Using EMR Data for Population Registries

1. Using EMR Data for Population Registries Diana Gumas, JHMCIS Senior Director for Research Systems, EPR and EPR2020/Amalga David Thiemann, Center for Clinical Data Analysis 1

2. 2 Preliminary anonymous data - How many patients with specific lab results, ICD9s, radiology studies, etc., are available for my study? What are the demographics? Preliminary anonymous data - How many patients with specific lab results, ICD9s, radiology studies, etc., are available for my study? What are the demographics?

3. Possible research data sources EPR (JHH & JHBMC) Sunrise Clinical Manager (JHH – inpatient) Meditech (Bayview) Casemix Datamart GE Centricity (JHCP) EPR2020 Departmental Systems (ED, OR, Anesthesia) Clinical Research Management System (CRMS) IDX (professional fees) Death Registry 3

4. Methods for Data Access Historical: Researcher Negotiates Access With Clinical System /DBA Logistic nightmare, technical challenge Clinical Research Management System (CRMS) Study cohort with real-time links to enterprise data Center for Clinical Data Analysis Monthly/quarterly data extracts from designated systems 4

5. Clinical Research Management System (CRMS) 5 1,054 Users 1079 Active Studies 25,430 Participants Data Available in CRMS eIRB EPR (patient demographics) Study participants / accruals Electronic Case Report Forms - in next 2-3 months As of July 1, 2009, must be used on all JH SOM studies which could result in a patient bill Highlight overall workflow supported by CRMS - Nightly data pull from eIRB The Protocol Library houses study-related documents for the conduct of the trial. It includes: Interfaces to other systems: eIRB, EPR, Protocol LibraryAs of July 1, 2009, must be used on all JH SOM studies which could result in a patient bill Highlight overall workflow supported by CRMS - Nightly data pull from eIRB The Protocol Library houses study-related documents for the conduct of the trial. It includes: Interfaces to other systems: eIRB, EPR, Protocol Library

6. Clinical Research Management System (CRMS) 6 Ways to extract data Canned Reports (click for examples) Ad-hoc querying using SQL Possible with CCDA support - automated study-specific data extracts

7. EPR2020 Data for Researchers 7 4.2M Patients, 23.4M Visits 12.3M Documents, 6.8M Radiology Reports 25.6M Lab Results 1.5M Problems, 2.2M Medications, 140K Allergies Planned Bayview & JHCP data ICD9 diagnosis codes and CPT charges (IDX) Future Death Registry Blood Product Data for Transfusions Eclipsys SCM Order data HMED (ED), ORMIS, eADR/Medivision Data Discovery Tool Research application – tailored for investigator’s needs “My participants” and their data Alert me about key events Search through de-identified data to answer questions Data Discovery Tool Research application – tailored for investigator’s needs “My participants” and their data Alert me about key events Search through de-identified data to answer questions

8. My Participant’s Lab Data 8 Today research staff laboriously look up patients in EPR one by one, find and transcribe the lab results. Time consuming. Error Prone With EPR 2020 Researcher immediately sees “their” participants Grid with lab results they can filter & sort Exportable to Excel Status Positive feedback from 5 study teams Improving speed Anticipate widespread availability by End of Summer 2010Today research staff laboriously look up patients in EPR one by one, find and transcribe the lab results. Time consuming. Error Prone With EPR 2020 Researcher immediately sees “their” participants Grid with lab results they can filter & sort Exportable to Excel Status Positive feedback from 5 study teams Improving speed Anticipate widespread availability by End of Summer 2010

9. Registry Cohort Discovery using EPR2020 A JHM investigator wants to find and enroll diabetic patients aged 45-65 years with hemoglobin A1C between 7 and 9% serum creatinine < 2 mg/dl 9 NEAR FUTURE! Launch a de-identified researcher view Get list of patients Use Amalga Filter features to narrow in on those patients who meet eligibility criteria Once researcher has IRB approval, give view with PHI. FOCUS ON 3738 IN THE FIRST SLIDE FILTER SLIDE FOCUS ON THE RIGHT SIDE LAST SCREEN FOCUS ON NUMBER 8 SOPMETHOW WITH LAST THREE COLUMNS OF INFO NEAR FUTURE! Launch a de-identified researcher view Get list of patients Use Amalga Filter features to narrow in on those patients who meet eligibility criteria Once researcher has IRB approval, give view with PHI. FOCUS ON 3738 IN THE FIRST SLIDE FILTER SLIDE FOCUS ON THE RIGHT SIDE LAST SCREEN FOCUS ON NUMBER 8 SOPMETHOW WITH LAST THREE COLUMNS OF INFO

10. Center for Clinical Data Analysis (CCDA) Provides periodic (monthly/quarterly) bulk data extracts (delimited/flat files, .xls): Preliminary, anonymous data for feasibility, grant applications and statistical sample-size estimates IRB-approved case-finding--for study enrollment (mailings, phone solicitation), chart review, and cohort/case-control studies Research data extracts - monthly/quarterly integrated extracts from EPR, POE, ORMIS, lab/PDS, billing systems, vaccination/transfusion/culture data, etc. 10

11. How CCDA works Email CCDA@jhmi.edu, cc: dthiema1@jhmi.edu; phone 410-955-65558 (Thiemann) For IRB-approved research: Provide full protocol + IRB approval Meet to discuss query methods, format Iterate, then schedule prod (email extracts, Jshare) Cost: $100/hour For non-IRB projects (exploratory analyses, QI) Same process, cost subsidized by ICTR/JHM Do NOT implicitly morph QI into IRB 11

12. The Basics: Getting Clinical Data Into a Registry Database Real work, not ad hoc/bootstrap Need $$$ and FTE(s) Smart analyst(s) who know database technology and understand (or can learn) nuances of the sources and content domain Hands-on PI management/guidance Statistical liason early, before database schema and ETL methods are set in stone 12

13. The Extract-Transform-Load process:Getting Clinical Data into Research DB Raw clinical/administrative data is useless for research Build an intermediate (staging) database Don’t do data management in SAS/Stata/Excel Data dictionary—derivation for each field Templated, tested, documented cleanup scripts/routines. Intermediate tables: Log each step/modification For each batch, be able to re-create data transform from scratch Version control, change control and documentation are vital Build data versioning into the database 13 OK, you have data—now what? In essence you’re building a mini-data-warehouse so you need some of the same processes and tools. Need to be able to re-create from scratch if process/criteria change. Most researchers use stat tools or Excel. Madness.OK, you have data—now what? In essence you’re building a mini-data-warehouse so you need some of the same processes and tools. Need to be able to re-create from scratch if process/criteria change. Most researchers use stat tools or Excel. Madness.

14. Transforming Data Raw data typically string (char/text) fields Unanalyzable characters (* < >, comments) still have meaning Put non-numeric data in separate field. Avoid numerical recoding (999) ~3% of pts have multiple/non-preferred MRNs Need 1-to-many link table Assays/reference ranges/coding changes Avoid using raw codes (CPT/ICD) in research db Map clinical codes to research terms Defer analytic assumptions. When recoding data, anticipate problems. Keep options open. 14

15. More Data Transform Challenges NEVER trust raw data. Learn business logic of source system. CPTs morph annually, internal complexity/redundancy Lab assays/reference/terms change Parsing is inherently unreliable Administrative names/groups change (clinic #s, departments). Duplicate-value problems (labs, orders) System-attribution source/datetime (POE, lab) Always run an aggregate (“group by” ) query to identify alternative names (eg lab name) and values (number, result) before transform. Otherwise you’ll miss something 15

16. Understanding Business Logic Trust but verify: Test coding accuracy Providers may habitually use imprecise/inaccurate diagnosis codes (especially in profee data) ICD9 procedure indications often a billing fiction Trained coders may make systematic errors Different content domains may have different standards (inpt vs outpt coders) Don’t infer/assume dependencies unless enforced by source system. Run min/max queries, aggregates, outer joins Confirm date ranges, data ranges, relative proportions by year Don’t assume that null rows actually are empty. Maybe the query missed something 16

17. JHM Clinical Data Landscape: Past, Present and Future Past : Babble of unintegrated systems EPR (antiquated technology, VSAM files, DB2) contains text, not queryable, analyzable data Present: EPR2020 (aka Amalga) –integrated data!! Has everything in EPR, plus JHCP, plus gradually adding data from clinical/departmental/administative systems (IDX CPTs, transfusion medicine, ORMIS, HMED, eADR, death registry, ad infinitum) Future: ? Epic, ? JHM Data Warehouse Epic: One system replacing all major JHM systems JHH timeline: 4+ years 17 Research queries for “EPR” data actually hit Amalga. Amalga has 3 main roles: Clinical/legacy repository, research repository, staging database for Epic +/- warehouse.Research queries for “EPR” data actually hit Amalga. Amalga has 3 main roles: Clinical/legacy repository, research repository, staging database for Epic +/- warehouse.

18. JHM Data Sources: Casemix Datamart Gold standard for JHM (non-profee) administrative data, including payer/insurance data Combines data from Keane (hospital charges), ADT (admission/discharge/transfer), HDM (ICD9 diagnosis + procedure coding), HSCRC (regulatory submissions) Not a true data warehouse; meager reconciliation Best source for length of stay, resource use, ICD9 diagnoses Outpatient ICD9s limited Has JHH + BMC + HCGH data 18

19. JHM Data Sources: IDX (profee) Gold standard for inpatient +outpatient CPT (profee charge) data ICD9 diagnosis data problematic Limitation: No data from non-faculty providers (private physicians, etc.) Difficult to query. Has a data warehouse, limited access. Early target for EPR2020/Amalga integration. 19

20. JHH Data Sources: SCM/POE Sunrise Clinical Manager/Provider Order Entry Replicated transactional database, difficult to query For registry purposes POE has large attribution/process challenges: Stutter-step orders, multiple alerts, imputed times Great source for inpatient meds, labs, physiologic monitor data No codified ICD9/Snomed/RxNorm data No outpatient data 20

21. JHH Data Sources: SCC/AIM Sunrise Critical Care (aka Emtek, Eclipsys). JHH ICUs + stepdown units + oncology AIM analytic database contains selected but comprehensive batch extract Sunsets as ICUs switch to POE ClinDoc Challenging to query. Lots of denormalized fields 21

22. JHH + BMC Data Sources: PDS PDS=Pathology Data Systems Includes lab, transfusion medicine, anatomic pathology, cytopath, John Boitnott’s death registry Lab data also available via EPR2020/Amalga and POE 22

23. BMC Data Sources: Meditech Shrink-wrapped, comprehensive inpatient + outpatient clinical + financial system Difficult for ad hoc research queries. Exports data to Datamart and EPR2020 BMC-JHH patient linkage doable but difficult, needs caution 23

24. JHCP Data Sources: GE Centricity All clinical + administrative data for JHCP clinics Largely opaque to research query; JHCP sometimes collaborates directly, especially for its physician/investigators Early target for EPR2020/Amalga integration Linkage challenges to BMC and JHH mrns 24

25. JHH Departmental Data:ORMIS + eADR/Medivision ORMIS: Operating Room Management Information System Mostly transactional scheduling/tracking/administrative data, limited clinical data. Has diagnoses, procedures, case start/stop times eADR/Medivision (anesthesia) still evolving, limited research data access Design challenges similar to legacy SCC critical-care system. 25

26. JHH Departmental Data: HMED (Emergency Department) Mostly opaque to research Replicated data hosted by Datamart 26