Cohort Discovery and Analytics
OMOP CDM for Analytics and Cohort Analysis
Objective:
This lab serves as the culmination of the course, providing students with the skills to transform and integrate healthcare data collected from various sources (EHRs, HIEs, FHIR resources, tokenized clinical notes, and patient engagement data) into the OMOP Common Data Model (CDM). Students will learn the structure and purpose of the OMOP CDM, perform ETL (Extract, Transform, Load) processes to standardize the data, and leverage it for analytics and cohort analysis. This lab emphasizes the importance of data standardization for research, interoperability, and large-scale analytics.
Lab Overview
Introduction to OMOP CDM:
Understand the purpose and structure of the OMOP Common Data Model.
Explore how OMOP enables standardized data storage and facilitates large-scale healthcare analytics and research.
Data Sources Overview:
Review and Load Standard Vocabulary
Review Synthetic FHIR Data
ETL Process:
Learn the steps to Extract, Transform, and Load (ETL) data into the OMOP CDM format.
Map different data sources to the appropriate OMOP CDM tables (e.g., Person, Observation, Condition, Drug Exposure).
Cohort Analysis and Analytics:
Use the standardized OMOP dataset for cohort creation and analysis.
Perform tasks such as identifying patients with specific conditions, analyzing medication adherence patterns, or studying treatment effectiveness.
Visualization and Reporting:
Generate meaningful visualizations and reports from the OMOP-transformed data to showcase cohort analysis outcomes.
Lab Workflow
ETL Process:
Extraction:
Review Synthetic FHIR Data
Transformation:
Map source data fields to the corresponding OMOP CDM tables:
Patient demographic data → Person Table
Diagnoses and conditions → Condition Occurrence Table
Medications → Drug Exposure Table
Lab results and observations → Observation Table
Standardize terminology using OMOP-compatible vocabularies (e.g., SNOMED, RxNorm, LOINC).
Loading:
Load the transformed data into the OMOP CDM database.
Cohort Creation:
Use SQL queries or cohort-building tools (e.g., Atlas, Achilles) to create patient cohorts based on specific criteria:
Example: "Patients with Type 2 Diabetes who are non-adherent to Metformin therapy."
Data Analytics:
Analyze the cohort for trends, treatment effectiveness, or adverse events.
Perform comparisons between patient groups (e.g., adherent vs. non-adherent patients).
Visualization and Reporting:
Create charts, graphs, and reports summarizing the cohort analysis.
Reflect on how the OMOP CDM enables large-scale research and analytics.
Learning Objectives
By the end of this lab, students will be able to:
Understand the OMOP CDM:
Explain the purpose of OMOP CDM in standardizing healthcare data for analytics.
Understand the structure and key tables in the OMOP schema.
Integrate and Standardize Data:
Perform ETL processes to integrate data from diverse sources into the OMOP CDM.
Map source data to OMOP CDM tables and standardize terminology using OMOP-compatible vocabularies.
Leverage Standardized Data for Analytics:
Create patient cohorts using OMOP-transformed data.
Analyze trends, patterns, and outcomes in patient care based on standardized data.
Visualize and Report Findings:
Develop visualizations and reports that summarize insights from cohort analyses.
Communicate the impact of data standardization on healthcare research and analytics.
Recognize the Value of Data Standardization:
Appreciate how OMOP CDM enables interoperability, large-scale analytics, and reproducible research.
Understand the challenges and solutions in transforming real-world data into a common format.
Outcome
This lab equips students with the knowledge and skills to transform healthcare data into the OMOP CDM and leverage it for meaningful analytics. By understanding the entire process—from data collection to cohort analysis—students gain practical insights into how standardized data drives evidence-based healthcare decisions and research.
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