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DATA QUALITY IN HEALTH CARE: AN IMPORTANT CHALLENGE FOR HIE
SHIEC 2016
Michael Hogarth, MD, FACPMedical Director of Clinical Registries, UCDHSProfessor, Internal MedicineProfessor and Vice Chair, Dept. of Pathology and Laboratory Medicine
http://[email protected]
How much of $35B was spent on measuring and improving EHR data quality?
basic completeness and correctness is not a new problem
What is the “quality” of your EHR data today?
Population health ‘analytics’ must have good data quality!
Peeking inside a HIMSS Stage-7 EHR’s data
75% of recordshave unknown race?
Nobody is older than 85?
(1) Only have dx for pts. admitted after 1984?
(2) Someone is pre-admitted for 2020....
35 million procedures are “unknown” type?We have a procedure for someoneTo be admitted 12 years from now
Only 659,000 records have a diagnosis
(UCDHS repository profiling 2014)
UC-ReX: Number of urine pregnancy tests in 2015
?12 UPTs?
UPT
Non-deceased over 85=1.8M?
There are only 600,000 people over 85 in California!
The UCDHS “Clinical Registries”(what diabetics should be in your cohort?)
Hip Fracture Registry -- Delirium
What happened Dec 14?
Transfusions
Missing data
Missing data: lab results brought by patient, not entered
Data Quality: What is it?
Dimensions of Data QualityData Quality
CompletenessConsistency
Uniqueness Accuracy
Relevance (fit for use)
Data recorded is correct
All relevant data was recordedData recorded agrees with itself
Data are recorded once
Data recorded is sufficient to support a use/scenario
Fit for purpose in population analytics:
‘fit for purpose’ includes a purpose but also a cohort (a specific population of patients) requiring specific Measurement Value Set for a particular “metric”
Example – Type 2 DM Data (UK Transform project)
Example of data profiling for DM2
UK Transform Project
What is Data Profiling?
• Systematic and generalizable method of data quality assessment
• Provides insights into data quality• Provides a general “topography” for
your data – a 10,000ft view– Kind of data– The state of that data– Data density
Aim of Research
Data Density (UCDHS pScanner database)
Example Profiling
Rules
A Common Challenge:Who should “own” data quality?
A potential approach to data profiling
• Use a Common Data Model so you can leverage tools already available that run on that model
• The OMOP CDM“Observational Medical Outcomes Partnership”OHDSI’s ACHILLES
http://www.slideshare.net/KeesvanBochove/usage-of-open-source-software-for-real-world-data-analysis-in-pharmaceutical-companies-and-healthcare-institutions
Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES)
R, JSON, JavaScriptD3 JS library, PostreSQL
ACHILLES Person Context(UCDHS pScanner database)
Observations in the CDM (UCDHS pScanner database)
Selected Conditions (UCDHS pScanner database)
Conditions “Heat Map” (Asthma) (UCDHS pScanner database)
Condition map – Breast Neoplasm
Drug ”exposure” heat map (UCDHS pScanner database)
Questions?
Lake Tenaya, Yosemite National Parkhttps://en.wikipedia.org/wiki/Tenaya_Lake
