See the 2,456 pharmacies on the National E-Pharmacy Platform
EMR Components and Workflow
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
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
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
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
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
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
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
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
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
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
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/