Informatics Basic Science Research Agendas

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CPBS 7711: Electronic Health Records /
Clinical Research Informatics

Michael G. Kahn

6 December 2011


Various phenotypes

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff

Submission

& Reporting

Evidence
-
based

Review

New

Research

Questions

Study

Setup

Study Design

& Approval

Recruitment

& Enrollment

Study

Execution

Clinical

Practice

Public

Information

T1 Biomedical Research

Investigator Initiated T1


T2 Translational Research

Industry Sponsored Commercialization

Clinical

Trial Data

Basic

Research Data

Pilot

Studies

Required

Data Sharing

Outcomes

Reporting

Outcomes

Research

Evidence
-
based Patient
Care and
Policy

EMR

Data

A Lifecycle View of Clinical Research

Translational Phases

Westfall JM, Mold J,
Fagnan

L. Practice
-
based research


“Blue Highways” on the NIH roadmap. JAMA. 2007 Jan 24;297(4):403
-
6.

Translational Zones Example

Beta
-
blockers and Myocardial Infarctions

Drolet

BC,
Lorenzi

NM. Translational research: understanding the continuum from bench to bedside.
Transl

Res. 2011 Jan;157(1):1
-
5.

Setting the context: Translational Barriers

Bench

Bedside /Clinic

Translational Barrier 1

Wide
-
spread

Appropriate

Use in Standard
Practice

Translational
Barrier 2

New Terms: Translational Bioinformatics &
Clinical Research Informatics

Sakar

IN. Biomedical informatics and translational medicine. J
Transl

Med. 2010 Feb 26;8:22.

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff

The Clinical Data Landscape

Slide from Philip R.O. Payne,
Ph.D. The
Ohio State
University Medical Center,
Department of Biomedical
Informatics

Slide from Philip R.O. Payne,
Ph.D. The
Ohio State
University Medical Center,
Department of Biomedical
Informatics

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff

A Framework for Health Care Data Use


Internal Data/Information


Patient Care


Patient specific


Aggregate


Comparative


General Operations


External Data/Information


Comparative


Expert/Knowledge
-
based (Research)


Regulatory

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Patient Encounter Data and Information

Primary Purpose

Type

Clinical

Administrative

Patient
-
specific

Identification

sheet (aka “Face Sheet”)

Problem list

Medication record

History

Physical

Progress notes

Consultations

Physicians’ orders

Imaging and X
-
ray results

Lab results

Immunization record

Operative report

Pathology report

Discharge summary

Diagnoses codes

Procedure codes

Identification sheet

Consents

Authorizations

Preauthorization

approvals

Scheduling

Admission or registration

Insurance eligibility

Billing

Diagnoses codes

Procedure codes

Aggregate

Disease indexes

Specialized registries

Outcomes

data

Statistical reports

Trend analyses

Ad hoc reports

Cost reports

Claims denial

analyses

Staffing analyses

Referral analyses

Statistical reports

Trend analyses

Ad hoc reports

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Important documents you may not recognize

Problem

list

Significant active

or dormant illnesses and operations. Items
may be acute (this encounter only) or chronic (long
-
duration,
chronic or intermittent). Includes entries from all care
-
givers.

Includes both medical and non
-
medical issues

Medication
s list

A list of all active medications that the patient has been
prescribed and
supposedly

is taken. Patient compliance
issues may result in significant deviation from the med list

Medication
administration
record

(MAR)

Detailed

record of every patient that the patient received or
did not receive while under inpatient care. Reasons for not
receiving a medication include: refused, away, NPO

H&P: History

and Physical

A comprehensive review of the patients symptoms

and signs
as understood at the beginning of a treatment episode.
Sections include: CC, HPI, PMH, PSH, FH, SH, ROS, PE,
Assessment & Plan

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Important documents you may not recognize

Progress Notes

On
-
going reassessment

and documentation during the
course of treatment made by physicians, nurses, therapists,
social workers and other clinical staff. Most popular
documentation model used to be SOAP, now being replaced
by APSO

Physician
orders

Directions

or prescriptions given to other members of the
health care team regarding medications, tests, diets,
treatments, etc.

Discharge
summary

For an inpatient encounter, a summative

account of the
reason for admission, the significant findings from tests,
procedures performs, therapies provided, response to
treatment, condition at discharge and instructions for home
care, including medications, activity, diet and follow
-
up care.

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Data Creation Flow: Inpatient

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Data Creation Flow: Inpatient

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Data Creation Flow:
Outpatient

Modified from: Wager KA, Lee FW Glaser JP. Managing Health Care Information Systems. A Practical Approach For Health Care Exe
cut
ives.
Jossey
-
Bass San Francisco 2005.

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff


UB
-
04

Uniform billing form
used by Medicare and
adopted by most
insurance companies

CMS
-
1500

Billing form for physician
services

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff


SeDLAC: A Secondary Database Resource
supported by the CCTSI Informatics Core


Primary data resources are:



National Center for Health Statistics
(
www.cdc.gov/nchs/
) and


Agency for Healthcare Research and Quality
(
www.ahrq.gov
)



Extensive collection of searchable databases:


freely available to all


replicated on SeDLAC servers

Available Databases


NHIS: population
-
based interview


National Health Interview Survey


NAMCS: outpatient provider
-
based


National Ambulatory Medical Care Survey


NHAMCS: hospital urgent care
-
based


National Hospital Ambulatory Medical Care Survey


MEPS: family
-
based, repeated measures


Medical Expenditure Panel Survey


HCUP: inpatient
-
based


Health Care Utilization Project

Available Databases


NSFG: women
-
based (expanding to include
men)


National Survey on Family Growth


BRFSS: population
-
based (telephone)


Behavioral Risk Factor Surveillance Survey


NHANES:population
-
base interview and exam


National Health and Nutrition Examination Survey


NHCHS: agency
-
based


National Home Care and Hospice Survey

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff


Children’s Hospital Clinical Analytics:

Access
to
Clinical Databases


Epic

--

TCH only. DX, PX, medications/Rx,
flow
sheets
.


Colorado
Hospital Association (CHA):

administrative
database of inpatient (only) for all Colorado hospitals.
Updated quarterly


CHCA PHIS



Pediatric Health Information Systems
-

an external database of comparisons 30+ free
-
standing Children's Hospitals. Updated quarterly.


NACHR
I
--

National Association of Children's
Hospitals and Related Institutions: more than 70
participating hospitals
-

Updated quarterly.

Available EPIC Data

Comprehensive Medical Record

-

Admit, transfer, discharge

-
MR#, Account #

-
Name, address, phone, zip code

-
Diagnosis, Procedure codes

-
DRG, MDC

-
ED transfers

-
Department(s)

-
Appointments

-
Providers

Outcomes etc..


Demographics

-
Age, race, gender, county,
etc

Utilization, claims & billing

-
Individual charges

-
Insurance billing

-
Insurance payment


Clinical Documentation

-
Vital signs

-
Allergies

-
Detailed flowsheet
data

-
Med orders, med admin, Med Rx

-

Procedure orders / notes

-
Physician, nursing, ancillary notes

-
Laboratory, Microbiology, Radiology,
Pathology Results

Behind The Scenes



The Emergency Dept ERD

Examples of Research Participation


Elaine Morrato


“Erickson M, Miller N, Kempe A, Morrato EH, Benefield E. Benton K. Variability
in
Spinal Surgery Outcomes among Children s Hospitals in the
US
”.

Presented at the
2009 American Academy of Orthopedic Surgeons
(AAOS) Annual Meeting
, Las Vegas, NV, February 25
-
28, 2009.



Morrato EH, Erickson M, Beaty B; Benton K; Benefield E, Kempe,
A.


Variability in surgical outcomes for spinal fusion surgery in U.S.
children’s hospitals
”.



Presentation presented at the Health Services Epidemiology Spotlight Session
at the

Society for Epidemiology Research Conference,
June 24
-
27, 2008,
Chicago, IL.


Am J Epidemol
.


2008; 167(Suppl): S40.



Information CI group provided:


Patient list for specific surgical procedures (laproscopic vs open)


Diagnosis


Demographics


Outcomes

Examples of Research Participation


Peter Mourani: “Outcomes of Premature Infants Admitted to PICU with
Acute Respiratory Disease”.



Information CI group provided:


Medication orders, Lab test results


DX codes, ADT events


Outcomes, Analysis


Sarena Teng: “Retrospective Review of Propofol as Bridge to
Extubation in Pediatric Post
-
operative Cardiac Patients”



Information CI group provided:


List ICU patients on mechanical ventilation, propofol


Medication review


Patient outcomes

Examples of Research Participation


Marion Sills: “Emergency Department Overcrowding and Quality of
Care for Children”.

Information CI group provided:


Medication orders


Lab test results


DX codes


ADT events


Molli Pietras: “Evaluation of Prolonged Precedex Infusion in
Critically Ill Infants and Children”



Information CI group provided:


List of qualifying patients


Flowsheet data


Medications


Outcomes


PHIS Data Overview

All data submitted electronically
(no manual entry) on a quarterly
basis


PHIS By The Numbers*


Participating Hospitals:
40


Inpatient Cases:
2.2 million


Inpatient Days:
13.1 million


ED encounters:

6.7 million



Total Charges:
$90.7 billion


Total ICD
-
9 Codes:
33.6 million


Pharmacy Transactions:
116.8 million


Physicians:

297,250



* Since 2002, does not include
available archived data back to 1992

Seattle

Oakland

Palo Alto

Madera

Los Angeles

Orange

San Diego

Phoenix

Denver

Dallas

Fort Worth

Corpus Christi

Houston

Little Rock

New Orleans

Birmingham

Memphis

Nashville

Atlanta

St. Petersburg

Miami

Boston

Hartford

New York

Philadelphia

DC

Norfolk

Pittsburgh

Dayton

Columbus

Cincinnati

Akron

Buffalo

Milwaukee

Chicago

St. Louis

Detroit

Indianapolis

Minnesota

Omaha

Kansas City

PHIS Patient
Abstract


Demographics


Gender


Birthweight (gms)


DOB


Pediatric Age Group


AAP Age Code


Age (based on age at
admission)


Age in Years


Age in Months (if less than 2
yrs)


Age in Days (if less than 30
days)


Race/Ethnicity



Episode of Care


LOS


Admit Date/Month/Year


Discharge Date/Month/Year


Infection Flag


Surgical and Medical Complication
Flags


Disposition


Pre
-
Op LOS


Post
-
Op LOS

PHIS Patient
Abstract


Physician Profiles


Attending Physician


Attending Physician Sub
-
specialty


Principal Px Physician


Principal Px Physician Sub
-
specialty


Dx
/
Px

Profiles


Principal
Dx


Principal
Px


Clinical Classification (Groupers)


Major Diagnostic Category (MDC)


CMS (HCFA) DRG


APRDRG


Version 15


Version 20


Version 24

Topics

1.
Translational research barriers

2.
Sources of clinical
d
ata

3.
The generation of clinical
d
ata

4.
Administrative data
s
ources

5.
National clinical
d
ata
s
ources

6.
TCH clinical data sources

7.
How can I exploit any of this stuff


Kohane

Nat Rev Genet 2011 Jun 12(6) 417
-
28

Kohane

Nat Rev Genet 2011 Jun 12(6) 417
-
28


URL:

www.gwas.net

QRS duration

Dementia

Peripheral vascular disease

Cataracts

Type II diabetes

Coordinating
center

RFA HG
-
07
-
005:

Genome
-
Wide Studies in Biorepositories with
Electronic Medical Record Data


2007 NIH Request for Applications from the
National Human Genome Research Institute


“The purpose of this funding opportunity is to provide
support for investigative groups affiliated with existing
biorepositories to
develop necessary methods and
procedures

for, and then to perform, if feasible,
genome
-
wide studies in participants with phenotypes
and environmental exposures derived from electronic
medical records, with the aim of widespread sharing of
the resulting individual genotype
-
phenotype data to
accelerate the discovery of genes related to complex
diseases.”
(Emphasis added)

EMR
-
based Phenotype Algorithms


Typical components


Billing and diagnoses codes


Procedure codes


Labs


Medications


Phenotype
-
specific co
-
variates (e.g., Demographics,
Vitals, Smoking Status, CASI scores)


Pathology


Imaging?



Organized into inclusion and exclusion criteria

EMR
-
based Phenotype Algorithms


Iteratively refine case definitions through partial
manual review to achieve ~PPV ≥ 95%



For controls, exclude all potentially overlapping
syndromes and possible matches; iteratively
refine such that ~NPV ≥ 98%

Phenotype Reuse

T2DM

Diabetic Retinopathy

Primary Phenotypes

Site

Phenotype

Validation
(PPV/NPV)

Group Health

Dementia

73% / 92%

Marshfield
Clinic

Cataracts / Low HDL

98% / 98%

82% / 96%

Mayo Clinic

PAD

94% / 99%

Northwestern
University

Type 2 DM

98% / 100%

Vanderbilty
University

QRS Duration

97% / 100%

www.gwas.net

Opportunities for CPBS Collaborations


NLP/Text Mining electronic records


Novel
phenotyping

classification algorithms


(Limited) access to genotypes


Disease
-
specific


Study
-
specific


Investigator
-
specific


CPBS 7711: Electronic Health Records /
Clinical Research Informatics

Michael G. Kahn

6 December 2011