Applying the OMOP Common Data model to Dutch healthcare data. A case study

Transcription

1 Applying the OMOP Common Data model to Dutch healthcare data A case study 2017 October PHUSE Edinburgh PHUSE Edinburgh 1

2 Rients van Wijngaarden, Msc Senior Data Scien<st PHARMO Ins<tute, the Netherlands 2

3 Outline PHARMO Database Network Challenges OMOP Common Data Model & OHDSI PHARMO Diabetes Cohort Syntac<c mapping Seman<c mapping Deployment and Tools Conclusion and future steps 3

4 PHARMO Database Network (NL) 4

5 PHARMO in Europe future partners 5

6 Challenges Different Electronic Health Record (EHR) databases use different data standards EHR data is ooen coded in na<onal coding systems Collabora<ons are ooen tailor made solu<ons Risk of reinven<ng the wheel 6

7 OMOP Observa<onal Medical Outcomes Partnership public-private partnership aimed at the development of a common data model (CDM) and vocabulary specifically for observa<onal healthcare databases The partnership ended in

8 OHDSI Observa<onal Health Data Sciences and Informa<cs collabora<ve a mul<-stakeholder, interdisciplinary collabora<ve ac<vely maintains the OMOP common data model and vocabulary aims at the crea<on of open-source solu<ons to leverage the value of observa<onal health data through large-scale analy<cs 8

9 Common Data Model (version 5.0) 9

10 PHARMO Diabetes Cohort 10

11 PHARMO Diabetes Cohort General Prac<<oner (GP) data from Diabetes care mainly managed in GP sezng Sample of 73,829 pa<ents with a Type 2 Diabetes Mellites diagnosis. Average follow-up of 6.7 (±3.7) years 11

12 SyntacQc mapping data items available in the source data are mapped to the corresponding data items in the predefined target tables for example: medica<on prescrip<ons mapped to drug exposure table White Rabbit scans and summarizes source data Rabbit In A Hat GUI to create and annotate mappings 12

13 Rabbit In A Hat 13

14 SemanQc mapping local coding systems are mapped to the coding systems as used within the OMOP common data model OHDSI tools are available based on English, not applicable to Dutch codes 14

15 SemanQc mapping Clinical concept Local coding system (Inter)naQonal Mapped to Drug prescrip<ons KNMP Na<onal RxNorm Condi<ons ICPC Interna<onal SNOMED Observa<ons WCIA Na<onal SNOMED Measurements WCIA Na<onal LOINC 100% mapping not feasible 80% mapping of cumula<ve count of occurences 15

16 Deployment Syntac<c and seman<c mappings results in set of Extrac<ons, Transform, Load (ETL) scripts Took 4 itera<ons to get the final set of ETL scripts We chose to handle data issues in the source tables to keep the ETL process comprehensive OHDSI tools installed 16

17 Achilles Tool for data characteriza<on Summarizes data loaded in the OMOP CDM Creates predefined reports Achilles is an R package Results are visualized in a web applica<on 17

18 Achilles dashboard 18

19 Achilles drug exposure report 19

20 Atlas brings different individual components together in one user interface aid researchers to perform scien<fic analysis on the OMOP data allows users to create cohorts based on entry and exit criteria enables the user to browse the various vocabularies build on open source tools 20

21 Atlas dashboard 21

22 Conclusion OMOP CDM may seem daun<ng at first forces you to rethink your own data sooware installa<ons are tricky but: We are not alone OHDSI is constantly developing and sharing new tools and packages it will facilitate remote access and collabora<on 22

23 Future steps Expand the mapping to other data sets (pharmacies, hospitalisa<ons, etc.) Expand the mapping to other disease cohorts Finetune seman<c mappings Run various analysis methods developed within OHDSI Explore the possibili<es for remote access Collaborate in mul<-database studies with OMOP 23

24 Contact details T E +31 (0) pharmo@pharmo.nl 24