Models for Data Quality and Data Quality Assessment

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Presentation transcript:

Models for Data Quality and Data Quality Assessment Michael G. Kahn MD, PhD Department of Pediatrics University of Colorado Anschutz Medical Campus Michael.Kahn@ucdenver.edu Funding was provided by a contract from AcademyHealth/Electronic Data Methods Forum, AHRQ 1R01HS019912-01 (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute).

Disclosures Work based on EDM Forum commissioned paper:

Defining Data Quality using “Fitness for Use” “Data are of high quality if they are fit for their intended uses in operations, decision making, and planning. Data are fit for use if they are free of defects and possess desired features.” – Joseph Juran Quality linked to intended use (context) Not all tasks require highly accurate data The same data may have different data quality with different intended uses

Weiskopf: DQ Dimensions from the Literature completeness correctness concordance plausibility currency accessibility accuracy agreement recency corrections made consistency believability timeliness availability misleading reliability trustworthiness missingness PPV variation validity presence quality rate of recording sensitivity Weiskopf & Weng (JAMIA 2012) Methods and Dimensions of EHR Data Quality Assessment: Enabling Reuse for Clinical Research.

Weiskopf: Dimensions of EHR DQ Completeness: Is a truth about a patient present in the EHR? Correctness: Is an element that is present in the EHR true? Concordance: Is there agreement between or within elements in the EHR, or between or within elements in the EHR and another data source? Plausibility: Does an element in the EHR makes sense in light of knowledge about what that element is measuring? Currency: Is an element in the EHR a relevant representation of the patient state at a given point in time? Weiskopf & Weng (JAMIA 2012) Methods and Dimensions of EHR Data Quality Assessment: Enabling Reuse for Clinical Research.

DQ Dimensions 1. Completeness 2. Consistency 3. Correctness Accuracy Reliability 4. Timeliness 5. Relevance 6. Usability 7. Security + definitions + measures

How to measure data quality? Lisa Schilling, University of Colorado Daniella Meeker, Rand Corporation Patrick Ryan, Observational Medical Outcomes Partnership Jeff Brown, Mini-Sentinel