I had defined the Reliability Pulse of Monitoring Clinical Data's Rhythm of Quality in both my previous EMR (Patient Electronic Health Record Technology) company and in the Clinical Trial Research Organization and thought to share some of the Key Performance Indicators (KPI) we've used. I tried to incorporate elements related to clinical trials, data quality dimensions, compliance with GCP (Good Clinical Practice) principles, and the pharmaceutical/CRO environment.
Here are some examples of data quality KPIs (Key Performance Indicators) in the healthcare industry
Accuracy KPIs:
Percentage of patient diagnoses/medications matching clinical documentation
Percentage of claim entries aligning with medical coding standards
Number of dosage value errors caught per treatment cycle
Completeness KPIs:
Percentage of patient records with all required fields populated
Percentage of missed appointments with no-show reasons captured
Percentage of clinical trial data sets with zero missing data points
Consistency KPIs:
Number of variations in format/layout across electronic medical record (EMR) interfaces
Percentage consistency in code set/terminology usage across departments
Deviation from standard data dictionary definitions
Integrity KPIs:
Number of duplicate patient medical record instances
Percentage of referential integrity violations between systems
Number of data validation rule failures prior to ETL loads
Timeliness KPIs:
Percentage of patient vitals readings transmitted in real-time
Aged data: Number of months since last patient history review
Lag between lab results data availability vs defined service level agreements (SLAs)
Validity KPIs:
Number of values violating allowable value constraints
Percentage of data inputs failing validation rules/sanity checks
Count of records failing HIPAA data privacy rules
Here are some examples of data quality KPIs that are general to the clinical trial data:
Accuracy KPIs:
Percentage of data points that match source data (e.g. medical records, lab reports)
Number of transcription errors or data entry mistakes identified
Percentage of data queries resolved satisfactorily
Completeness KPIs:
Percentage of case report forms with all mandatory fields filled
Number of missing key data points (e.g. adverse events, concomitant medications)
Percentage of protocol deviations with reasons documented
Consistency KPIs:
Number of coding inconsistencies across sites/associates
Deviation from standard data formats, terminologies and conventions
Percentage of data differences between EDC and external data sources
Integrity KPIs:
Number of duplicate subject records identified
Rate of database errors, corruptions or constraint violations
Percentage of logical inconsistencies between related data points
Timeliness KPIs:
Duration between last patient visit and data entry
Percentage of serious adverse events reported within required timelines
Lag between site data transfer and availability in clinical database
Validity KPIs:
Number of values out of expected range (e.g. vitals, dosages)
Percentage of data failing edit checks or validation rules
Count of protocol deviations or violations against study criteria
Here are some examples of data quality KPIs relevant for a clinical research organization (CRO):
Accuracy KPIs:
Percentage of data transcribed accurately from source documents to EDC system
Number of data queries or issues identified during monitoring visits
Ratio of confirmatory SDV (source data verification) failures
Completeness KPIs:
Percentage of case report forms submitted with all expected data present
Number of missing critical safety data points (e.g. SAEs, con-meds)
Rate of protocol deviations with missing descriptors or reasons
Consistency KPIs:
Deviation from CDISC data standards across therapeutic areas
Number of terminology inconsistencies across sites/languages
Percentage of data transfer inconsistencies between EDC and clinical database
Integrity KPIs:
Number of duplicate subject records across systems/extracts
Rate of database errors, audit trail issues or edit check failures
Percentage of data failing cross-form, cross-visit integrity checks
Timeliness KPIs:
Average lag between patient visit and site data entry
Percentage of SAEs reported to sponsor within required timelines
Duration between site data transfer and availability in data repository
Validity KPIs:
Number of values out of expected range based on study plan/protocol
Rate of data failing up-front edit checks in EDC
Count of protocol deviations without justifications provided
By tracking and reviewing these KPIs, the CRO can proactively identify issues, monitor site performance, and ensure high quality clean data is being delivered to the sponsor pharmaceutical company. This allows them to uphold GCP standards, ensure subject safety, and maintain solid inspection readiness. By monitoring these clinical trial data quality KPIs, sponsors and CROs can ensure the reliability, traceability and quality of the underlying data that drives insights and submissions. High quality data is critical for patient safety, adherence to GCP principles, and regulatory compliance.
Sash Barige
Oct-29-2023
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