This document provides an outline for a presentation on electronic medical records (EMRs). It begins with defining the components of an EMR, including labs, admissions/discharge/transfer data, orders, radiology, notes, and billing. It then discusses the history and adoption of EMRs from the 1960s to present. The document reviews studies showing the effectiveness of EMRs in improving quality of care and achieving treatment standards. It also outlines how EMR data is structured in databases and data warehouses and describes common health data standards like ICD, CPT, LOINC, SNOMED, and HL7. The presentation covers meaningful use incentives and provides examples of using EMR data for research studies.

1. Biomedical
Informatics
1/20/12
John Sharp, MSSA, PMP, FHIMSS
Manager, Research Informatics
Quantitative Health Sciences

2. 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

3. Why EMRs

4. What is an EHR/EMR
Components

5. EMR Components
Lab ADT
Orders EMR Radiology
Notes
Billing

7. EMR by Workflow
Check in Vitals Medical Hx
Insurance Nursing Symptoms
Lab
Orders After Visit
Radiology
Prescriptions Summary
Results

8. Inpatient Workflow
Results
Admit
Orders Flowsheet Lab
ADT
Images
Orders D/C
Clinical
Procedures Notes Orders
Notes
Results Summary

10. Brief History of EMRs
And Adoption Trends

11. 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

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

13. 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

15. Effectiveness of EMRs

16. EMRsand Quality of Care

17. 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

18. Patricia Sengstack
CPOE Configuration to
Reduce Medication Errors,
JHIM, Fall 2010 - Volume 24(4)
26-32

19. EMR Alert Types
Clinical 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 guidelines for specific diagnoses, drug
treatment 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 Duplicate testing alerts, drug formulary guidelines
patient convenience

20. Unintended Consequences of
Health IT
A Look at Implementing CPOE
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.

21. Reducing Unintended
Consequences of Electronic
Health Records
http://www.ucguide.org/understand-identify/understand.html

22. Infrastructure
databases, warehouses

23. 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)

24. 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

25. EMR Standards and
Vocabularies
ICD9, ICD10 SNOMED-CT
CPT HL7
LOINC DICOM
UMLS

26. 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/

27. ICD9 Code Categorization
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)

28. CPT - procedures
 Current Procedural Terminology
 Includes everything from phlebotomy to major surgeries
 Number: 7800
 Added procedures as needed
 Controlled by the AMA

29. CPT Categories
1. Evaluation and Management  Examples
2. Anesthesiology 99253 Initial inpatient consultation
3. Surgery 11770 Excision of pilonidal cyst or sinus;
4. Radiology simple
5. Pathology and Laboratory 33512 Coronary artery bypass, vein
6. Medicine only, four coronary venous grafts
62270 Spinal puncture, lumbar, diagnostic
76498 Unlisted diagnostic radiographic
procedures
78205 Liver imaging (SPECT)
86900 Blood typing, ABO
93010 Electrocardiogram, routine ECG with
at least 12 leads; tracing only without
interpretation or report

30. 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

31. 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

32. 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

33. 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>

34.  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/

35. # 0x44 - Item 1: > (0x00080100, SH, "mV")
# 0x2 - Code Value OK > (0x00080102, SH,
DICOM "UCUM") # 0x4 - Coding Scheme
Code 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

36. UMLS
Unified 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

37. Meaningful Use
Of EMRs

38. 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

39. Meaningful
Use

40. 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

41. Use of EMRs in Research

42. 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

43. 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

44. 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

45. 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

46. 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

47. EMR Data in Research
Example
 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

48. Registry Validation

49. 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

50. 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

51. Upcoming Publication
 Book chapter on eResearch
 Editor, Rob Hoyt, University of West Florida
 http://www.uwf.edu/sahls/medicalinformatics/