Data Quality: Practices, Technologies and Implications Symposium on Data Science for Healthcare (DaSH) October 19 & 20, 2017 John Novak, Sr. Director of Architecture and Government Technologies

New Jersey Innovation Institute (NJII)

NJII Healthcare iLab 2010 – 2015: $23 Million - Regional Extension Center, formerly NJ-HITEC (9,000+ Physician and Specialist Network) #1 recognized in the country 2010 – Present: Health Information Exchange, Highlander Health Data Network (5-hospital healthcare data exchange network) 2011 – Present: CMS Certified Data Registry (300+ Measures covering over 40 specialties for over 4,500 providers) 2012 – Present: Population Health Service Provider (Serving 17 DSRIP Hospitals, 5 ACOs, and 500,000 lives) 2015 – 2017: $5 Million – ONC HIE Interoperability Grant as State-Designated Entity NJ Master Person Index (MPI), Healthcare Provider Directory (HPD), Common Key Service (CKS), State Registry Gateway for Immunizations History and Statewide ADT Notification Service and Transitions of Care Use Cases 2015 – 2019: $50 Million - CMS Practice Transformation Network Awardee (Transform 11,500 providers to achieve performance targets and MIPS / APM scoring and preparedness 2016 – 2018: $1.5 Million- Medicaid IAPD Onboard and implement ToC for Medicaid Providers onto existing HIEs for the achievement of Meaningful Use

What we Found Incomplete demographics Lack of mapping/normalization Public Health Registries (not all elements are covered) HIO/IDNs inconsistent or not captured Lack of mapping/normalization Set up and never monitored for quality Stale information Purpose of connections Meaningful Use, was a connection not much about what/how/why

What are the Data Quality Issues in Healthcare? Standards Lack of standard requirements The one standard is there is no standard Consistency What and is collected How is it collected Data exists in a variety of different places Across the continuum of care Data exists in many different formats Structured/Unstructured What are the data quality issues in healthcare?

Standards Lack of standards Conformance Patient identifiers not a set of defined, there are recommendations Public Health Willingness to collect data Conformance Variety of standards HL7 v2.x, v3.0, DICOM, CCDA & FHIR Even with in these standards conformance is a challenge RxNorm, SNOMED, ICD-9 & ICD-10 Mapping across vocabularies

Lack of Consistency Terminology Workflows Technology Hemoglobin A1C (lab test) Hemoglobin A1C Hgb A1C, Hb A1c Glycohemoglobin H and H (Hemoglobin and Hematocrit) Common fields Workflows Technology Platforms Maturity of Organizations

Format/Interoperability Data exists in many different formats Discreet data Transcribed data Structured/Unstructured data Formats PDFS, images, video, audio recordings Discrepancies Based on the source systems For Interoperability Standards Consistency Format

Moving Data Even a basic transaction can have its challenges, when scaled it be comes significantly more complex.

Notification Example

Where is the Data?

Sample Scorecard

Moving the Needle Standards Validation Regulatory levers Organizational compliance Regionalization Validation Human validation and intervention Technical validators