WORKING WITH EHR DATA FROM DUKEUNIVERSITY HEALTH SYSTEM: WHAT IS IT

AND HOW DO I DO IT?

Benjamin A. Goldstein PhD, MPHben.goldstein@duke.edu

Department of Biostatistics & BioinformaticsSchool of Medicine

Duke University

May 13th, 2020

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TALK AGENDA

What are Electronic Health RecordsWhat are EHR data elementsTypes of studies we can do with EHR dataSome analytic considerations with EHR dataA case study in an EHR based studyOptions for accessing Duke EHR data

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WHAT ARE ELECTRONIC HEALTH RECORDS?

“An Electronic Health Record (EHR) is an electronic version of apatient’s medical history, that is maintained by the provider overtime” (Centers for Medicaire & Medicaid Services (CMS) website)HITECH Act was part of the 2009 stimulus geared to incentivizethe use of EHRsSynonyms: Electronic Medical Record (EMR), Patient HealthRecord (PHR)

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GROWTH OF EHR USAGE

https://dashboard.healthit.gov/quickstats/quickstats.php

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https://dashboard.healthit.gov/quickstats/quickstats.php

EHR VENDORS

https://dashboard.healthit.gov/quickstats/pages/

FIG-Vendors-of-EHRs-to-Participating-Professionals.php

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https://dashboard.healthit.gov/quickstats/pages/FIG-Vendors-of-EHRs-to-Participating-Professionals.phphttps://dashboard.healthit.gov/quickstats/pages/FIG-Vendors-of-EHRs-to-Participating-Professionals.php

FRONT END OF EHRS

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BACK END OF EHRS

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COMPLEXITY OF EPIC BACKEND

Chronicles - ~95,000 Data ElementsData stored immediately

Clarity - ~17,000 Tables & 125,000 columns

Data stored overnight

Caboodle - 19 Tables & 76 Dimensions

Disease Specific Registries- Diabetes- Afib- Etc

Analytic Tools- Provider Dashboards- Predictive models- Etc

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DATA ELEMENTS

Patient DemographicsEncounters (Outpatient/Inpatient)DiagnosesProceduresLab ResultsVital SignsMedicationsSocial HistoryProvider InformationRadiological ResultsDoctor Notes

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DEMOGRAPHICS

Patients have a single ID that follows them across all encounters- medical record number (MRN)Basic information: Age, Sex, Race/Ethnicity that is typically staticTime varying elements include: Payer, address

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ENCOUNTER TYPE

Encounters have an encounter ID that links the encountercontext to what happened (diagnoses, tests etc.)Three Basic Encounters:

Outpatient (AV - Ambulatory Visit)Inpatient (IP)Emergency Department (ED)

Other types of encounters can include telephone consults,emails etc.

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CONTEXTUALIZING INFORMATION FOR ENCOUNTERS

When someone seen (i.e. time stamps for arrival and departure)Who the patient saw (i.e. provider specialty, provider type)Where the patient was seen (i.e. clinic location, facility type)What happened (i.e. vital signs, labs taken, diagnoses made)We don’t have good information on Why — diagnoses don’toften relate to “chief complaint”

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INTERNATIONAL CLASSIFICATION OF DISEASES (ICD)CODES

Hierarchical system to code all diagnoses that are made during ahealth encounterIn 2015, the US switched to the ICD-10 system (previouslyICD-9)ICD-9 had ∼13,000 unique codes, ICD-10 has ∼68,000Since these are used as billing codes, the codes can bemanipulated to increase billingCodes don’t always represent the primary concern

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STRUCTURE OF ICD-10 CODES

Myocardial Infarction:I21⇒ Acute Myocardial Infarction

Subsequent numbers designate location of event, e.g. I21.01⇒ MIof left main coronary artery

I22⇒ Subsequent MII23⇒ Complications of MIhttps:

//www.icd10data.com/ICD10CM/Codes/I00-I99/I20-I25

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https://www.icd10data.com/ICD10CM/Codes/I00-I99/I20-I25https://www.icd10data.com/ICD10CM/Codes/I00-I99/I20-I25

ROLLING UP ICD-CODES

Dealing with 68,000 unique codes is not realistic or efficientAgency for Healthcare Research and Quality (AHRQ) developedClinical Classification Software (CCS) systemAllows researchers to roll codes up to appropriate levels

https://www.hcup-us.ahrq.gov/toolssoftware/ccs/

AppendixCMultiDX.txt

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https://www.hcup-us.ahrq.gov/toolssoftware/ccs/AppendixCMultiDX.txthttps://www.hcup-us.ahrq.gov/toolssoftware/ccs/AppendixCMultiDX.txt

CURRENT PROCEDURAL TERMINOLOGY (CPT) CODES

CPT is coding system for what happened during an encounter,e.g., surgeries, x-rays, etc.∼ 10,000 in useAlso tied to reimbursementsSimilar systems for organizing CPTs as ICDs

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MEDICATIONS

EPIC has > 100 medication-related tablesMedications are often organized as:

PrescribedAdministeredReconciliation

For prescribed medication dosages may be messyLike diagnoses and procedures medications can become overlygranular

RxNorm is a system for rolling up medications into hierarchieshttps://mor.nlm.nih.gov/RxNav/search?searchBy=String&

searchTerm=acetaminophen

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https://mor.nlm.nih.gov/RxNav/search?searchBy=String&searchTerm=acetaminophenhttps://mor.nlm.nih.gov/RxNav/search?searchBy=String&searchTerm=acetaminophen

LABORATORY TEST RESULTS

Laboratory tests (along with vitals) differentiate EHR data fromadministrative dataThere may be multiple tests panels used which can be labeleddifferentlyModern systems have standardized the nomenclature oflaboratory tests

Duke has a catalog of the laboratory tests used:https://testcatalog.duke.edu/

Typically will see time stamps for when test was ordered andresultedAn analytic concern is that these measurements are irregularlycaptured across encounters

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https://testcatalog.duke.edu/

VITALS SIGNS

Most encounters will capture blood pressure, weight andtemperatureIn the hospital vitals may be documented every couple hoursICU monitors can capture very dense data: minute-by-minute oreven waveformData will typically be stored in long running tallies called“flowsheets”

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SOCIAL HEALTH

Data such as smoking status, drug and alcohol use, employmentstatus, marital status, etc., may be reported, but is frequentlyunreliableSocioeconomic status typically doesn’t exist but proxies can beused via primary payer or neighborhood addressThere is a growing emphasis on capturing patient reportedoutcomes (PROs)

Food insecurity, PROMIS, pain, depression inventories

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OTHER DATA ELEMENTS

Problem ListsDate stamped indicators for when someone has different conditions- not always reliable

Admission-Discharge-Transfer (ADT) DataTime stamps are recorded every time a patient moves in thehospital

Provider DataInformation on who a patient saw and interacted with

User DataEvery time someone signs into EPIC a log is generated

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UNSTRUCTURED DATA

Structured Data refer to quantitative data in a ready-to-analyzeformatGrowing emphasis on incorporating unstructured data whichrequire some processingExamples include:

NotesImagesGenetic data

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ORGANIZING DATA

DATA LAKESLoose organization of dataAble to maintain all data elementsNo explicit linkage between data elementsCan be complicated to work with

DATA MARTSStructured data in a relational formatEasier to access dataDesigned for particular use case(s)Results in loss of informationHigher maintenance cost

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NEED FOR DATA MODELS

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PCORNET DATA MODEL

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WORKING WITH DATA MODELS

ADVANTAGESSimpler data organization, making it easier to accessUniform set of decisions so that data are consistent acrossinstitutions

DISADVANTAGESA general loss of granularity

Not all data elements fit within the data modelMany measures are grouped together

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WHY WE WANT TO USE EHR DATA FOR CLINICALRESEARCH

Data Readily AvailableOften 100,000’s of PatientsInformation collected over a variety of fieldsAbility to study many different clinical questionsRepresentative population

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WHY WE MAY Not WANT TO USE EHR DATA FORCLINICAL RESEARCH

DATA ARE NOT ORGANIZED FOR RESEARCHData exist in disparate placesAll patients have different pieces of informationObservational Data

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EHR VS CLINICAL TRIALS DATA

RCT Data EHR DataWhy are data Data are collected for Data are collected forcollected? the study clinical careWhen are data Pre-planned study visits Random clinicalcollected? encountersWho/Where are Research staff enter Entered by cliniciansdata entered? into CRFsWhat data are Same data for Only information deemedentered? all patients important by clinicianHow are data Statistician pulls Informaticist extractsextracted? from RedCap dataHow are studies Top-down - start with study Bidirectional design -designed? and collect relevant data start with study but assess

available data

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WHAT WE CAN DO WITH ELECTRONIC HEALTHRECORDS

1 Risk PredictionNear term prediction - Risk of in-hospital mortalityLong(er) term risk - 30 Day Revisit

2 Population HealthHealth Service Utilization - Assessment of high utilizersDisease Epidemiology - Experience of incident diabetes in DurhamCounty

3 Comparative Effectiveness Research (CER)Retrospective Studies - Assessment of community intervention fordiabeticsProspective Studies - Point of care randomization, Pragmatic Trials

4 Association AnalysesRisk factors for disease - Phenome Wide Association StudiesData mining - Drug-Drug interactions

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EHR BASED STUDIES

Retrospective ProspectiveRisk I Returning to Hospital I Implement alert forPrediction 30 days after discharge Readmissions RiskIntervention I Compare Medical vs I Point of CareAssessment Surgical Treatment RandomizationPopulation I Experience of Incident I Screening forHealth Diabetics Diabetes

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ADVANTAGES OF STUDIES BASED ON EHR DATA

POPULATION HEALTHEHRs often capture data on particular communities of interest

Estimated that ∼ 80% of Durham County residents receive healthservice at Duke University Health System affiliated providers

COMPARATIVE EFFECTIVENESSOpportunity to see real-world usage of medical treatmentsCan assess both adoption of therapies and effectiveness &safety of therapies

RISK PREDICTIONContains granular information capturing patients’ clinical stateDirect pipeline to implement models into clinical practice

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WHO IS NEEDED FOR AN EHR BASED STUDY?

Biostatistician Informaticist

Epidemiologist/Clinician

Data Manipulation

Study Design

Data Extraction

Analysis

Research Question

Variable Definition

Collaborative Clinical Research

Clinical Research

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SUMMARY POINTS

EHR systems are complex database systems to store patienthealth dataFor clinical researchers there are a lot of appealing reasons towant to work with EHR dataThe raw data elements often need to be processed - typicallythrough hierarchical structuresOrganizing the data into data models can aid analytics

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Analytic Challenges with EHR Data

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CREATING ANALYTIC DATA SETS

Don’t work directly off datamarts but create analytic data setsCreating analytic datasets requires many decisions, most ofwhich can’t be tested via sensitivity analysesNeed to define granularity of the analysis (encounter, patient,etc.)Larger projects may need datamart sub-extracts to generatemultiple analytic data sets

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THREE WAYS EHR DATA DIFFER FROMTRADITIONAL CLINICAL DATA

1 We don’t have everything we want2 Outcomes are not defined - need to phenotype data3 Data are irregularly and potentially densely captured

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CHALLENGE 1:WE DON’T HAVE EVERYTHING WE WANT

Patients may seek care at multiple facilitiesChanges in standard of careMissing information on when individuals are healthyMost social health information is not recorded or reliableCannot expect death to be reliably captured

Most people don’t die in the hospitalEHRs have only 20-50% sensitivity

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LINKING EHR DATA

Data from other facilities (PCORNet)Claims: Center for Medicaire & Medicaid Services (CMS)Mortality: National Death Index (NDI) & Social Security DeathIndex (SSDI)Genetic DataGeocoded Information: housing, environmental, censusPersonal Tracking Data: wearables, sensors

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MISSING DATA IN EHR DATA SETS

Data in EHRs are typically not missing but not collectedData are Not Missing At Random (NMAR)

Typical imputation strategies would not be appropriateWe’ve termed this Informed Presence

Inclusion of proxy measures:Missingness categoriesNumber of previous encountersNumber of times a lab was tested or code was documented

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ADDRESSING INCOMPLETENESS VIA DESIGN

Inpatient analyses are typically well contained but outpatientanalyses can lead to loss to follow-upDefine local patient population

Live in the catchment of the health systemRequire a certain a number of primary care appointments beforeeligible for study

Contextual and proxy information can be linked inNeighborhood for SESClaims data for additional encountersNDI/SSDI for death

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CHALLENGE 2NEED FOR COMPUTABLE PHENOTYPES

EHR data do not have direct information on disease states“Problem Lists” exist but are not always reliableWe can develop algorithms to define disease statesThe algorithms typically have high positive predictive value andspecificity but not always high sensitivity

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ISSUES OF DATA DEFINITION:WHAT IS A DIABETIC?

Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, Hammond WE, Califf RM, Spratt SE. A Comparison of Phenotype Definitions for Diabetes Mellitus. J Am Med Inf Assoc 2013 (epub ahead of print). http://www.ncbi.nlm.nih.gov/pubmed/24026307

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ISSUES OF DATA DEFINITION:WHAT IS A DIABETIC?

ICD-9 250.x0 Expand. ICD-9ICD-9 & 250.x2 (249.xx, 357.2, Abnormal Diabetes250.xx (exclude type I) 362.0x, 366.41) HbA1c Glucose OGTT Meds

ICD-9 250.xx XCMS CCW X* X*NYC A1c Registry XMeds XDDC X X X X X XSUPREME-DM X* X* X X X X•eMERGE X* X X X

* Distinction between Inpatient and Outpatient Visits

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DEFINITION DIFFERENCES

ANY TYPE2 TYPE2unsp

0.6

0.7

0.8

0.9

1.0

0.00 0.01 0.02 0.03 0.04 0.01 0.02 0.03 0.04 0.05 0.01 0.02 0.03 0.04 0.051−Specificity (FPF)

Sen

sitiv

ity (

TP

F)

AuthoritativeSource 250 A1C CCW DDC4 MED NW SUP A1C_OR_MED

Diabetes Validation Results faceted by Endpoint

Spratt et al. JAMIA 201745 / 74

ADDITIONAL PHENOTYPING CHALLENGES

Death: Need to include external dataDisease Incidence: Need to apply ‘burn-in’ periodsCensoring: Need to apply ‘burn-out’ periods

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CHALLENGE 3:DATA ARE BOTH LONGITUDINAL AND CROSS-SECTIONAL

EHR data consists of cross-sections of longitudinal dataMost data are stored in datamarts that cover fixed periods of time

Need to use methods for longitudinal data to model updatingexposures

We most often use time-varying Cox ModelsMost analyses don’t account for a patient’s trajectory - just mostrecent value

Since data are a cross-section no notion of time 0Define “burn-in” periods to define eligibilityUse “burn-out” periods to define censoring

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MULTIPLE MEASUREMENTS PER PERSON

OPPORTUNITIESGet to observe patient’s evolving health statusMore frequent visits than a typical longitudinal studyDenser visit information

CHALLENGESVisits are irregularly spacedDifferent ways to aggregateYou don’t know which data are not captured in your dataset

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ASSESSING DATA QUALITY

Weiskopf et al. EGEMS 2017

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TAKE HOMES

Turning EHR data into analytic data is an involved process thatrequires many choicesWhen analyzing data we may sit downstream of these choicesand not get to test their impactThe temporal structure of EHR data opens up the need fordifferent analytic methods

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CASE STUDY: ENVIRONMENTAL IMPACTS ON ASTHMAEXACERBATION IN CHILDREN

Research Question: How do the built and natural environmentsimpact children with asthmaStudy Population: Children (ages 5-18) who have a diagnosisof asthma and live in Durham CountyExposure: Weather, air quality, distance to highway, etc.Outcome: Asthma exacerbationsData Source: Duke EHR data linked with publicly availabletemporal-spatial data

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ANALYTIC STEPS

1 Abstract all children with asthma within Duke EHRDefined computable phenotype based on diagnosis codes andmedication prescription

2 Identify period of time children had address in Durham County3 Capture asthma exacerbations based on encounter type (e.g.,

outpatient, urgent care, ED, inpatient), diagnosis code andprescription for rescue medication

4 Link in exposure data based on patient address (spatial factors)and dates of service (temporal factors)

5 Extract other clinical data such as comorbidities, BMI, andlaboratory values

Data elements used: Demographics, Diagnoses, Medications,Laboratory Test, Vitals, Address, Service Location, Geo-Coded Data

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DISTANCE TO HIGHWAY AND ASTHMA EXACERBATION

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PREDICTING DAILY EXACERBATIONSBASED ON ENVIRONMENTAL FACTORS

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OTHER ANALYSES

Protective effect of Well-Child visitsFactors associated with medication escalationBuilt environment’s impact on asthma exacerbationImpact of COVID exposure and stay at home order on kids withasthma

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Accessing EHR Data at Duke

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EHR DATA AT DUKE

Duke University Health System (DUHS) consists of 3 hospitals (2in Durham, 1 in Raleigh) and a network of outpatient andspecialty clinicsThe EHR system is managed by Duke Health TechnologySolutions (DHTS)

They are responsible for meeting both operations and researchdata needs

Duke finished switching to an integrated EPIC based system inAugust 2013Before this different departments had their own EHR systemsLegacy (pre-2014) data are available but may be less reliable

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EHR DATA ARE Big DATA

Since 2014 there have been:>1.7 million Unique Patients>400,000 Inpatient Encounters>27 million Outpatient Encounters

These patients each have diagnoses, vital signs, labs, medicationorders, etc.

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POPULATION HEALTH AT DUKE

DUHS is the primary provider inDurham County

Estimated that ∼ 80% of DurhamCounty residents get health servicesat DUHS

One “hole” is Lincoln CommunityHealth Clinics which services anunderserved population

They share an EHR system withDUHS but special permission isneeded to access their data.

Durham Neighborhood Compass isa CTSI initiative to use Duke EHRdata to inform about public health inDurham County https://compass.durhamnc.gov/en/

compass/DIABETES_TOTAL/tract

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https://compass.durhamnc.gov/en/compass/DIABETES_TOTAL/tracthttps://compass.durhamnc.gov/en/compass/DIABETES_TOTAL/tracthttps://compass.durhamnc.gov/en/compass/DIABETES_TOTAL/tract

THE NEED FOR PRE-REQUIREMENTSWith Great Power Comes Great Responsibility

Most forms of EHR data contain Protected Health Information(PHI)While data are available for minimal risk research purposes, it isimportant to protect the identity of patients, many of whom aremembers of the local communityData can exist as:

Fully Identified - names, DOB, Address, etcLimited Data - minimal PHI (e.g. Dates of Service)Deidentified - no PHI

One should use the minimal amount of PHI necessary forresearch purposes

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SOME PRE-REQUIREMENTS

CITI IRB TrainingAn active IRB approved by the School of MedicineData stored in a secured server (PACE)Depending on degree of direct access a DHE (Duke Health)Account

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PROTECTED ANALYTICS COMPUTING ENVIRONMENT(PACE)

HIPAA/FISMA Compliant Virtual Machine (Windows and Linuxenvironments)You can easily get data in but need to go through an honestbroker to get data outCannot connect to internet but preloaded with R, Python, SAS,GIS etc.Each user has resources equivalent to a laptop

Can connect to GPU Machines through Microsoft Azure

Directory structure is based off the approved IRB protocol -effective for project teams to share data and code

https://pace.ori.duke.edu/

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https://pace.ori.duke.edu/

3 POINTS OF ACCESS(IN REALITY MORE)

Self ServiceGUI Based Tools (DEDUCE)Code Based Tools (CRDM)

Expert Assisted Access - ACE Fee For Service

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3 POINTS OF ACCESS(IN REALITY MORE)

Self ServiceGUI Based Tools (DEDUCE)Code Based Tools (CRDM)

Expert Assisted Access - ACE Fee For Service

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DEDUCE

GUI based system to query data - easiest way to access dataBuild ”cohort” via hierarchical queries and indicate whichelements to extractOrder of operations can impact the way cohort is builtGUI based cannot “run” jobs repeatedlyData go back to the ’90s but data pre-2014 (pre-EPIC data) maybe less reliable

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CLINICAL RESEARCH DATAMART (CRDM)

Newer offering - launched earlier this year with support fromCHDI, TDH, CTSI, DHTShttps://sites.duke.edu/crdm

Designed to support clinical research and development of clinicalregistriesMeet the principles that:

Data pulls need to reproducibleProvide code based access to broader range of analystsMost data queries don’t need the most up-to-date dataMost studies use many of the same data elements (ICDs, Labs,Medications, etc.)Be linkable to other data assets such as department specificdatamarts and publicly available contextual data (e.g.neighborhood data, weather etc.)

Active development of new data tables to meet researcherneeds, e.g. family linkage table

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https://sites.duke.edu/crdm

ORGANIZATION OF CRDM

Data organized into an extension of the PCORnet Common DataModel (CDM)

PCORnet CDM contains most standard data elements (labs,medication, diagnoses, etc.)Added additional tables such as providers, encounter details etc.Most useful when you don’t need details of the hospital encounters(e.g. no patient bed flow)

Only contains data starting in 2014 (when Duke switched toEPIC system)Data are refreshed and QA’d quarterly (working on daily refresh)Data are in a Oracle database that can be accessed from withinPACE via R/Python/SAS

Since code-based system reproducible data pulls

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CRDM STRUCTURE

PCORnetData Model

Electronic Health Records

Data

All patientsStructured data

elements, curated to match a common

data modelMetadataCode base

Project 1 Project 2 Cohort

Department/Specialty Datamarts

e.g., Stork,Transplant,Cardiology

Other datasetse.g., claims data

Data Sidecars

IRB approval

Geospatial Data

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ACCESSING THE CRDM

1 Obtain IRB/RPRR approval to access EHR data2 Get PACE account3 Fill out RedCap to register project and get initial access

https://redcap.duke.edu/redcap/surveys/?s=CFKLE9EKLY

4 Set up your initial Database Connection within PACE

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https://redcap.duke.edu/redcap/surveys/?s=CFKLE9EKLY

SOME CRDM USE CASES

Population HealthMapping pediatric diseases in Durham CountyDeveloping a phenotype for patients with NASH & NAFLD

Comparative EffectivenessEvaluation of opioid prescriptions in post-surgical settingUse and effectiveness of biologic therapies for patients with asthma

Epidemiological StudiesMultifactorial analysis of pediatric asthma outcomesLipoprotein A testing in patients with cardiovascular disease risk

Predictive ModelingDeveloping a risk score for 30-day readmissionsPrediction of healthcare utilization for patients with Type 2 diabetes

RegistriesHealth outcomes and healthcare utilization patterns in pediatricpatients with epilepsyGrowth trajectories in pediatric patients who are obese

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ACE FEE-FOR-SERVICE

Component of Duke Health Technology Solutions (DHTS)Custom data pulls and data services (e.g. building outdatamarts, APIs etc.)Cost and time is largely dependent on complexity of the datadomains requestedMost useful when you need unstructured data elements ordetailed hospital flow data

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COMPARING SERVICES

DEDUCE CRDM ACE FFSAccess: GUI Interface Direct SQL Query Work with Informaicist

Reproducibility: Moderate High High(need to query in (need to reengagesame way) informaticist)

Automatable Queries: No Yes Yes

Data Elements: Most Structured PCORnet CDM + AllData Elements Duke Specific Side Cars

Data Refresh: Daily Quarterly Daily(Working towards daily)

Cost: Free Currently Free $(Creating CostRecovery Model)

Time to Access Data: Always Available Always Available Variable

Ideal use: Cohort creation Reproducible data Need to accessw/out needing to code extraction “harder” data elements

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NAVIGATING THE SYSTEM:DUKE DATASHARE CATALOG

https://medschool.duke.edu/research/

data-science-information-technology/data/

data-services-catalog72 / 74

https://medschool.duke.edu/research/data-science-information-technology/data/data-services-cataloghttps://medschool.duke.edu/research/data-science-information-technology/data/data-services-cataloghttps://medschool.duke.edu/research/data-science-information-technology/data/data-services-catalog

NAVIGATING THE SYSTEM:EHR ENABLED RESEARCH SUPPORT GROUP (EERS)

Investigators can request a freeconsultation to review theinvestigator’s request in order to:

Define a technical approach for aproject timelineConnect the investigator withDuke resourcesRefine budget estimates

https://ctsi.duke.edu/

ehr-enabled-research-support-eers

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https://ctsi.duke.edu/ehr-enabled-research-support-eershttps://ctsi.duke.edu/ehr-enabled-research-support-eers

CONCLUSIONS

The Duke EHR system contains a wealth of information onpatient healthThe data present opportunities to study population health withinDurham CountyEHR data can be quite complex both structurally andepidemiologicallyThere are multiple ways to access EHR data at Duke

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