Ensuring the Quality of Data for Multi-Site Health Services Research - PowerPoint PPT Presentation

Ensuring the Quality of Data for Multi-Site Health Services Research Bradley G Hammill Duke School of Medicine & Duke Clinical Research Institute brad.hammill@duke.edu

Quality Across the Clinical Data Flow Electronic Study- Clinical Research Health Specific Encounter Database Record Dataset Many possible data quality intervention points

The PCORnet Experience  Distributed Research Network – 13 Clinical Data Research Networks (CDRNs) comprising 80+ sites – Use of Common Data Model (CDM) – Primarily electronic health record data – Control of data is local, not central – Queries are used to generate summary results for return Electronic PCORnet Study- Clinical Health Common Specific Encounter Record Data Model Dataset

PCORnet Research Process Step 1: Research Needs • Purpose : State needs for addressing study objective(s) Step 2: Business Specifications • Purpose : Translate high-level clinical concepts to low(er)-level clinical concepts to obtain from the data. Fill in any gaps posed by the research question. Step 3: T echnical Specifications • Purpose : Provide detailed instructions for defining concepts based on the PCORnet CDM Step 4: SAS Query • Purpose : Generate the SAS query to be sent to sites for execution “ PCORnet Query Programming Guidelines”

ADAPTABLE Trial  A spirin D osing: A P atient-centric T rial A ssessing B enefits and L ong-Term E ffectiveness – Pragmatic clinical trial – Demonstration project of PCORnet – Leveraging EHR data – 20+ sites “…designed to reflect ‘real - world’ medical care by recruiting broad populations of patients, embedding the  General query strategy trial into the usual healthcare setting, and leveraging data from health systems – 1-2 beta tests (limited sites) before official distribution to produce results that can be readily used to improve patient care.”

Step 1: Research Needs  Among the enrolled population, describe medical history and prevalent conditions at baseline – Ex. Prior cardiac revascularization  Among the enrolled population, summarize the rate of concurrent medication usage, at baseline and throughout the course of the trial – Ex. Aldosterone antagonist  Among the enrolled population, compare event rates between treatment groups – Ex. Bleeding w/transfusion

Step 2: Business Specifications  Define population – Enrolled patients  Define relevant time periods – History: 1 year prior to enrollment – Follow-up: Up to 2.5 years following enrollment – Medication reporting: At baseline & every 6 months  List of specific procedures that make up a concept – Prior cardiac revascularization PCI, CABG, other? – Transfusion Whole blood, red blood cells, other?

Step 2: Business Specifications  List specific drug names and ingredients for each medication – Aldosterone antagonist Brand names: Inspra, Aldactone Ingredients: eplerenone, spironolactone  List specific diagnoses that make up a concept – Bleeding Intracranial hemorrhage Gastrointestinal hemorrhage Other?  Other important things – Medication usage Prescription or dispensing that covers a date

Step 3: T echnical Specifications  Codes, codes, codes (+ some logic)  Some things to keep in mind – Do not make assumptions about the data (esp. based on your site’s data or experience with claims data) – Do specify comprehensive code lists – Do use validated algorithms where possible, but… – Do not limit yourself to validated algorithms – Do pre-test all queries – Do have a plan for site variability in data & results – Be flexible

Step 3: T echnical Specifications

Coding: Transfusions  Specific transfusions (whole blood, red blood cells) Issues: ICD-9-CM (Px) 99.03, 99.04  Study period crosses ICD-10 ICD-10-PCS implementation date / 01-Oct-2015 3023[0|3][H|N|P]1, 3024[0|3][H|N|P]1, 3025[0|3][H|N|P]1, 3026[0|3][H|N|P]1  While at most sites other/non-spec HCPCS P9010, P9011, P9016, P9021, P9022, P9038, P9039, transfusions are ~25% of all transfusions, some sites are as high as 75% P9040, P9051, P9054, P9057, P9058  Other or non-specific transfusions  Annual transfusion rates in pre-test query (general CV population) were about 3%? ICD-9-CM (Px) 99.0x (except above) What to do with sites with <0.5%? ICD-10-PCS [Many]  What about revenue center codes? HCPCS P90xx (except above) CPT 36430

Coding: Major Bleeding  Specific diagnosis codes ( too many to list ) Issues: ICD-9-CM (Dx)  Study period crosses ICD-10 ICD-10-CM implementation date / 01-Oct-2015  Additional logic  Annual major bleeding rates in pre-test query (general CV population) were about – Type of encounter: Inpatient 5%? No concerning site outliers. – Diagnosis type: Primary  Validation studies have shown this to be a – Timing: Between enrollment date & follow-up end date less-than-reliably coded outcome  Some sites in PCORnet do not have primary diagnosis indicators for IP encounters. How to handle?

Coding: Aldosterone Antagonist  Specific medication codes ( too many to list ) Issues: Dispensing / National Drug Code (NDC)  Use both medication tables? Not all sites Prescribing / RxNorm concept unique identifier (RxCUI) have both.  Dispensing window  Don’t forget to include discontinued codes. – D ISPENSE _D ATE > D ISPENSE _D ATE + D ISPENSE _S UP  Which RxCUI term types to include?  Prescribing window (?) o Need to know dose form? – R X _O RDER _D ATE > R X _E ND _D ATE o Need to know strength? – R X _S TART _D ATE > R X _E ND _D ATE  How to handle missing end date & days – R X _O RDER _D ATE > R X _O RDER _D ATE + R X _D AYS _S UPPLY supply information in prescribing table? – R X _S TART _D ATE > R X _S TART _D ATE + R X _D AYS _S UPPLY – R X _S TART _D ATE | R X _O RDER _D ATE in a defined period

Coding: Other issues Study population – Trials = Enrolled – Observational = Loyalty cohort? Relevant time periods – Medical history look-back – “Current” lab values Procedures in the CDM – Sites have made many different decisions – Ex: Injected medications, E&M codes, any many more

Dealing with Data & Coding Variability  Understand the diversity – Data characterization results – Study-specific queries fit-for-use  Have a plan /fit fawr yoos/ – Write algorithms and specifications “defensively” phrase – Include multiple concept specifications Typically used to describe data that is capable of meeting specific study requirements  Test queries, then re-test Can also be used to describe sites  Acknowledge the reality of the data that are capable – Potentially select sites based on initial query results – Show site-specific results as part of the report

Summary  Data quality requires planning  Data quality results from attention to detail  Data quality means acknowledging when data are less than perfect  Data quality means dealing with data variability T HANK YOU ! Q UESTIONS ?