Biomedical Informatics

1/20/12John Sharp, MSSA, PMP, FHIMSSManager, Research Informatics

Quantitative Health Sciences

Outline

1. What is an EMR/EHR? – components

2. History and adoption of EMRs

3. Effectiveness of EMRs

4. Infrastructure - databases, warehouses

5. Standards

6. Meaningful Use

7. Use of EMR data in Research

Why EMRs

What is an EHR/EMRComponents

EMR Components

EMR

Lab

NotesBilling

ADT

OrdersRadiolo

gy

EMR by Workflow

Check inInsurance

VitalsNursing

Medical HxSymptoms

LabRadiology

Results

OrdersPrescriptio

ns

After VisitSummary

Inpatient Workflow

AdmitADT

OrdersFlowshe

et

OrdersNotes

Results

Procedures

ClinicalNotes

ResultsLab

Images

D/COrders

Summary

Brief History of EMRsAnd Adoption Trends

Early History of EMRs

Earliest were in the 1960s

Began with lab systems and ADT (Admission, Discharge, Transfer)

1970s and 1980s – slow progress as technologies improved to include separate systems for nursing, physicians notes, OR scheduling. Epic Systems founded in 1980s

1990s – better integration of systems, first web-based systems

EMR Adoption

Hsiao et al. (2010); CDC/NCHS, National Ambulatory Medical Care Survey.

Wiring the Health System

Theoretical arguments – better coordination of care through information sharing

Empirical Rationale – Using health information technology to improve quality and efficiency of care – VA and Kaiser as examples of early EMR adopters

---------------------------------David Blumenthal, MD, MPP – former director of the Office of the National Coordinator for Health IT in NEJM, 12/15/11

Effectiveness of EMRs

EMRs and Quality of Care

EMR and Quality of Care

Achievement of composite standards for diabetes care was 35.1 percentage points higher at EHR sites than at paper-based sites 

Achievement of composite standards for outcomes was 15.2 percentage points higher

 Across all insurance types, EHR sites were associated with significantly higher achievement of care and outcome standards and greater improvement in diabetes care

Better Health Greater Cleveland

Patricia SengstackCPOE Configuration to Reduce Medication Errors,JHIM, Fall 2010 - Volume 24(4)26-32

EMR Alert TypesClinical Decision Support

Target Area of Care Example

Preventive care Immunization, screening, disease management guidelines for secondary prevention

Diagnosis Suggestions for possible diagnoses that match a patient’s signs and symptoms

Planning or implementing treatment

Treatment guidelines for specific diagnoses, drug dosage recommendations, alerts for drug-drug interactions

Followup management Corollary orders, reminders for drug adverse event monitoring

Hospital, provider efficiency Care plans to minimize length of stay, order sets

Cost reductions and improved patient convenience

Duplicate testing alerts, drug formulary guidelines

Unintended Consequences of Health IT

Pittsburgh

Specific order sets designed for critical care were not created.

Changes in workflow were not sufficiently predicted, resulting in a breakdown of communication between nurses and physicians.

Orders for patients arriving via critical care transportation could not be written before the patients arrived at the hospital, delaying life-saving treatments.

Changes, unrelated to the CPOE system, were made in the administration and dispensing of medication that further frustrated the clinical staff, for example: At the same time the CPOE system was installed, the satellite

pharmacy serving the neonatal ICU was closed and medications had to be obtained from the central pharmacy, delaying treatment.

Emergency prescriptions were required to be preapproved and all drugs were moved to the central pharmacy.

A Look at Implementing CPOE

Reducing Unintended Consequences of Electronic Health Records

http://www.ucguide.org/understand-identify/understand.html

Infrastructuredatabases, warehouses

EMR Databases

Relational vs. Non- relational

Microsoft SQL - relational

Oracle - relational

MySQL – open source

Intersystems Cache – Epic (object database which can handle large volumes of transactional data)

Data Warehouses

Also called Clinical Data Repositories

Collection of all clinical data for reporting, research, quality improvement, clinical decision support

Requires interfaces with multiple systems, data mapping and harmonization

Enables data mining, extraction of data sets

EMR Standards and Vocabularies

ICD9, ICD10

CPTLOINC

SNOMED-CT

HL7DICOMUMLS

ICD9 – ICD10

15,000 Diagnoses

Grouped by disease category

Drive the Problem List in most EMRs

Also used for billing

Transition to ICD10 68,000 codes– by July 2013– Cleveland Clinic using a product by IMO to ease the transition. Already in use for problem list and encounter diagnoses.

https://www.cms.gov/ICD9ProviderDiagnosticCodes/

http://www.who.int/classifications/icd/en/

1. INFECTIOUS AND PARASITIC DISEASES (001-139)2. NEOPLASMS (140-239)3. ENDOCRINE, NUTRITIONAL AND METABOLIC DISEASES, AND IMMUNITY

DISORDERS (240-279)4. DISEASES OF THE BLOOD AND BLOOD-FORMING ORGANS (280-289)5. MENTAL DISORDERS (290-319)6. DISEASES OF THE NERVOUS SYSTEM AND SENSE ORGANS (320-389)7. DISEASES OF THE CIRCULATORY SYSTEM (390-459)8. DISEASES OF THE RESPIRATORY SYSTEM (460-519)9. DISEASES OF THE DIGESTIVE SYSTEM (520-579)10. DISEASES OF THE GENITOURINARY SYSTEM (580-629)11. COMPLICATIONS OF PREGNANCY, CHILDBIRTH, AND THE PUERPERIUM

(630-679)12. DISEASES OF THE SKIN AND SUBCUTANEOUS TISSUE (680-709)13. DISEASES OF THE MUSCULOSKELETAL SYSTEM AND CONNECTIVE TISSUE

(710-739)14. CONGENITAL ANOMALIES (740-759)15. CERTAIN CONDITIONS ORIGINATING IN THE PERINATAL PERIOD (760-779)16. SYMPTOMS, SIGNS, AND ILL-DEFINED CONDITIONS (780-799)17. INJURY AND POISONING (800-999)

ICD9 Code Categorization

CPT - procedures

Current Procedural Terminology

Includes everything from phlebotomy to major surgeries

Number: 7800

Added procedures as needed

Controlled by the AMA

CPT Categories

1. Evaluation and Management

2. Anesthesiology3. Surgery4. Radiology5. Pathology and

Laboratory6. Medicine

Examples99253 Initial inpatient consultation11770 Excision of pilonidal cyst or

sinus; simple33512 Coronary artery bypass, vein

only, four coronary venous grafts62270 Spinal puncture, lumbar,

diagnostic76498 Unlisted diagnostic

radiographic procedures78205 Liver imaging (SPECT)86900 Blood typing, ABO93010 Electrocardiogram, routine

ECG with at least 12 leads; tracing only without interpretation or report

LOINC

Logical Observation Identifier Names and Codes terminology

LOINC codes are intended to identify the test result or clinical observation

Provides a set of universal names and ID codes for identifying laboratory and clinical test results

Number: 100,000

Includes: name of the component, timing of the measurement, type of sample (serum, urine, etc.), scale of measurement

Used by almost all lab systems and EMRs

Managed by the Regenstrief Institute, Inc. at University of Indiana

SNOMED-CT

Systematized Nomenclature of Medicine-Clinical Terms

Comprehensive clinical terminology

Over 300,000 concept codes

Helpful in software development to map data to medical concepts

Also includes relationships between concepts, such as, knee ‘is a’ body part

HL7 – Health Level 7

A messaging language for health care

Used for real-time data transfer from one system to another - interoperability

Used here for sending data from Lab system to Epic

Standards that permit structured, encoded health care information of the type required to support patient care, to be exchanged between computer applications while preserving meaning

HL7.org

HL7 example - ADT

MSH|^~\&|GHH LAB|ELAB-3|GHH OE|BLDG4|200202150930||ORU^R01|CNTRL-3456|P|2.4<cr>

PID|||555-44-4444||EVERYWOMAN^EVE^E^^^^L|JONES|19620320|F|||153 FERNWOOD DR.^

^STATESVILLE^OH^35292||(206)3345232|(206)752-121||||AC555444444||67-A4335^OH^20030520<cr>

OBR|1|845439^GHH OE|1045813^GHH LAB|15545^GLUCOSE|||200202150730|||||||||

555-55-5555^PRIMARY^PATRICIA P^^^^MD^^|||||||||F||||||444-44-4444^HIPPOCRATES^HOWARD H^^^^MD<cr>

OBX|1|SN|1554-5^GLUCOSE^POST 12H CFST:MCNC:PT:SER/PLAS:QN||^182|mg/dl|70_105|H|||F<cr>

For imaging

Designed to ensure the interoperability of systems

Used to: Produce, Store, Display, Process, Send, Retrieve, Query or Print medical images and derived structured documents as well as to manage related workflow.

http://medical.nema.org/

# 0x44 - Item 1: > (0x00080100, SH, "mV") # 0x2 - Code Value OK > (0x00080102, SH, "UCUM") # 0x4 - Coding Scheme Designator OK > (0x00080103, SH, "1.4") # 0x4 - Concept group revision OK > (0x00080104, LO, "millivolt") # 0xA - Code Meaning OK > (0x003A0212, DS, "1") # 0x2 - Sensitivity correction factor OK > (0x003A0213, DS, "0") # 0x2 - Channel baseline OK > (0x003A0214, DS, "0") # 0x2 - Channel Time skew OK > (0x003A021A, US, 0x0010) # 0x2 - Bits per sample OK > (0x003A0220, DS, ".05") # 0x4 - Filter low frequency OK > (0x003A0221, DS, "100") # 0x4 - filter high frequency OK

DICOMCode

UMLSUnified Medical Language System

 Integrates and distributes key terminology, classification and coding standards to promote more effective and interoperable biomedical information systems and services, including electronic health records

100 source vocabularies in the UMLS Metathesaurus

Includes SNOMED-CT, LOINC, others

From the National Library of Medicine

Meaningful UseOf EMRs

EMR Incentives $44,000 over five years for eligible

professionals

Must show meaningful use

Must be an approved EMR

Program to assist small practices -REC

Most health systems have or are in process

Meaningful Use

Meaningful Use

Eligible Hospital Meaningful Use Table of Contents

Core and Menu Set Objectives

https://www.cms.gov/EHRIncentivePrograms/Downloads/Hosp_CAH_MU-TOC.pdf

Use of EMRs in Research

Basis for Research

Integrating research workflow into the EMR Clinical trial patient calendar

A rich source of clinical data – data mining

Data is from real clinical situations, unlike highly controlled clinical trials

But is messy – not always easy to compare groups, clinical events are not in a standard sequence

Missing data

How to Begin

Research question

Define cohort – inclusion, exclusion criteria

Data elements to be included

Statistical tests to be utilized – descriptive statistics or more

Modify cohort or data elements

Analyze results

Retrospective Cohort Studies

Descriptive

Typically utilizes discrete data elements in the EHR

Internal validation recommended – comparing a random sample of patients in the database with what is documented in the front end of the EHR

Example: Development and Validation of an Electronic Health Record–Based Chronic Kidney Disease Registry

Prospective Cohort Studies

Prospective in the sense that measurements are taken from the EMR at specific time points

Time points need to be within a given range, for instance, 1 year after time zero plus or minus one month

Missing data may eliminate patients from the cohort

Example: Underdiagnosis of Hypertension in Children and Adolescents

Prospective Studies

Begin collecting data from the EMR at a specific time point

May also include manual data collection

Example – biomarker for infection in the ICU

EMR Data in ResearchExample

Chronic Kidney Disease Registry

Established 2009

60,000 patients from the health system

Cohort – Adults with two eGFRs less than 60 within 3 months, outpatient results only, or diagnosis of CKD

http://www.chrp.org/pdf/HSR_12022011_Slides.pdf

Registry Validation

Validation Results

Our dataset’s agreement with EHR-extracted data for documentation of the presence and absence of comorbid conditions, ranged from substantial to near perfect agreement.

Hypertension and coronary artery disease were exceptions

65% sensitivity

50% negative predictive value

Registry Results

2011

5 out of 5 abstracts accepted to American Society of Nephrology annual meeting

Three papers accepted to nephrology journals

NIH grant

Partnerships with other research centers

Upcoming Publication

Book chapter on eResearch

Editor, Rob Hoyt, University of West Florida

http://www.uwf.edu/sahls/medicalinformatics/