CPBS 7711: Electronic Health Records / Clinical Research Informatics

Michael G. Kahn6 December 2011

Various phenotypes

Topics

1. Translational research barriers

2. Sources of clinical data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

6. TCH clinical data sources

7. How can I exploit any of this stuff

Submission& ReportingEvidence-based

Review

NewResearchQuestions

StudySetupStudy Design

& Approval

Recruitment& Enrollment

StudyExecution

ClinicalPractice

PublicInformation

T1 Biomedical Research Investigator Initiated T1 T2 Translational ResearchIndustry Sponsored Commercialization

ClinicalTrial Data

BasicResearch Data

PilotStudies

RequiredData Sharing

OutcomesReporting

OutcomesResearch

Evidence-based Patient

Care and Policy

EMRData

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 /ClinicTranslational Barrier 1

Wide-spreadAppropriateUse in Standard Practice

Translational Barrier 215-20 years

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 data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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 data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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 Executives. Jossey-Bass San Francisco 2005.

Patient Encounter Data and InformationPrimary Purpose

Type Clinical Administrative

Patient-specific

Identification sheet (aka “Face Sheet”)Problem listMedication recordHistoryPhysicalProgress notesConsultationsPhysicians’ ordersImaging and X-ray resultsLab resultsImmunization recordOperative reportPathology reportDischarge summaryDiagnoses codesProcedure codes

Identification sheetConsentsAuthorizationsPreauthorization approvalsSchedulingAdmission or registrationInsurance eligibilityBillingDiagnoses codesProcedure codes

Aggregate Disease indexesSpecialized registriesOutcomes dataStatistical reportsTrend analysesAd hoc reports

Cost reportsClaims denial analysesStaffing analysesReferral analysesStatistical reportsTrend analysesAd 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. Translational research barriers

2. Sources of clinical data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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 data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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 data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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.

– NACHRI -- National Association of Children's Hospitals and Related Institutions: more than 70 participating hospitals- Updated quarterly.

Available EPIC DataComprehensive Medical Record- Admit, transfer, discharge-MR#, Account #-Name, address, phone, zip code-Diagnosis, Procedure codes-DRG, MDC-ED transfers-Department(s)-Appointments-ProvidersOutcomes 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

1

INPATIENT Emergency Department

AmbulatorySurgery

ObservationUnit

PHIS Data Repository

Medical Records

System

Billing

Systems

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 data

3. The generation of clinical data

4. Administrative data sources

5. National clinical data sources

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 PhenotypesSite 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. Kahn6 December 2011