Transcript Informatics Basic Science Research Agendas

CPBS 7711: Electronic Health Records /
Clinical Research Informatics
Michael G. Kahn
6 December 2011
Various phenotypes
Topics
1.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
How can I exploit any of this stuff
A Lifecycle View of Clinical Research
T1 Biomedical Research
Basic
Research Data
Pilot
Studies
Outcomes
Research
Investigator Initiated T1  T2 Translational Research
Industry Sponsored Commercialization
New
Research
Questions
Clinical
Practice
EMR
Data
Evidencebased Patient
Care and
Evidence-based
Policy
Review
Outcomes
Reporting
Public
Information
Study Design
& Approval
Clinical
Trial Data
Submission
& Reporting
Required
Data Sharing
Study
Setup
Recruitment
& Enrollment
Study
Execution
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
Wide-spread
Appropriate
Use in Standard
Practice
Translational
Barrier 2
Bench
Bedside /Clinic
Translational Barrier 1
New Terms: Translational Bioinformatics &
Clinical Research Informatics
Sakar IN. Biomedical informatics and translational medicine. J Transl Med. 2010 Feb 26;8:22.
Topics
1.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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 Executives. Jossey-Bass San Francisco 2005.
Patient Encounter Data and Information
Primary Purpose
Type
Clinical
Administrative
Patientspecific
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 Executives. 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
Medications 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 Executives. 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 Executives. 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 Executives. 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 Executives. 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 Executives. Jossey-Bass San Francisco 2005.
Topics
1.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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 Systemsan external database of comparisons 30+ freestanding Children's Hospitals. Updated quarterly.
– NACHRI -- 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
Data Repository
PHIS DataPHIS
Overview
INPATIENT
Ambulatory
Surgery
Emergency
Department
Observation
Unit
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
Medical Records
Billing
System
Systems
All data submitted electronically
(no manual entry) on a quarterly
basis
1
Minnesota
Omaha
Kansas City
Milwaukee
Dayton
Chicago
Columbus
St. Louis
Cincinnati
Detroit
Akron
Indianapolis
Buffalo
Boston
Hartford
New York
Philadelphia
Seattle
DC
Oakland
Norfolk
Pittsburgh
Palo Alto
Phoenix
Memphis
Madera
Denver
Nashville
Los Angeles
Dallas
Little Rock
Atlanta
Orange
Fort Worth
New Orleans
St. Petersburg
San Diego
Corpus Christi
Birmingham
Miami
Houston
PHIS Patient Abstract
• Episode of Care
– LOS
– Admit Date/Month/Year
– Discharge Date/Month/Year
– Infection Flag
– Surgical and Medical Complication
Flags
• Age in Years
– Disposition
• Age in Months (if less than 2
yrs)
– Pre-Op LOS
• Age in Days (if less than 30
– Post-Op LOS
days)
• Demographics
– Gender
– Birthweight (gms)
– DOB
– Pediatric Age Group
– AAP Age Code
– Age (based on age at
admission)
– Race/Ethnicity
PHIS Patient Abstract
• Physician Profiles
– Attending Physician
– Attending Physician Subspecialty
– Principal Px Physician
– Principal Px Physician Subspecialty
• Clinical Classification (Groupers)
– Major Diagnostic Category (MDC)
– CMS (HCFA) DRG
– APRDRG
• Version 15
• Version 20
• Version 24
• Dx/Px Profiles
– Principal Dx
– Principal Px
Topics
1.
2.
3.
4.
5.
6.
7.
Translational research barriers
Sources of clinical data
The generation of clinical data
Administrative data sources
National clinical data sources
TCH clinical data sources
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
Dementia
Cataracts
Type II diabetes
Coordinating
center
Peripheral vascular disease
QRS duration
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?
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
Diabetic Retinopathy
T2DM
Site
Phenotype
Validation
Primary Phenotypes
(PPV/NPV)
Group Health Dementia
73% / 92%
Marshfield
Clinic
Cataracts / Low HDL
Mayo Clinic
PAD
98% / 98%
82% / 96%
94% / 99%
Northwestern
University
Vanderbilty
University
Type 2 DM
98% / 100%
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