challenges in transforming observational data for analysis don griffin health informatics technology...

Challenges In Transforming Observational Data

For Analysis

Don Griffin

Health Informatics Technology Director

Computer Sciences Corporation

May 20, 2009

OR

How To Call Into QuestionYour Observational Data

Without Even Trying

Health Informatics May 20, 2009 2

Objectives

Lofty Objective:

Present a complete health Informatics solution:• that is flexible enough to accommodate all of the types of source data that end users will

require—even if they do not know what those data will be—and

• that is rich enough in functionality to support all of the data transformations and manipulations that end users will require to convert those source data into research-oriented knowledge on which they may confidently rely.

More Practical Objective:

Leave those in the audience with an appreciation for the things that must be done ahead-of-time to make multifarious, disparate, observational source data sets useful for analysis.


Definitions

Observational Data

– “... the outcomes of acts of measurement using particular protocols within the context of any objective scientific measurement activity.”

– “… the basic or atomic notion of an observation represents:• the outcome of some measurement taken of a defined attribute or characteristic of some

‘entity’ (e.g., an organism ‘in the field,’ a specimen, a sample, an experimental treatment, etc.),

• within some context (possibly given by other observations).”

– “Every observation entails the measurement of one or more properties of some real-world entity or phenomenon.”

Biodiversity Information Standards – TDWG

For Our Purposes:

– we are most interested in observational data on drug exposures and medical conditions (but other data may interest us, too), and

– chief sources will be Medical Claims and Electronic Health Records (EHRs).


Definitions

Data Transformation– “... the operation of changing (as by rotation or mapping) one configuration or

expression into another in accordance with a mathematical rule; especially: a change of variables or coordinates in which a function of new variables or coordinates is substituted for each original variable or coordinate…”

– “… an operation that converts (as by insertion, deletion, or permutation) one grammatical string (as a sentence) into another…”

Merriam-Webster’s Dictionary

– One of the three pillars of data governance (along with compliance and integration). “… transformation is a goal unto itself, as well as an enabler for the goals of compliance and integration.”

The Data Warehousing Institute

• For Our Purposes:– we are most interested in reformatting data into a Common Data Model that

allows portability of analysis methods across disparate source data sets, and

– in standardizing data representations to make analysis results from disparate source data sets readily comparable.


Transforming Observational Data

Again, for our purposes, the process is rather simple. However, to do it correctly presents some challenges.


The IT View of the End User’s Goal

Skillful use of Common Data Model content to communicate “complex ideas… with clarity, precision, and efficiency” (and, ideally, unimpeachability )

– Show the data

– Induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else

– Avoid distorting what the data have to say

– Present many numbers in a small space

– Make large data sets coherent

– Encourage the eye to compare different pieces of data

– Reveal the data at several levels of detail, from a broad overview to the fine structure

– Serve a reasonably clear purpose: description, exploration, tabulation, or decoration

– Be closely integrated with the statistical and verbal descriptions of a data set

Edward Tufte, The Visual Display of Quantitative Information


The IT View of IT’s Goals

Provide services necessary to populate the Common Data Model

– Data Architecture

– Data Collection

– Data Extraction, Transformation, and Loading (ETL)

– Data Management

Help (or do not hinder) end users in pursuit of their own goals

– Preserve the data (i.e., their native values, formats, etc.)

– Avoid distorting the data

– Maintain data detail

Foster the widespread understanding of the data

– What the data are and are not

– What the data can and cannot do


IT Issues/Challenges

Source Target(CDM)

Technical

Philosophical

DataCollection

DataManagement

ETLDesign

DataArchitecture

DataUnderstanding



Data Collection

– Batch vs. Stream

– Reception and Profiling

– Verification to Specification

– Culling and Cleansing

– Staging


Profiling


Verification to Specification


Profiling



Data Management

– Inventory and Tracking

– Privacy, Security, and Compliance

– Master/Reference Data Management

– Logging and Auditing


Privacy

Protected Health Information– Any information (not just textual data) in the medical record or designated data set that

can be used to identify an individual, and

– That was created, used, or disclosed in the course of providing a health care service (e.g., diagnosis, treatment, etc.)

HIPAA regulations allow researchers to access and use PHI when necessary to conduct research. However, HIPAA only affects research that uses, creates, or discloses PHI that will be entered in to the medical record or that will be used for the provision of heath care services (e.g., treatment). – Research studies involving review of existing medical records for research information,

such as retrospective chart review, are subject to HIPAA regulations.

– Research studies that enter new PHI into the medical record (e.g., because the research includes rendering a health care service, such as diagnosing a health condition or prescribing a new drug or device for treating a health condition) are also subject to HIPAA regulations.

– If in doubt, stay away from the 18 “identifiers.”


Privacy

18 Identifiers1. Names;

2. All geographical subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000.

3. All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older;

4. Phone numbers;

5. Fax numbers;

6. Electronic mail addresses;

7. Social Security numbers;


Privacy

18 Identifiers

8. Medical record numbers;

9. Health plan beneficiary numbers;

10. Account numbers;

11. Certificate/license numbers;

12. Vehicle identifiers and serial numbers, including license plate numbers;

13. Device identifiers and serial numbers;

14. Web Universal Resource Locators (URLs);

15. Internet Protocol (IP) address numbers;

16. Biometric identifiers, including finger and voice prints;

17. Full face photographic images and any comparable images; and

18. Any other unique identifying number, characteristic, or code (note this does not mean the unique code assigned by the investigator to code the data)


Privacy

De-identification is a possible solution. However, additional standards and criteria apply.

– Any code used to replace the identifiers in datasets cannot be derived from any information related to the individual and the master codes, nor can the method to derive the codes be disclosed. For example, a subject's initials cannot be used to code his data because the initials are derived from his name.

– The researcher must not have actual knowledge that the subject could be re-identified from the remaining identifiers in the PHI used in the research study. That is, the information would still be considered identifiable is there was a way to identify the individual even though all of the 18 identifiers were removed.


Privacy

The following is NOT considered PHI, and therefore is not subject to HIPAA regulations.– Health information absent the 18 identifiers.– Data that would ordinarily be considered PHI, but which are not associated with or derived

from a healthcare service event (treatment, payment, operations, medical records), not entered into the medical record, and not disclosed to the subject. Research health information that is kept only in the researcher’s records is not subject to HIPAA, but is regulated by other human subjects protection regulations.

Examples of research health information not subject to HIPAA include such studies as the use of aggregate data, diagnostic tests that do not go into the medical record because they are part of a basic research study and the results will not be disclosed to the subject, and testing done without the PHI identifiers.– Some genetic basic research can fall into this category such as the search for potential

genetic markers, promoter control elements, and other exploratory genetic research. – In contrast, genetic testing for a known disease that is considered to be part of diagnosis,

treatment and health care would be considered to use PHI and therefore subject to HIPAA regulations.

University of California, BerkeleyCommittee for Protection of Human Subjects



Data Extraction

– Form (e.g., ASCII vs. EBCDIC)

– Format (e.g., delimited, fixed-length, ragged right, etc.)

Data Transformation

– Reformatting (usually from flat to relational)

– Probabilistic Matching

– Augmentation (excluding Standardization)

– Master <fill in the blank> Indexing

– Standardization

Data Loading


Augmentation

Person Timeline

Drug A

Drug B

DrugEra1

DrugEra2 DrugEra3

Persistencewindow

Persistencewindow

A1 A2 A3 A4

B1 B2

Person Timeline

Condition A

Condition B

ConditionEra1

ConditionEra2 ConditionEra3

Persistence

window

A1 A2 A3 A4

B1 B2


Standardization



Data Architecture

– Common Data Model Design Paradigms– “All models are wrong, but some are useful” George Box, Statistician

– Flexibility vs. Intuitiveness “Compromise”


OMOP Common Data Model (conceptual)


OMOP Common Data Model (logical)

CDM Domain

PERSON

PERSON_ID

SOURCE_PERSON_KEYYEAR_OF_BIRTHGENDER_CONCEPT_CODE (FK)RACE_CONCEPT_CODE (FK)LOCATION_CONCEPT_CODE (FK)

DRUG_EXPOSURE

DRUG_EXPOSURE_ID

PERSON_ID (FK)DRUG_EXPOSURE_START_DATEDRUG_EXPOSURE_END_DATEDRUG_CONCEPT_CODE (FK)DRUG_EXPOSURE_TYPE (FK)SOURCE_DRUG_CODESTOP_REASONREFILLSDRUG_QUANTITYDAYS_SUPPLY

CONDITION_ERA

CONDITION_ERA_ID

PERSON_ID (FK)CONDITION_CONCEPT_CODE (FK)CONDITION_START_DATECONDITION_END_DATECONDITION_OCCUR_TYPE (FK)CONDITION_OCCURRENCE_COUNTCONFIDENCE

CONCEPT_PARENT_CHILD

CONCEPT_PARENT_CHILD_ID

PARENT_CONCEPT_CODE (FK)CHILD_CONCEPT_CODE (FK)

CONCEPT

CONCEPT_CODE

CONCEPT_NAMECONCEPT_DESCRIPTION

CONCEPT_PROPERTY

CONCEPT_PROPERTY_ID

CONCEPT_CODE (FK)PROPERTY_ID (FK)CONCEPT_PROPERTY_VALUE

CONCEPT_PROPERTY_QUALIFIER

CONCEPT_PROPERTY_QUALIFIER_ID

CONCEPT_PROPERTY_ID (FK)QUALIFIER_ID (FK)

PROPERTY

PROPERTY_ID

PROPERTY_NAMEPROPERTY_DESCRIPTION

QUALIFIER

QUALIFIER_ID

QUALIFIER_NAMEQUALIFIER_DESCRIPTION

CONCEPT_ASSOCIATION_OR_ROLE

CONCEPT_ASSOCIATION_PR_ROLE_ID

SUBJ ECT_CONCEPT_CODE (FK)ASSOCIATION_OR_ROLE_ID (FK)PREDICATE_CONCEPT_CODE (FK)

ASSOCIATION_OR_ROLE

ASSOCIATION_OR_ROLE_ID

ASSOCIATION_OR_ROLE_NAMEASSOCIATION_OR_ROLE_DESCRIPTION

OBSERVATION_PERIOD

OBSERVATION_PERIOD_ID

PERSON_ID (FK)OBSERVATION_START_DATEOBSERVATION_END_DATEPERSON_STATUS_CONCEPT_CODE (FK)RX_DATA_AVAILABILITY

VISIT_OCCURRENCE

VISIT_OCCURRENCE_ID

PERSON_ID (FK)VISIT_CONCEPT_CODE (FK)VISIT_START_DATEVISIT_END_DATESOURCE_VISIT_CODE

PROCEDURE_OCCURRENCE

PROCEDURE_OCCURRENCE_ID

PROCEDURE_CONCEPT_CODE (FK)PERSON_ID (FK)PROCEDURE_DATESOURCE_PROCEDURE_CODEPROC_OCCUR_TYPE (FK)

DRUG_EXPOSURE_REF

DRUG_EXPOSURE_TYPE

DRUG_EXPOSURE_TYPE_DESCPERSISTENCE_WINDOW

CONDITION_OCCURRENCE_REF

CONDITION_OCCUR_TYPE

CONDITION_OCCUR_TYPE_DESCPERSISTENCE_WINDOW

DRUG_ERA

DRUG_ERA_ID

PERSON_ID (FK)DRUG_ERA_START_DATEDRUG_ERA_END_DATEDRUG_EXPOSURE_TYPE (FK)DRUG_CONCEPT_CODE (FK)DRUG_EXPOSURE_COUNT

CONDITION_OCCURRENCE

CONDITION_OCCURRENCE_ID

PERSON_ID (FK)CONDITION_CONCEPT_CODE (FK)CONDITION_OCCUR_TYPE (FK)SOURCE_CONDITION_CODECONDITION_START_DATECONDITION_END_DATESTOP_REASONDX_QUALIFIER

OBSERVATION

OBSERVATION_OCCURRENCE_ID

PERSON_ID (FK)OBSERVATION_CONCEPT_CODE (FK)OBSERVATION_TYPE (FK)SOURCE_OBSERVATION_CODEOBS_VALUE_AS_NUMBEROBS_VALUE_AS_STRINGOBS_VALUE_AS_CONCEPT_CODE (FK)OBS_UNITS_CONCEPT_CODE (FK)OBSERVATION_DATEOBS_RANGE_LOWOBS_RANGE_HIGH

OBSERVATION_TYPE_REF

OBSERVATION_TYPE

OBSERVATION_TYPE_DESC

PROC_OCCURRENCE_REF

PROC_OCCUR_TYPE

PROC_OCCUR_TYPE_DESCPERSISTENCE_WINDOW


Solution Framework

CORE BUSINESS INTELLIGENCE SERVICES

FOUNDATIONAL DATA SERVICES

SU

PP

OR

TIN

G S

ER

VIC

ES

Data Architecture

Data Collection Data Integration Data Management

QueriesReports/

DashboardsOLAP, ROLAP MOLAP, HOLAP

Process Models

Statistical Analysis and

ValidationBusiness Rules/

Predictive ModelsOptimization

Database Management System Data Models Metadata

Reception and Profiling


Culling and Cleansing

Staging for Integration

Probabilistic Matching

Augmentation

Master Person Indexing

Controlled Medical

Vocabularies

Inventory and Tracking

Privacy, Security, and Compliance

Master/Reference Data Maintenance

Logging and Auditing

Bus

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ss In

teg

ratio

n S

erv

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s

Pre

sen

tatio

n a

nd P

ort

al S

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Sys

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Solution Context

CORE BUSINESS INTELLIGENCE SERVICES

FOUNDATIONAL DATA SERVICES

LIFE SCIENCES SOLUTIONS

OVERALL SOLUTION STEWARDSHIP

SU

PP

OR

TIN

G S

ER

VIC

ES

Data Architecture

Data Collection Data Integration Data Management

Scientific Applications

Strategy Process Intelligence Governance

Protocol Feasibility

Study Recruitment

Health Outcomes & Economics

Drug Safety Monitoring

QueriesReports/

DashboardsOLAP, ROLAP MOLAP, HOLAP

Process Models

Statistical Analysis and

ValidationBusiness Rules/

Predictive ModelsOptimization

Database Management System Data Models Metadata

Reception and Profiling


Culling and Cleansing

Staging for Integration

Probabilistic Matching

Augmentation

Master Person Indexing

Controlled Medical

Vocabularies

Inventory and Tracking

Privacy, Security, and Compliance

Master/Reference Data Maintenance

Logging and Auditing

Bus

ine

ss In

teg

ratio

n S

erv

ice

s

Pre

sen

tatio

n a

nd P

ort

al S

ervi

ces

Sys

tem

s M

ana

ge

me

nt

Se

rvic

es

Exploratory Data Analysis

StudyManagement

Site Management

Drug Safety Management

Clinical Data Management

Executive Dashboards

Operational Reporting

Licensing Intelligence

Closed Loop Marketing

Market Intelligence

Marketing

Thank You

Don Griffin ([email protected])

Health Informatics Technology Director

Computer Sciences Corporation

May 20, 2009

challenges in transforming observational data for analysis don griffin health informatics technology...

Documents

observational source

reformatting data

data transformations

definitions observational

types of source data

disparate source data

common data model

pillars of data governance