Transcript 0. 2013-06_24_AcademyHealth-DataQualityAssessment_KAHN.ppt

Models for Data Quality and Data Quality
Assessment
Michael G. Kahn MD, PhD
Department of Pediatrics
University of Colorado Anschutz Medical Campus
[email protected]
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
accuracy
recency
accuracy
corrections made consistency
believability
timeliness
availability
misleading
reliability
trustworthiness
missingness
PPV
variation
validity
presence
quality
quality
validity
rate of recording
sensitivity
validity
Weiskopf & Weng (JAMIA 2012) Methods and Dimensions of EHR Data Quality Assessment: Enabling
Reuse for Clinical Research.
Weiskopf: Dimensions of
EHR DQ
I.
II.
III.
IV.
V.
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