W ORKING WITH EHR D ATA FROM D UKE U NIVERSITY H EALTH S YSTEM : W HAT IS IT AND H OW D O I DO IT ? Benjamin A. Goldstein PhD, MPH ben.goldstein@duke.edu Department of Biostatistics & Bioinformatics School of Medicine Duke University May 13 th , 2020 1 / 74
T ALK A GENDA What are Electronic Health Records What are EHR data elements Types of studies we can do with EHR data Some analytic considerations with EHR data A case study in an EHR based study Options for accessing Duke EHR data 2 / 74
W HAT A RE E LECTRONIC H EALTH R ECORDS ? “An Electronic Health Record (EHR) is an electronic version of a patient’s medical history, that is maintained by the provider over time” (Centers for Medicaire & Medicaid Services (CMS) website) HITECH Act was part of the 2009 stimulus geared to incentivize the use of EHRs Synonyms: Electronic Medical Record (EMR), Patient Health Record (PHR) 3 / 74
G ROWTH OF EHR U SAGE https://dashboard.healthit.gov/quickstats/quickstats.php 4 / 74
EHR V ENDORS https://dashboard.healthit.gov/quickstats/pages/ FIG-Vendors-of-EHRs-to-Participating-Professionals.php 5 / 74
F RONT E ND OF EHR S 6 / 74
B ACK E ND OF EHR S 7 / 74
C OMPLEXITY OF EPIC B ACKEND Caboodle - 19 Tables & 76 Dimensions Disease Specific Registries Analytic Tools - Diabetes - Provider Dashboards - Afib - Predictive models - Etc - Etc Clarity - ~17,000 Tables & 125,000 columns Data stored overnight Chronicles - ~95,000 Data Elements Data stored immediately 8 / 74
D ATA E LEMENTS Patient Demographics Encounters (Outpatient/Inpatient) Diagnoses Procedures Lab Results Vital Signs Medications Social History Provider Information Radiological Results Doctor Notes 9 / 74
D EMOGRAPHICS Patients have a single ID that follows them across all encounters - medical record number (MRN) Basic information: Age, Sex, Race/Ethnicity that is typically static Time varying elements include: Payer, address 10 / 74
E NCOUNTER T YPE Encounters have an encounter ID that links the encounter context 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. 11 / 74
C ONTEXTUALIZING I NFORMATION F OR E NCOUNTERS 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’t often relate to “chief complaint” 12 / 74
I NTERNATIONAL C LASSIFICATION OF D ISEASES (ICD) C ODES Hierarchical system to code all diagnoses that are made during a health encounter In 2015, the US switched to the ICD-10 system (previously ICD-9) ICD-9 had ∼ 13,000 unique codes, ICD-10 has ∼ 68,000 Since these are used as billing codes, the codes can be manipulated to increase billing Codes don’t always represent the primary concern 13 / 74
S TRUCTURE OF ICD-10 C ODES Myocardial Infarction: I21 ⇒ Acute Myocardial Infarction Subsequent numbers designate location of event, e.g. I21.01 ⇒ MI of left main coronary artery I22 ⇒ Subsequent MI I23 ⇒ Complications of MI https: //www.icd10data.com/ICD10CM/Codes/I00-I99/I20-I25 14 / 74
R OLLING U P ICD-C ODES Dealing with 68,000 unique codes is not realistic or efficient Agency for Healthcare Research and Quality (AHRQ) developed Clinical Classification Software (CCS) system Allows researchers to roll codes up to appropriate levels https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ AppendixCMultiDX.txt 15 / 74
C URRENT P ROCEDURAL T ERMINOLOGY (CPT) C ODES CPT is coding system for what happened during an encounter, e.g., surgeries, x-rays, etc. ∼ 10,000 in use Also tied to reimbursements Similar systems for organizing CPTs as ICDs 16 / 74
M EDICATIONS EPIC has > 100 medication-related tables Medications are often organized as: Prescribed Administered Reconciliation For prescribed medication dosages may be messy Like diagnoses and procedures medications can become overly granular RxNorm is a system for rolling up medications into hierarchies https://mor.nlm.nih.gov/RxNav/search?searchBy=String& searchTerm=acetaminophen 17 / 74
L ABORATORY T EST R ESULTS Laboratory tests (along with vitals) differentiate EHR data from administrative data There may be multiple tests panels used which can be labeled differently Modern systems have standardized the nomenclature of laboratory tests Duke has a catalog of the laboratory tests used: https://testcatalog.duke.edu/ Typically will see time stamps for when test was ordered and resulted An analytic concern is that these measurements are irregularly captured across encounters 18 / 74
V ITALS S IGNS Most encounters will capture blood pressure, weight and temperature In the hospital vitals may be documented every couple hours ICU monitors can capture very dense data: minute-by-minute or even waveform Data will typically be stored in long running tallies called “flowsheets” 19 / 74
S OCIAL H EALTH Data such as smoking status, drug and alcohol use, employment status, marital status, etc., may be reported, but is frequently unreliable Socioeconomic status typically doesn’t exist but proxies can be used via primary payer or neighborhood address There is a growing emphasis on capturing patient reported outcomes (PROs) Food insecurity, PROMIS, pain, depression inventories 20 / 74
O THER D ATA E LEMENTS Problem Lists Date stamped indicators for when someone has different conditions - not always reliable Admission-Discharge-Transfer (ADT) Data Time stamps are recorded every time a patient moves in the hospital Provider Data Information on who a patient saw and interacted with User Data Every time someone signs into EPIC a log is generated 21 / 74
U NSTRUCTURED D ATA Structured Data refer to quantitative data in a ready-to-analyze format Growing emphasis on incorporating unstructured data which require some processing Examples include: Notes Images Genetic data 22 / 74
O RGANIZING D ATA D ATA L AKES Loose organization of data Able to maintain all data elements No explicit linkage between data elements Can be complicated to work with D ATA M ARTS Structured data in a relational format Easier to access data Designed for particular use case(s) Results in loss of information Higher maintenance cost 23 / 74
N EED FOR D ATA M ODELS 24 / 74
PCOR NET D ATA M ODEL 25 / 74
W ORKING WITH D ATA M ODELS A DVANTAGES Simpler data organization, making it easier to access Uniform set of decisions so that data are consistent across institutions D ISADVANTAGES A general loss of granularity Not all data elements fit within the data model Many measures are grouped together 26 / 74
W HY W E W ANT TO USE EHR D ATA FOR C LINICAL R ESEARCH Data Readily Available Often 100,000’s of Patients Information collected over a variety of fields Ability to study many different clinical questions Representative population 27 / 74
W HY W E M AY Not W ANT TO USE EHR D ATA FOR C LINICAL R ESEARCH D ATA ARE NOT ORGANIZED FOR RESEARCH Data exist in disparate places All patients have different pieces of information Observational Data 28 / 74
EHR VS C LINICAL T RIALS D ATA RCT Data EHR Data Why are data Data are collected for Data are collected for collected? the study clinical care When are data Pre-planned study visits Random clinical collected? encounters Who/Where are Research staff enter Entered by clinicians data entered? into CRFs What data are Same data for Only information deemed entered? all patients important by clinician How are data Statistician pulls Informaticist extracts extracted? from RedCap data How are studies Top-down - start with study Bidirectional design - designed? and collect relevant data start with study but assess available data 29 / 74
W HAT W E C AN D O W ITH E LECTRONIC H EALTH R ECORDS Risk Prediction 1 Near term prediction - Risk of in-hospital mortality Long(er) term risk - 30 Day Revisit Population Health 2 Health Service Utilization - Assessment of high utilizers Disease Epidemiology - Experience of incident diabetes in Durham County Comparative Effectiveness Research (CER) 3 Retrospective Studies - Assessment of community intervention for diabetics Prospective Studies - Point of care randomization, Pragmatic Trials Association Analyses 4 Risk factors for disease - Phenome Wide Association Studies Data mining - Drug-Drug interactions 30 / 74
EHR B ASED S TUDIES Retrospective Prospective Risk ◮ Returning to Hospital ◮ Implement alert for Prediction 30 days after discharge Readmissions Risk Intervention ◮ Compare Medical vs ◮ Point of Care Assessment Surgical Treatment Randomization Population ◮ Experience of Incident ◮ Screening for Health Diabetics Diabetes 31 / 74
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