February 25, 2003: I. Sim EMRs Medical Informatics Ida Sim, MD, PhD February 25, 2003 Division of General Internal Medicine, and Program in Biological

February 25, 2003: I. Sim EMRsMedical Informatics

Ida Sim, MD, PhD

February 25, 2003

Division of General Internal Medicine, and Program in Biological and Medical Informatics

UCSF

Electronic Medical Records

Copyright Ida Sim, 2003. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


• Electronic medical records (EMR)– clinical benefits

• reduction in medical errors, prescription errors• supports quality improvement programs

– research benefits• “Frankly, one of the biggest attractions to LastWord

is going to be a boon to clinical research. Information will be accessible in a much more uniform and complete way.” Haile Debas, Daybreak, Feb. 2, 2001

• Are EMRs what they promise?

Background


Field Trip

• March 11, 1:30 to 3pm• Palo Alto Medical Foundation

– state of the art electronic medical record• same one that Kaiser is spending $2.8 billion on

– the promise and the reality


• Understand key properties of useful electronic medical records and data warehousing– free vs. coded entry– importance of a standardized clinical vocabulary

• Understand implications of database technologies on clinical research

• Be familiar with basic concepts in data security and privacy

Learning Objectives


• Sample Study– a single-institution outcomes research question

• Electronic Medical Records (EMRs)– relational databases– vocabulary

• Data Warehousing• Security and Privacy

Outline


• Retrospective analysis• Compare 1 year re-admission rate for acute MI for

– diabetics admitted with acute MI, discharged • on -blockers• not on -blockers

• First acute MI in 1999 to 2001, followup to 2002

An Outcomes Research Project


• Find diabetics admitted with AMI‘1999 to‘2001• Find whether D/C’ed on -blocker• For these patients, find all re-admissions in the year

following the index MI– identify re-admissions that were for acute MI

• Analyze– predictor = -blocker status– primary outcome = acute MI readmission rate– secondary outcome = length of stay (LOS)

Study Steps


• Data needed– admission: Admission Discharge Transfer system– diabetes diagnosis: chart, HgbA1C– MI diagnosis: chart, troponins, EKG readings

• or just trust coding of admission diagnosis?

-blocker usage: orders, pharmacy

• Existing (legacy) systems– claims, pharmacy, ADT, lab, xray, med record, etc

Health System Minnesota: 50 paper, 50 computer

200,000 lives, 460 physicians



Data Needed for -Blocker Study


Pros Cons

ChartReview

ElectronicMedicalRecord

Data Collection Method


• EMR provides individual patient data for– real-time clinical care – reimbursement (eg for E&M coding)– see table for major functionality dimensions

• Clinical workstation includes interfaces to– practice management systems– pharmacy benefit management– knowledge resources (e.g., WWW, guidelines)

• “EMRs” range from flat file, text-based systems to full-featured workstations

What is an EMR?


8 Types of EMR Functionality

Viewing Electronic viewing of chart notes, problem and medication lists, dischargesummaries, laboratory results, and radiology results.

Documentation Entry of visit note and other information into the EMR, whether throughdictation or direct keyboard entry.

Order Entry Electronic physician order entry of drug prescriptions, laboratorytests, radiology studies, or referrals.

Care Planningand Management

Managing patients in disease management programs, such as for asthma orcongestive heart failure

Patient-Directed Patient education materials; web-based education modules, self-diagnosisalgorithms, patient-viewing of EMR data, and e-mail with care providers

Billing and OtherAdministrative

Determination of insurance eligibility, assistance with visit level coding,management and tracking of referrals.

PerformanceReporting

Quality and utilization reporting to both internal and external audiences

Messaging E-mail or other messaging system among providers and staff within theorganization, or to external organizations


• Physician friendliness– if docs won’t use it, it won’t help research

• What data it contains• How that data is stored (and retrieved)• Security• Cost, maintenance, technical support, etc

Critical EMR Features


• Workflow compatible– portable

• Easy data entry– voice-recognition– pen-based (PDAs)– digital ink

• Preserves doctor-patient relationship

• Secure Fujitsu 510

Physician Friendliness


• Contents: data and detail sufficient for– real-time clinical care

• notes, orders, labs, prescriptions, xray (reports)...

– administration• demographic, billing, provider IDs...

– research?• standardized data collection, symptom scales, etc

• Structure: generally should store contents in relational form– unstructured free text (flat file) difficult to compute on– relational data schema provides structure to the EMR data

• e.g., fields for diagnosis, medication name, dosage

EMR Contents and Structure


Relational Admissions Database MasterTable

ID Name Sex Birthdate Insurance000-01-001 Lee M 09-Jul-00 B/T Healthnet000-01-002 Smith F 22-Oct-25 Medicare000-01-003 Perez F 13-Jun-57 B/T Pacificare

AdmissionNumberTableAdm# ID Admit Date Discharge

Date001 000-01-001 31-Dec-94 12-Jan-95002 000-01-001 27-Mar-96 31-Mar-96003 000-01-002 03-Feb-95 16-Feb-95004 000-01-002 27-Feb-95 20-Mar-95005 000-01-003 19-Nov-97 23-Nov-97

AdmissionTableAdm# Admit

ServiceAdmit

DiagnosisPrincipalDischargeDiagnosis

001 Med Acute MI Acute MI002 Med COPD Pneumonia003 Surg THR THR004 Med Acute MI Acute MI005 Gyn Menorrhagia von Willebrand's

Secondary Discharge Diagnosis TableAdmission # Secondary Discharge Diagnoses

001 COPD001 Diabetes002 COPD003 Acute MI004 VF Arrest005 Diabetes


What Goes Into the Table Cells?

• If the entire chart were stored in relational tables, all the chart information (including HPI) is in the cells

• Free vs. coded entries– “Mrs. Jones suffered an anterior non-Q wave MI” vs– MI: Yes, Location: Anterior, Type: Non-Q

• Structure and coding is essential for making the EMR more machine interpretable– free text entries in structured fields better than plain flat

file– even better to code entries into standardized terms


• A term is a designation of a concept or an object in a specific vocabulary

• e.g., English blood = German blut

• Standardization required for communication– acts like a dictionary

• DGIM tried to use STOR to pull out all CHF patients for quality improvement program but terms used were too varied

• i.e., how to guarantee that all acute MI admissions will be retrieved if asked for?

• Vocabularies (collections of terms)– general standardized: ICD-9, CPT, MeSH– research-domain specific: CDEs for cancer, etc...– your own data dictionary

Standardization of Clinical Terms


Cost/Benefits of Coding

• The more coded and more structured your data, the more advanced computing you can do with that data– because the computer can “understand” more

• But coding and structuring costs time and effort– selecting billing codes for outpatient practice– structured templates for clinic notes may be too

constraining

• Tradeoff between – costs of more coding and structuring, and– benefits to accrue from “smarter” computing


Notable Clinical Vocabularies

Vocabulary Name Domain Use

SNOMED Standardized Nomenclatureof Human and Vet Medicine

ClinicalMedicine

EMRDocumentation

MeSH Medical Subject Heading BiomedicalIndexing

BibliographicRetrieval

ICD-9 International Classificationof Diseases

Diseases Billing

CPT Current ProceduralTerminology

MedicalProcedures

Billing

DSM-IV Diagnostic and StatisticalManual of Mental Disorders

Pyschiatry Billing,Nosology

LOINC Logical ObservationIdentifier Names and Codes

Labs Lab systems,Billing


Dangers of ICD-9 Coding• VBAC uterine rupture rate

– 665.0 and 665.1 ICD-9 discharge codes used in study (NEJM 2001;345:3-8)

– letter to editor: in 9 years of Massachusetts data• 716 patients with 665.0 and 665.1 discharged• reviewed 709 charts• 363 (51.2%) had actual uterine rupture• others had incidental extensions of C-section incision, or were

incorrectly coded or typed• 674.1 (dehiscence of the uterine wound) also used to code another

197 ruptures (or 35% of confirmed cases of uterine rupture)

• Administrative codes are not ideal for research


ICD-9 Concept Coverage

• How well would ICD-9 do in capturing a medical chart?

• Inpatient and outpatient charts from 4 medical centers abstracted into 3061 concepts [Chute, 96]

– diagnoses, modifiers, findings, treatments and procedures, other

• Matching: 0=no match, 1=partial, 2=complete– 1.60 for diagnoses– 0.77 overall– ICD-9 augmented with CPT: overall 0.82


UMLS• A meta-thesaurus of over 40 English and non-English

vocabularies– SNOMED, MeSH, ICD-9, CPT, DSM, Read code, etc.– designates a UMLS preferred term

• e.g., “Atrial Fibrillation” is preferred over– a fib, afib, or AF– auricular fibrillation, or ushka predserdiia fibrilliatsiia

• UMLS terms categorized into 55 semantic types– e.g., signs and symptoms, biologic function, chemicals, finding,

pathologic function• Also links concepts together

– Atrial Fibrillation is-a Cardiovascular Disease


UMLS Semantic Coverage

• 1996 UMLS with ~30 vocabularies (Humphreys)

– 32,679 normalized strings submitted (80% for EMR)• 58% exact concept found• 28% related to broader concept but modifications not

found• 13% related concept found• 1% not found

– semantic coverage varied from 45% to 71%

• SNOMED International and Read did the best• Bottom line: current vocabularies cannot fully

capture all the clinical concepts in medical charts


Research Data Dictionaries

• Research data dictionaries are lists of study variables and their definitions

• Standardization of data dictionaries facilitates data sharing, merging, and meta-analysis

• Terms in a data dictionary should ideally come from a standard clinical vocabulary– e.g., SOB? shortness of breath? breathlessness?

• ICD-9: Dypsnea and other respiratory abnormalities (786.0)• CPT: no matching concept or term• UMLS: Dypsnea is preferred term


Notable Research Data Dictionaries

• Common Data Elements (from the NCI)– standardized study variables for breast, lung, cervical,

prostate cancer– http://cii-server5.nci.nih.gov:8080/cde_browser/cde_java.show

• HCFA’s MedQuest modules – domain specific data dictionaries

• a fib, CHF, diabetes, pneumonia, orthopedics, etc.

• Other domain specific ones?– prospective meta-analysis movement attempting to

disseminate common data dictionaries


Common Data Elements Example• Menopausal Status: “Indication of whether a

woman is potentially fertile or not.” • Allowed values:

Post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy and not currently receiving therapy with LH-RH analogs [eg. Zolades])

Post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy)

Pre (<6 mo since LMP AND no prior bilateral ovariectomy, AND not on estrogen replacement)

Above categories not applicable AND Age < 50Above categories not applicable AND Age >=50


Choosing a Vocabulary

• For an EMR– billing: ICD-9, CPT– clinical data capture: SNOMED best but expensive ($50K/site)

• federal government arranging a national site license (ie free for all)

– research: any is better than none!

• For your own research databases– if standard domain-specific data dictionary exists, use it– if not, use a standard clinical vocabulary

• often ICD-9 or CPT, or SNOMED, or UMLS preferred terms

– try not to be defining your own terms and your own definitions• upfront work will make it easier to share data later


EMR for Research Summary• An EMR is not automatically going to help

clinical research– if it’s all unstructured free text, it won’t help much at

all• the more structured it is (ie more defined fields), the better

– if it’s just coded sporadically in ICD-9• problem with gamed codes• poor coverage of many clinical concepts

– if it’s coded in SNOMED• some clinical concepts still not well covered• if national site licenses happens, will be a terrific boon

• EMR better than chart review; can we do even better?





Outline


Data Warehouses

• Touted for– business decision making– health care quality improvement– outcomes research

• What is a data warehouse? aka clinical data repository– how is it different from a regular EMR?


Types of Queries

• Clinical care• What was Mr. Smith’s last

potassium?• Does he have an old CXR

for comparison?• What antihypertensives

has he been on before?• What did the neurology

consult say about his epilepsy?

• Research• What proportion of

diabetics with AMI admissions were discharged on -blockers?

• What was the average Medicine length of stay in 2000 compared to 1995?

• What is the trend in use of head CTs in patients with migraine?


MICU

FinanceResearch

QA

Data Warehouse

Internet

ADT Chem EMR XRay PMB Claims

• Integrated historical data common to entire enterprise

A Clinical Data Warehouse


• Need many types of data for research and QI• E.g., for our outcomes study, need

– admission: ADT (admission/discharge/transfer) system– diabetes diagnosis: e-chart, HgbA1C– MI diagnosis: e-chart, troponins, EKG readings– -blocker usage: online ordering, pharmacy system

• Existing (legacy) systems– claims, pharmacy, ADT, lab, xray, med record, etc– HealthSystems Minnesota with 50 computer systems, 50

paper systems Health System Minnesota: 50 paper, 50 computer




Why are Data Warehouses Useful?


• Extract data from legacy systems• Clean data and feed it to warehouse• Allow ad hoc use

– data query, data mining, data analysis

• Service users– modify data content based on queries– provide standard reports– provide alerts to trends

Data Warehousing Procedure


• Requires physical networking and transmission standards (protocols)

MICU

FinanceResearch

QA

Warehouse

Internet


Networking


Prerequisites for Large-Scale Medical Data Merging

• Health-specific network protocols needed– Health-Level 7 (HL-7)

• to provide standards for the exchange, management and integration of data that support clinical patient care and the management, delivery and evaluation of healthcare services

– Digital Imaging and Communications in Medicine (DICOM)

• common data exchange format for medical images


HL-7 Version 2.x Example

MSH|…message headerPID|…patient identifier<!-OBR…observation request>OBR|1|870930010ÔE|CM3562^LAB|80004ÊLECTROLYTES|R|

198703281530|198703290800||| 401-0ÎNTERN^JOE^^^^MD^L|N|||||SER|^SMITH^RICHARD^W.^^^DR.|(319)377-4400|

This is requestor field #1. Requestor field #2|Diag.serv.field #1.|Diag.serv.field #2.|198703311400|||F<CR>

<!-OBX…observation result>OBX|1|ST|84295^NA||150|mmol/l|136-148|H||A|F|19850301<CR> OBX|2|ST|84132^K+||4.5|mmol/l|3.5-5|N||N|F|19850301<CR> OBX|3|ST|82435^CL||102|mmol/l|94-105|N||N|F|19850301<CR> OBX|4|ST|82374^CO2||27|mmol/l|24-31|N||N|F|19850301<CR>


• Common data schema– type (e.g. relational)– data modeling (i.e. column names)

• Common naming of data items– eg., “MI” vs. “myocardial infarction”

• For online data sharing and merging– a physical connection between the computers– common data transmission protocols

• e.g., HL-7– common database communication protocol

• e.g. SQL over TCP/IP (the telnet protocol)

Prerequisites for Data Warehouse Construction


MICU

FinanceResearch

QA

???

Internet


Data Warehouse Contents


Should Warehouse Schema = EMR Schema? MasterTable

ID Name Sex Birthdate Insurance000-01-001 Lee M 09-Jul-00 B/T Healthnet000-01-002 Smith F 22-Oct-25 Medicare000-01-003 Perez F 13-Jun-57 B/T Pacificare

AdmissionNumberTableAdm# ID Admit Date Discharge

Date001 000-01-001 31-Dec-94 12-Jan-95002 000-01-001 27-Mar-96 31-Mar-96003 000-01-002 03-Feb-95 16-Feb-95004 000-01-002 27-Feb-95 20-Mar-95005 000-01-003 19-Nov-97 23-Nov-97

AdmissionTableAdm# Admit

ServiceAdmit

DiagnosisPrincipalDischargeDiagnosis

001 Med Acute MI Acute MI002 Med COPD Pneumonia003 Surg THR THR004 Med Acute MI Acute MI005 Gyn Menorrhagia von Willebrand's

Secondary Discharge Diagnosis TableAdmission # Secondary Discharge Diagnoses

001 COPD001 Diabetes002 COPD003 Acute MI004 VF Arrest005 Diabetes


Example Data Warehouse Schema Discharge Diagnoses

DischargeDiagnosis Admission #

LOS Service Team Attending

Acute MI 001 13 Med II RedAcute MI 004 22 Med I BlueTHR 003 14 Surg III BronzeCOPD 002 5 Med II WhiteMetrorrhagia 005 4 Gyn A Buff

Discharge Meds for AMI Admissions Table

Admission #Aspirinon D/C

Beta-Blockeron D/C

Statinon D/C

ACE Inhibitor onD/C

001 ASA 325 mg QD Atenolol 50 mgQD

Simvastatin 20 mgQD

Lisinopril 10 mgQD

004 ECA 81 mg QD Metoprolol 100mg BID

Atorvastatin 40 mgQD

Ramipril 5 mg QD

• diagnoses would be ICD-9 codes• perhaps a separate table for admission diagnoses


Data Warehouse Summary

• Enterprise viewpoint more appropriate for research than patient viewpoint of EMR

• Integrates data from multiple sources• Querying and processing occurs “offline”

– little impact on real-time clinical care

• Schema can evolve to optimize for analytic needs– can make or modify tables off of legacy systems

Viewpoint Time Queries

EMR Patient Real-Time ClinicalData Warehouse Enterprise Historical Ad Hoc


• Compare 1 year re-admission rate for acute MI in diabetics discharged on -blockers or not– data captured in EMR and other databases– data aggregated in data warehouse– you query the data warehouse — NOT YET….

Study Steps Using EMR





Outline


Privacy vs. Security• Security (a technical feature)

– confidentiality• ensuring that only authorized persons can read or copy

information– encryption of data during transmission impedes eavesdropping only

– integrity• ensuring that information is modified only in appropriate ways

– availability• ensuring that information is not made inaccessible

• Privacy (a legal concept)– right to keep personal information from outside world

• study nurse, data entry clerk, investigator, database administrator, etc may be authorized to see data but may disclose it inappropriately


• Physical security– firewalls

• Encryption– public/private keys

• People security– authority– authentication – access– audit

Internet

Firewall

Network Security

itsa

jaundice

ucsf.edu

LAN


• Authentication – are you who you say you are?

• use passwords, biometrics (e.g., retinal scan), smartcards

• Authority– do you have a need to know?

• different levels of data access for different users• Access

– how to allow only authenticated users to perform authorized activities on authorized data?

• Audit– who actually got into what?

People Security


Audit

• A post-hoc monitoring approach• If announced clearly, can deter unauthorized use• Examples

– Beth Israel (Boston) EMR• audit trail of all who’ve examined a patient’s psychiatric

evaluation notes• audit trail periodically sent to psychiatrist

– why to the psychiatrist and not to the patient?

– Veteran’s Administration Hospitals (DHCP system)


HIPAA Privacy Rule and Research

• Covered entities can disclose protected health information without a patient’s authorization only if– protocol eligible for waiver of informed consent, or– protocol is approved by an IRB or a “privacy board”

• “De-identified” data not covered, doesn’t need IRB oversight

• Limited data set has less stringent disclosure rules


De-identified Data Not Useful

• About 18 patient identifiers removed– direct identifiers

• name, address, electronic mail address, telephone number, fax number, social security number, health benefits number, financial account numbers, drivers license number or other vehicle numbers that are in the public records system

– indirect identifiers• e.g., zip codes(1st 3 digits ok), dates (including date

of admission or service), age and infant birth dates, serial numbers, account numbers, and "unique identifying number, characteristic, or code"


De-identification Isn’t Easy• 87% of the American populace can be uniquely

identified by only [Sweeney, L. ‘97]

– date of birth• in room of 23 people, what is chance that 2 people will share

the same birthday (independent of year of birth)?• http://www.people.virginia.edu/~rjh9u/birthday.html

– gender– five-digit ZIP code– easy to find someone’s info if you’re looking for it;

harder to find out who’s info it is that you have• Anonymizing databases does not remove your

duty to enforce security and safeguard privacy

http://www.people.virginia.edu/~rjh9u/birthday.html


Limited Data Set Compromise• Excludes

– names; postal address information, other than town or city, state, and ZIP code; phone numbers; fax numbers; e-mail addresses; Social Security numbers; medical record numbers; health plan beneficiary numbers; and device identifiers and serial numbers

• Ok to have admit dates, etc• Can you get away with using de-identified data?

What kind of Limited Data Set do you need?– check out http://www.ucsf.edu/hipaa/ and the CHR

http://www.ucsf.edu/hipaa/


Summary of Privacy & Security

• All research (private or federal) must be reviewed by IRB/privacy boards unless it satisfies new stricter rules for waiver of informed consent

• Anonymizing of databases helps but it isn’t foolproof

• In general, people are the weakest security link


• Compare 1 year re-admission rate for acute MI in diabetics discharged on -blockers or not– data captured in EMR and other databases– data aggregated in data warehouse– you request IRB approval– you are authorized to search Limited Data Set data in

data warehouse– audit trail of queries are maintained

Outcomes Research Project


• EMR does not always = easier clinical research• Structure and coding is critical

– structure: schema needed, designed to support intended queries

– coding: standardized, coded data trumps free text• especially important for research• but most standardized vocabularies have insufficient clinical

coverage

• Data warehouse/clinical data repository important for research but must be designed correctly

Take-Home Points

Documents

February 25, 2003: I. Sim EMRs Medical Informatics Ida Sim, MD, PhD February 25, 2003 Division of General Internal Medicine, and Program in Biological