Achilles a platform for exploring and visualizing clinical data summary statistics

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Achilles — a platform for exploring

and visualizing clinical data summary

statistics

Mark Velez, MA

Ning "Sunny" Shang, PhD

Department of Biomedical Informatics,

Columbia University

NIH BD2K bioCADDIE webinar, August 13th_{, 2015}

Biomedical Informatics

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Outline

• OHDSI

• ACHILLES demo

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What is OHDSI

• The Observational Health Data Sciences and

Informatics (OHDSI) program is a

multi-stakeholder, interdisciplinary collaborative

– To bring out the value of observational health data through large-scale analytics and evidence

generation

• Clinical characterization

• Population-level estimation • Patient-level prediction

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What is OHDSI

• Single observational data source is unlikely to

be sufficient for research analysis needs

– Analyze multiple data sources concurrently

• Using a common data model and the

foundational infrastructure to enable

observational research

– By 2014, 58 databases in CDM – > 250 million patients covered

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What is OHDSI

• Mission

– To transform medical decision making by creating reliable scientific evidence about disease natural history, healthcare delivery, and the effects of

medical interventions through large-scale analysis of observational health database for population-level estimation and patient-population-level predictions

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Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)

statistical analysis Analytic tools

Data Source 1 Data Source 2 Data Source 3

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Data transform in CDM

• Extracting, transforming, and loading (ETL)

process

– WhiteRabbit: analyzes the structure and content of a database

– RabbitInAHat: connects and maps tables and

columns from the raw dataset to the CDM dataset – ETL-CDMBuilder: transform raw data to CDM

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ACHILLES

(Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems)

• An open source analytics framework

• Interactively explore population-level

summary statistics for the data stored in CDM

– Profile your CDM data

– Explore population-level summaries – Review data quality assessment

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ACHILLES implementation

• ACHILLES R package

• Oracle / SQL Server / Postgres / Redshift

• Summary statistics export into Json to prepare

data for visualization

• Visualization by AchillesWeb (HTML5 /

JavaScript)

create strata tables Data quality queries (Heel) Export to JSON Visualization (AchillesWeb)

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ACHILLES Summary Statistics 1

• Summary of data set / clinical database

– Size of the database

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ACHILLES Summary Statistics 2

• Person demographic information and

demographic information over death

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ACHILLES Summary Statistics 3

• Metadata (e.g. observation periods, data

density)

– Observation periods document time intervals during which health care information captured

– Data density describes the unit quantity of records and concepts pertains in each database

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ACHILLES Summary Statistics 3

• Prevalence of condition/condition era/

observation/drug exposure/drug

era/procedure/visit

– Treemap view – Table view

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ACHILLES Summary Statistics 4

• Achilles HEEL

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ACHILLES Heel Error Types

Error Type Example

Clinical facts

Illogical change Monthly change of count of condition is more than 100% Invalid ids Person has invalid provider_id

Improper value based on norm

Year of birth is less than 1800 Negative payment

Improper value based on inter-relationship

A condition is recorded after the patient is dead Terminology

Not standard vocabulary a concept is not a standard OMOP vocabulary concept Non-mapped concept Data with unmapped concepts

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Applications of ACHILLES

• Explore summary statistics about the clinical

data

– Public domain (de-identified information)

• Integrate with clinical systems

• Achilles integrating other OHDSI tools

• Framework for other applications

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ACHILLES collaborating with other

OHDSI tools

ACHILLES Database profiling CIRCE Cohort definition HERACLES Cohort characterization

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ACHILLES Framework for other

applications—bioCADDIE DDI

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Suitability

• General definition

– the quality or state of being especially suitable or fitting [Merriam-Webster]

• In our project

– The extent to which a clinical dataset to meet the research needs for observational studies

– Data suitability is how suitable the data are for a specific research purpose

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Research methods

Suitability conceptual framework Web-based survey Metrics with Columbia EHR Hybrid Approach Implementation by Customizing ACHILLES EHR characteristics lit review Measures Categories Observa tional study-derived sub-measur e Desider ata study-derived sub-measur e

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Suitability of Clinical Database for Observational Study Can I access? Policy and Administration • Data policy documentat ion • Administrati ve platform • Technical accessibility Relevance • Healthcare organization description • Data organization documentation • Research data inventory • Available and retrievable temporal information Quality • Data quality control • Database data quality • Research sample data quality Usability • Data representatio n • Usefulness • Cohort availability • Database linkability Descriptive metadata and provenance documentation • Data provenance • Database content synopsis User --Researcher What’s inside? (content) Are data usable? Intrinsic

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Suitability Survey

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Important websites

• OHDSI

– http://www.ohdsi.org/

– Main GitHub Page: https://github.com/OHDSI/

– Forum: http://forums.ohdsi.org/

• ACHILLES

–

http://www.ohdsi.org/analytic-tools/achilles-for-data-characterization/

– R Package for Generating Statistics for ACHILLES:

https://github.com/OHDSI/Achilles

– Web Application for Viewing ACHILLES Results:

https://github.com/OHDSI/AchillesWeb

– Demo

http://www.ohdsi.org/web/achilles/index.html#/OHDSI%20Sam ple%20Database/dashboard