to download it from PhysioNet, database schema, first steps.
AmsterdamUMCdb
ICU database from Amsterdam UMC: content, access, and first steps.
HiRID
ICU database from Bern University Hospital: high temporal resolution.
SICdb
ICU database from Salzburg University Hospital: content and access.
The academic world
Learned societies, conferences, and training in medical informatics.
Learned societies in medical informatics
IMIA, EFMI, AIM, SFIM — the institutional landscape of medical informatics.
Conferences: MIE, STC and more
Overview of EFMI MIE and STC conferences, how to submit, calendar.
Training in health data science
Master's degrees, AI diplomas in healthcare, university programs, MOOCs — an overview.