Big Data: Compiling Data Across Ehrs

Big Data: Compiling Data Across EHRs

Tim Burdick MD MBA MSc

Dartmouth-Hitchcock Health Assistant Professor Primary Care Clinic Geisel School of Medicine Heater Road Community and Family Medicine Lebanon, NH Biomedical Data Science The Dartmouth Institute Nothing to disclose. Does he expect Forget the me to document stethoscope, kid. It’s the exam in the all about EHR data! computer?! Where are we going today?

Why digitize medical records?

EHRs under the hood

Linking data in PBRNs

Projects in PBRNs

The future of truly big data

Discussion Always start with the WHY?

Why make information electronic? Environment to Vision

• This is our patient, Fred

Environment ENVIRONMENT • Measure; create structure; codify

INDEX • Fred: HA1C = 7.8;BMI = 38 Data DATA • Look for patterns; understand relationships CONTEXT/ PAST RULES • Fred has Type 2 diabetes caused by his obesity Information INFORMATION • Understand patterns; find predictability MEANING/ MODEL • Patients w/ DM have lower quality life years & cost more Knowledge KNOWLEDGE • Apply patterns; understand principles/goals

INSIGHT • DM guidelines improve Quadruple Aim outcomes FUTURE

Wisdom WISDOM • Use compassion; apply values VALUE/ PURPOSE Vision • Effective treatment of patients with DM is good for all VISION

Carpenter & Canaday (2004) The Bad and The Ugly

ANNALS OF MEDICINE NOVEMBER 12, 2018 ISSUE WHY DOCTORS HATE THEIR COMPUTERS Digitization promises to make medical care easier and more efficient. But are screens coming between doctors and patients? By Atul Gawande And the Good

Millions of patients worldwide - 83% report feeling more more in control of their health - 25% report a correction to their medical record https://www.opennotes.org/

Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes

26,500 inpatient encounter measured adherence to CDS - Cost of care 7% lower - Length of stay 6% lower

Heekin et al. Am J Manag Care. 2018;24(8):361-366 In the Beginning there was...who is this man?

Lawrence L. Weed MD UVM College of Medicine Professor of Medicine, and Community Medicine, Director, PROMIS Laboratory (Problem Oriented Medical Information System)

Touchscreen of the Megadata Terminal. c. 1977 Grand Isle Clinic, VT – PROMIS (c. 1979)

Microwave dish Health Insurance Portability and Accountability Act (1996) Title II: Administrative Simplification Provisions

● Implemented 2005 for claims submissions ● Standard electronic codes for encounters (ICD-9, ICD-10) ● Standard data transmission methods ● Security requirements ● National Provider Identification (NPI)

HIPAA, not HIPPA (a female hippo?) What drove $36 billion dollar investment in health IT?

The US Housing Market

And the US economy generally American Reinvestment & Recovery Act (ARRA) 2009

Healthcare Information Technology for Econonomic and Clinical Health (HITECH)

● Federal government reimburses hospitals and physicians for EHR adoption ● Must demonstrate “Meaningful Use” of the EHR ● Incremental requirements each year ● Clicking for dollars Standards

● ICD – International Classification of Diseases (1900) ● SNOMED CT – Systematized Nomenclature of Medicine Clinical Terms (1965) ● LOINC – Logical Observation Identifiers Names and Codes (1994) ● RxNorm (2001)

SNOMED for scalding of the left index finger Common Data Models (CDM)

Enter heart rate in EHR user interface

EHR stores in database tables

Database Extraction, Transformation, Load (ETL)

• identify what data to pull out of EHR tables

• map to Observational Medical Outcomes Project (OMOP) data model

• merge data from multiple EHR into combine database with CDM MRN VS_Date VS_time VS_measure_source VS_value

34823948-1 2020_01_24 0819 HR_monitor 92

40294820-3 2019_11_12 2354 SBP_left_arm 135

93092835-7 2018_07_15 1343 DBP_right_arm 82

83458230-7 2017_10_28 1005 Ox_Sat_finger 91

00299128-4 2019_08_01 1739 wt_kg_bed 78

MRN Name_Last Name_First DOB Zip_Home

83458230-7 Flintstone Fred 457_BC 99731

34823948-1 Flintstone Wilma 462_BC 99731

93092835-7 Burdick Tim 1981 03755

Dartmouth Coop Charts

Then

Functional Assessments - Standardization - Paper forms - Manual data extraction & entry - Subsample of patients or charts - Data sizes in the hundreds - Time-intensive Dartmouth Coop Charts

Now

LOINC codes ● Formerly Oregon Community Health Information Network ● 1 shared EHR (Epic) since 2000 ● 500+ organization partners

○ > 100 FQHCs ● 47 states ● >10,000 providers ● > 500,000 unique patients per month ● >400 staff in 35 states ● Teach and lead clinical quality improvement to member organizations ● $53 Million in external research dollars ● Hundreds of peer-review publications in top journals PCORI CDRNs

● 9 clinical research data networks ● Regional aggregation of core data from many EHR systems ● EHR, Claims, Social Determinants, Geocoded, Patient Reported Outcomes, Insurance status, pharmacy dispense, test results, immunization registries pcornet: meta-network

 66 million patients ADVANCE CDRN  550 million encounters - OCHIN-affiliated clinics - Fenway Health clinics  6 billion lab results - Oregon Health & Science University clinics

 1 billion prescription dispenses pcornet projects

States with PCORI Rare Disease Project leads ● Do antibiotics in early childhood increase risk of obesity?

○ 700,000 children

○ 10-year longitudinal study

● Sleeve gastrectomy is safer than Roux-en-Y gastric bypass (RYGB)

○ 33,000 patients

○ 5-year study DARTNet (Distributed Ambulatory Research in Therapeutics Network)

. Started 2007 (AAFP, Univ Colorado Family Medicine, AHRQ, private tech company) . Developed ELT tools for any EHR . Map data to OMOP CDM . 12 PBRNs contracted services ● 5 million patients ● 12 million encounter per year  3 rural: WWAMI, Appalachian, Upstate NY ● 5 billion data points

. Value-add services

 Quality improvement analytics and reports

 Meaningful Use /PIP registries DARTNet Academic Partners

Academic Partners •Northwestern University •Ohio State University •University of Alabama at Birmingham •University at Buffalo •University of California San Diego •University of Colorado Denver •University of Kansas •University of Minnesota •University of Texas San Antonio •University of Vermont •University of Washington

NIH Clinical Translational Science Award centers (~ Dartmouth Synergy) © Epic Systems, Verona, WI Single Alcohol Screening Question

● 77 weeks of data ● 200+ clinics ● 95,537 screenings ● 24.5 million visits ● Now you have data about alcohol use patterns across large population

Correlate w use of services, chronic disease, mental health, etc Big, Really Big Data

 Genomics

 Imaging studies (MR, pathology)

 Social Determinants

 Exposome

 Proteome

 Microbiome genetics

 Wearables and implantables Discussion

What EHRs are you using? (Poll) eCW? Epic? athenahealth? Allscripts? Amazing Charts? Greenway? NextGen? Cerner? Other?

How are you leveraging data for quality improvement in your practice?

Research studies using your practice data from EHR?

What is keeping you from using your data more effectively?