Clinical Informatics & Applications

Javed MostafaBiomedical Research & Imaging CenterSchool of Information & Library Science

Translational & Clinical Sciences Institute

May 15, 2009

EPID 896 Clinical Research Curriculum seminar

Outline

• Informatics – Roots and evolution– Emergence of clinical informatics

• Data management and data mining

• Carolina data warehouse for health

Informatics

• A discipline which is concerned with effective and efficient use of computing to promote discovery, creativity, decision-making, and productivity

– A wide variety of sub-disciplines exists

An analogy

Engineering& Applied Math

ElectricalEngineering

MechanicalEngineering

CivilEngineering

Few Informatics Examples

Informatics in Relation to Medicine & Health

• Many associated domains exist, sometimes leading to confusion .. .– Bionformatics– Health informatics– Biomedical informatics– Medical informatics– Clinical informatics

• Additionally … nursing informatics, public health informatics …

Huang, R.Q. (2007). Competencies for graduate curricula in health, medical and biomedical informatics: a framework, Health Informatics Journal, Vol 13(2): 89–103.

Clinical Informatics

• American Medical Informatics Association (AMIA) recently approved the Core Content of of Clinical Informatics– Clinical Informaticians transform health care by

analyzing, designing, implementing, and evaluating information and communication systems

… that enhance individual, population health outcomes, improve patient care, and strengthen the clinician-patient relationship

Gardner et al. (2009). Core content for the subspecialty of clinical informatics. JAMIA, 16(2), 153-157.

Critical Areas of Clinical Informatics

• Care – provision of service to an individual• Health system – organization, policies, quality,

data management

Clinical Care

The HealthSystem

Information &CommunicationsTechnology

Clinical Informatics

Critical Areas in CI: Information Systems

• System development & integration

• Networks

• Security

• Data representation, manipulation, and sharing

A Key Challenge in CI: Data Management

• Volume of data growth is rapid

• Type of data is heterogeneous

• Need systematic way to aggregate – For retrieval and analysis

• To support decision making, quality control, and long-term projects such as research

Hersh, W. (2009). Information Retrieval: A Health and Biomedical Perspective, NYC:Springer.

Evolution of Data Management

1960s 1970s 1980s 1990s 2000+

Traditionalfiles

Hierarchical

Network

Relational

Object-relational

Object-oriented ?

Web-integratedmultimedia DBs

Relational Model

Relation is a term that comes from mathematics and represents a simple two-dimensional table. Representation based on logical associations only! No pointers …

Relation = TableName Job Branch

Relational Model

• 1980-1990+• E.F. Codd proposed the Relational Model• Simple and elegant and scales with ease

• Combined with Structured Query Languages (SQL) offers a powerful mechanism for data organization and access

DW Multidimensional Model

• Example of Two- Dimensional vs. Multi- Dimensional

REGION

REG1 REG2 REG3

P123

P124

P125

P126::

PRODUCT

Two Dimensional Model

::

Three dimensional data cube

Product

Reg 1P123

P124

P125

P126

Reg 2 Reg 3

Region

Multidimensional Star Schema

• Star schema:– Consists of a fact table with a single table for each

dimension.

DW OLAP

• OLAP – OnLine Analytical Processing– Fast analysis of shared multidimensional

information (FASMI)

• Data mining is a critical aspect of OLAP

DW Data Mining

• Prediction: – Determine how certain attributes will behave in the future.

• Identification:– Identify the existence of an item, event, or activity.

• Classification: – Partition data into classes or categories.

• Optimization:– Optimize the use of limited resources.

• Referred to as PICO …

Carolina Data Warehouse for Health Evolution

• UNC health care system started developing electronic medical records almost 20 years ago

• Inpatient and outpatient care in UNC hospitals, clinics and affiliated satellite practices throughout central North Carolina

• Paperless with full nursing notes, physician order entry, progress notes, laboratory, procedure notes, discharge summaries, medication lists, and the ability to write prescriptions available on-line

• 24/7 used by over 1900 physicians, 3000 nurses, with hundreds of thousands of patients each year

• Two years ago UNC Health Care System (UNCHCS) initiated development of an enterprise-wide data warehouse, the Carolina Data Warehouse for Health (CDW-H), to meet the dual challenges of enhancement of quality of care and clinical research with our patient populations (invested > $7 million so far)

CDW – H Strategic Vision

Portal Layer

Applications, Analysis Tools, Search, Query, Mining, ..

Information Federation Layer

Federated Data SourcesExternal & Internal

Biological, Images, Literature, etc.

Data Warehouse

Security Layer

ExtractTransform

Load

TumorRegistry

WebCISCDR

ResearchGenomics,

Proteomics, etc.

CleansingLinking

Conforming

PubMed

dbSNP

External Collaborators

Other Operational

Systems

Images

Other…

Source Databases

Rim?Systems

GE IDX

Pay

4 Pe

rfor

m

Publ

ic H

ealth

Clin

ical

Reg

istr

ies

Patie

nt S

afet

y

Qua

lity

Repo

rting

Coho

rt A

naly

sis

Out

com

es

Staging

Colla

bora

tion

Lay

er

caBI

G

Secure Exchange of information with outside

entities.

SelectiveText

Extraction

AdministrativePillar

SOAApplications

SOAApplications

SOAApplications

SiemensDSS

CDW-H: As It Is Now …• A retrospective, persistent record of cleansed,

transformed, and stored data originating from operational systems

• The “one source of truth” for reporting, analytic, and data mining

• Data organized logically into subject areas for the user’s benefit without regard to its source system

• Reports, analytics, and decision making will be consistent across the entire organization

CDW-H: As It Is Now …• Data is refreshed periodically (24-48 hrs) and is

not real time data

• CDW is not designed to replace or augment daily operational activities, but to support those activities through analytical retrospective processes

• Designed to address overall organizational priorities under the governance of the CDW Oversight and Operations Committees

CDW-H: As It Is Now …• Major Subject Areas in CDW include:

AccountAllergyAmbulatory ClaimChargeContact InformationCore MeasuresDiagnosisDrugDrug Order

Health MaintenanceImmunizationsLab ResultsMedicationsObservationOrderOrganizationPatientPatient Infection

Patient ReadmissionPatient Visit ProviderPayerPaymentProblemProcedureProviderVital Signs

Notes and Reports include:•Ancillary Reports

•Cardiology Reports

•Clinical Notes

•ECG Reports

•GI Reports

Data Set Size

• Number of Tables in Staging area: 219• Number of Columns in Staging area: 3,849• Number of Tables in ADS: 202• Number of Columns in ADS: 2,840• Number of Tables in Inpatient Datamart: 81• Number of Columns in Inpatient Datamart: 1,581• Number of Tables in Diabetes Datamart: 21• Number of Columns in Diabetes Datamart: 504

• Total number of unique Patients: 1.8 Million • Total number of unique Accounts: 4.5 Million

Data Marts• Focused subset of atomic store data to support specific analytical requirements

……

• The data is organized by Dimension and Facts

• Fact Tables contain the desired detailed information– Diabetes Facts: Last A1c, Last LDL, BP, Bilateral Amputee, Onset Date, Insulin Use, Micro

Albumin, etc.

• Dimensions are distinct threads of information that allow the facts to be summarized in specific ways

– Diabetes Dimensions: Patient, Clinic, Provider, Date, Visit, etc.

• Dimensions are expanded fully to provide the aggregation required– For example, the date dimension would specify the calendar date, the day of the week,

weekday / weekend, month, quarter, and year.

Topics Covered in the Diabetes Data Mart

Subject Areas: •Allergies •Discharge Medications •Drug Orders •Health Maintenance•Lab Results •Patient Diagnosis •Patient Medications•Patient Problems •Patient Procedures •Patient Providers•Visits•Vitals

Facts:•Diabetes •Diabetes Clinical Measures• …

Dimensions:•Allergen•Clinic•Date•Diagnosis•Division•Drug•Drug Order Master•Health Maintenance Category•Health Maintenance Standard QA•Hospital Service•Lab Tests•Order Master•Patient•Procedure•Provider•System User•Visit•Vital Master

Diabetes: Dimensions and Facts

Research Portal: Gateway for Researchers and Students

• An application to expose the various key features of the CDW-H in a user friendly way– Metadata and business terms

• A portal to find useful related resources and services related to the CDW-H

• Currently, offers a Cohort Discovery Service as a pre-research step

Medical Record Access: Challenges

De-identified data

Limited data set

Clinical data set

Conduct Study - witha particular cohort

Access & Approval

De-identified view

De-identified view Extracted Data for a Study

IRB Approval/Data Use Agreement

Summary of Access Rules

• The following table summarizes the basic documentation requirements

Level of Access Rule Scope of Data

De-identified No authorization needed

Must not contain any HIPAA defined data elements that may potentially reveal identity

Limited data set Requires signed Data Use Agreement

Largely de-identified PHI but may include some identifiers

Complete set Authorization / Wavier of Authorization

PHI that includes identifiers beyond the limited fields

Cohort Selection Demo• Project Summary Descriptions:

– Need to determine which woman with digital mammograms performed at UNC between May 2007 and June 2008 who also have a documented history or new diagnosis of cardiovascular disease

• Logon to portal• Construct cohort query • Review the results• Refine cohort query• Review the results

Logon to portal

Display of main CDW home page which will be the main source of information about CDW

Click on ‘Research Tools’ tab, to start a new search, click on ‘create A New Cohort search’ button

A default cohort selection query panel with available class and objects on the left side

Filter Area

Class &Objects

Remove filter criteria by highlighting and clicking on remove button, or pressing delete button, or dragging and dropping on the class list

Remove Button

Drag & Drop

Create a new filter with drag and drop Gender Code in filter area; choose filter condition as ‘Equal to’ from the list; and enter ‘F’ for female

Similarly, drag and drop Radiology procedure name in the filter area; click on drop down to pick values from list; search for word ‘digital’

Highlight Radiology procedure name displayed for digital search and click on include button

Drag and drop Radiology Report Date column in the filter area; choose between filter condition; and choose date from calendar

Click on Run Query button to execute the cohort query selection

Report display by current age, gender, and race

Edit the query to refine the selection by clicking on Edit Query button; add diagnosis code to filter area; choose between condition; enter heart disease codes; and click on Run Query to execute the refined query

Query result for patient who had digital mammograms between May 2007 and Jun 2008 and also had heart disease

TraCS Service Center

• Please visit: http://tracs.unc.edu

• Check the Research Resources area …

• A set of consultants – Clinical Research Analysts– System/Business Analysts – DB Programmer

Questions?

• Javed • jm@unc.edu