Analysis of Existing Data to Promote Continuous Learning and Improvement May 22nd, 2019

OptumLabs Founded as a partnership between Optum and Mayo Clinic in January 2013 OptumLabs  Data platform Clinical, academic, and policy partners provide scientific and medical knowledge, and enable translation to improve patient care

OptumLabs Data Warehouse (OLDW) Commercial claims 136M Electronic medical records 74M New in 2018: 7 years of Medicare FFS claims Medicare FFS claims 71M Consumer 50M Numbers as of June 30, 2018 Slide owner: John Brillante More than 250 million de-identified lives Combining Medicare FFS data with the OLDW data further diversifies our linked data asset

OptumLabs Data Warehouse High-level data schema: Linked, de-identified data Health Plan-Affiliated Data Administrative Claims Asset Lab test results Health risk assessment Socioeconomic status Enrollment Pharmacy claims Supplemental oncology data SSA death master file Insurance benefits design and CDHPs Physician claims Facility claims OPTUMLABS DATA WAREHOUSE Brief overview of assets in OptumLabs, but that is not the purpose of this talk Example of the type of data that data owners have All data is linked and then de-identified PRIOR to research We can’t make every data element available in a giant data lake — the data are available in certified, de-identified research views Note that asset is always being enhanced Mention that while OptumLabs cannot, other parts of Optum can re-identify in order to build registries, conduct patient/physician surveys, conduct chart review, etc. We follow same path that Brande mentions (Brande Yeast, Eli Lilly, speaking before me). We try to answer the question with existing assets for all of the reasons Brande mentioned (speed, costs, generalizability), but if we have to build on to the existing asset, we do. Encounters Signs, disease and symptoms Consumer data Non-affiliated claims (not linkable) American Hospital Association Observations and clinical findings Medication orders and administrations Area health resource file Future addition to warehouse Future addition to warehouse PanTher asset (EHR-derived)

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High Value Healthcare Collaborative (HVHC) Mission A provider learning network committed to improving healthcare value through data and collaboration. To accomplish this, we: Measure, innovate, test, and continuously improve value- based care Rapidly disseminate and facilitate adoption of proven high value care models across HVHC Members and beyond Advocate for policy and payment models that support sustainable high value healthcare

HVHC Member Facilities & HVHC CMS FFS Claims Data Coverage Intermountain Healthcare Hawaii Pacific Health Providence St Joseph Health Baylor Scott & White Health Mayo Clinic Mercy Sentara Healthcare Northwell Health Dartmouth-Hitchcock Source: CMS FFS claims data through 2018Q2

HVHC Data Trust CMS Claims Data 2012- 2018 All beneficiaries in all HVHC sites and referral regions 34M+ FFS beneficiaries Member-Submitted All-Pop Administrative Data 2014- 2018 All patients with an encounter at an HVHC site From billing data: demographics, diagnoses, procedures, etc. 15M+ patients, 105M+ encounters Member-Submitted Cohort-Specific Clinical Data Patients who meet criteria for specific HVHC projects From EMR data: lab values, procedure times, etc. 100K+ orthopedic (hip, knee, spine); 28K+ sepsis Linked Data Sets Features Highly secure Updated quarterly Customizable Reporting accessible via Member portal with interactive filters and drill-downs Uses CMS measures: reduced lag comparative reporting and insights Value analysis, research, and planning Clinical care demographic and descriptive statistics QI monitoring Updated April 2019