Achilles a platform for exploring and visualizing clinical data summary statistics
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1 Biomedical Informatics discovery and impact 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 13 th, 2015
2 Outline OHDSI ACHILLES demo Applications of ACHILLES 2
3 What is OHDSI The Observational Health Data Sciences and Informatics (OHDSI) program is a multistakeholder, 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 3
4 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 4
5 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 populationlevel estimation and patient-level predictions 5
6 OHDSI Infrastructure Data Source 1 Data Source 2 Data Source 3 Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) statistical analysis e.g. Treatment pathway Analytic tools ACHILLES CIRCE Others 6
7 OMOP Common Data Model (CDM) V 5 7
8 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 8
9 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 Data in CDM Summary statistics Web visualization of statistics 9
10 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) 10
11 ACHILLES Summary Statistics 1 Summary of data set / clinical database Size of the database First /Continuous observation 11
12 Dashboard Summary of clinical dataset 12
13 ACHILLES Summary Statistics 2 Person demographic information and demographic information over death 13
14 Person 14
15 Death 15
16 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 16
17 Observation Periods 17
18 Data Density 18
19 ACHILLES Summary Statistics 3 Prevalence of condition/condition era/ observation/drug exposure/drug era/procedure/visit Treemap view Table view Drill down view 19
20 Condition Treemap view 20
21 Condition Table view 21
22 Condition Drill down view 1 22
23 Condition Drill down view 2 23
24 ACHILLES Summary Statistics 4 Achilles HEEL Data quality control component 24
25 Achilles Heel Data quality tool 25
26 ACHILLES Heel Error Types Error Type Clinical facts Example Illogical change Monthly change of count of condition is more than 100% Invalid ids Improper value based on norm Improper value based on inter-relationship Terminology Not standard vocabulary Non-mapped concept Wrong mapping concept Person has invalid provider_id Year of birth is less than 1800 Negative payment A condition is recorded after the patient is dead a concept is not a standard OMOP vocabulary concept Data with unmapped concepts Drug is not coded with RxNorm 26
27 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 27
28 ACHILLES collaborating with other OHDSI tools ACHILLES Database profiling CIRCE Cohort definition HERACLES Cohort characterization 28
29 ACHILLES Framework for other applications biocaddie DDI Suitability Framework 29
30 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 30
31 Research methods EHR characteristics lit review Suitability conceptual framework Web-based survey Metrics with Columbia EHR Hybrid Approach Categories Measures Implementation by Customizing ACHILLES Observa tional studyderived submeasur e Desider ata studyderived submeasur e 31
32 Can I access? User -- Researcher What s inside? (content) Suitability of Clinical Database for Observational Study Are data usable? 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 Descriptive metadata and provenance documentation Data provenance Database content synopsis Usability Data representatio n Usefulness Cohort availability Database linkability Quality Data quality control Database data quality Research sample data quality Accessibility Representation Intrinsic Contextual Data (data characteristics)
33 Suitability Survey 33
34 Implementation 34
35 Important websites OHDSI Main GitHub Page: Forum: ACHILLES R Package for Generating Statistics for ACHILLES: Web Application for Viewing ACHILLES Results: Demo ple%20database/dashboard 35
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