E-Health Data Standardizations

Systems Architecture &

Big data management

Javed Mostafa, MA, PhDMcColl Distinguished Term Professor

Carolina Health Informatics Program

The University of North Caroline at

Chapel Hill

December 12th, 2017

Part 1: Electronic Health Record System

• Why Data Standardization?

• System Components

• Integrating Data and Data Services

Seminar Outline

Part 2: Big Data Management & Analytics

• Big Data in Healthcare: Data Management Principles

• Data Analytics

Why EHR?

EHR promises to:

Reduce error and improve safety

Improve organizational and service efficiency

Particularly over large, distributed services

Why Data Standardization?

In EHR systems a large number of data

standards are utilized, for:

1) Transactions across diverse systems

2) Organization and storage

3) Data manipulation and access

4) Reporting and presentation

5) Quality improvement and safety

6) Reimbursement and payment

Major Components of EHR

Over long period of system developments,

conducted in isolation, several different

systems have been created to serve different

purposes

● Clinical Documentation

● Nursing Documentation

● Laboratory

● Pharmacy

● Computerized Physician Order Entry (CPOE)

System Architecture

https://www.slideshare.net/MegasChara/course-7-unit-1-introduction-overview-components-of-hit-systems

Key Areas of Data

● Registration & Admin data + meta data (MD)

● Clinical data + MD

● Lab data + MD

● Nursing data + MD

● Radiology data + MD

● Pharmacy data + MD

● Care coordination data + MD

All data above are assembled to create the EHR

for a single patient

RADT: Registration & Admission I

These data include vital information for

accurate patient identification and

assessment, including, but not necessarily

limited to, name, demographics, next of kin,

employer information, chief complaint, patient

disposition, etc.

RADT: Registration & Admission II

The registration portion of an EHR contains a

unique patient identifier, usually consisting of a

numeric or alphanumeric sequence that is

unidentifiable outside the organization or

institution in which it serves.

RADT data allows an individual’s health

information to be aggregated for use in

clinical analysis and research.

LIS: Laboratory Information System I

Laboratory information systems (LIS) that are

used as hubs to integrate orders, results from

laboratory instruments, schedules, billing, and

other administrative information.

Laboratory data is integrated entirely with the

EHR only infrequently

LIS: Laboratory Information System II

Even when the LIS is made by the same vendor

as the EHR, many machines and analyzers are

used in the diagnostic laboratory process that

are not easily integrated within the EHR.

For example, the Cerner LIS interfaces over 400

different laboratory instruments. Cerner, a

major vendor of both LIS and EHR systems,

reported that 60 percent of its LIS installations

were standalone (i.e., not integrated).

RIS: Radiology Information System

Radiology information systems (RIS) are used

by radiology departments to tie together

patient radiology data (e.g., orders,

interpretations, patient identification

information) and images

The typical RIS will include patient tracking,

scheduling, results reporting, and image

tracking functions

RIS systems are usually used in conjunction with

picture archiving communications systems

(PACS), which manage digital radiography

studies

PIS: Pharmacy Information System

Inventory management for drugs/medications;

often include drug order fulfilling robots and

payer formularies

When in-house, based in the hospital or the

provider setting, the system is linked to

computerized-order-entry

Frequently, exists outside the provider setting

and hence not directly integrated

CPOE: Computerized Order Entry

Computerized physician order entry (CPOE)

permits clinical providers to electronically order

laboratory, pharmacy, and radiology services.

CPOE systems offer a range of functionality,

from pharmacy ordering capabilities alone to

more sophisticated systems such as complete

ancillary service ordering, alerting, customized

order sets, and result reporting

CDS: Clinical Documentation System

Physician, nurse, and other clinician notes

Flow sheets (vital signs, input and output, problem lists,

MARs)

Discharge summaries

Transcription document management

Medical records abstracts

Advance directives or living wills

Durable powers of attorney for healthcare decisions

Consents (procedural)

Big Picture …

Many more systems

and components exist

… associated with the

EHR Platform

Data Integration into CDR (Clinical Data

Repository)

https://www.slideshare.net/MegasChara/course-7-unit-1-introduction-overview-components-of-hit-systems

CDR: Data Access and Presentation

Data aggregated from diverse systems

and assembled for access and

presentation on demand

Query: Find cases of CHF not taking

ACE (angiotensin-converting-enzyme)

inhibitor

Would not be possible without

integration as the billing system

holds the diagnosis code and the

pharmacy system holds the

medication profile

IDN: Integration Data Network

Integrated Delivery Network (IDN) is an

infrastructure typically used to assemble data

into a single repository called Clinical Data

Repository (CDR)

Several challenges need to be resolved:

Data standards in and across components

Interface or Application Programming Interfaces

(APIs)

Reconciling duplication and redundancies

Best-of-Breed Architecture

RADT LAB PHARM RADIOLOGY CPOE

CLINICAL DOCS NURSING DOCS

INTERFACES

Clinical Data Dictionary

Clinical Data Repository

Unified Database

CLINICAL DOCS NURSING DOCS

Clinical Data Repository

LAB PHARM CPOERADT RADIOLOGY

Which Architecture to use?

Best-of-breed offer the maximum flexibility of choosing the best systems for specific departments / applications

Require interfaces for eachBack-up / recovery difficult

Unified database option demand dealing with a single vendor for all major components

Introduces less ambiguity / unpredictability hence ensure higher availability

Hybrid option more common

Should it be onsite or cloud (ASP)?

Most EHR vendors require installation of client-

server type systems whereby expertise is

needed to support server maintenance and

customizing/modifying client interfaces as

applications demand

Application Service Provider (ASP) approach

removes the necessity of setting up and

maintaining the server environment as the

vendor takes responsibility of this aspect Customization is still needed on the user-end to

match local needs

Tea Break!

Next: Big Data

Management &

Analytics

Data & Insights

Volume of data production poses difficult challengesClinical DecisionsEvidence based medicineComparative effectiveness

Analytics is a key functional component to supportVisualizationData summarizationRecommendationsOnline, human-machine driven decision making

A basic example of analytics technique

Data Production in General

According to International Data Corp. (IDC) data production is doubling every two years

Data production in the sciences as well as in clinical operational setting is v. intensive and growing

Units of Databit (b) 0 or 1 1/8 of a byte

byte (B) 8 bits 1 byte

kilobyte (KB) 10001 bytes 1,000 bytes

megabyte (MB) 10002 bytes 1,000,000 bytes

gigabyte (GB) 10003 bytes 1,000,000,000 bytes

terabyte (TB) 10004 bytes 1,000,000,000,000 bytes

petabyte (PB) 10005 bytes 1,000,000,000,000,000 bytes

exabyte (EB) 10006 bytes 1,000,000,000,000,000,000 bytes

zettabyte (ZB) 10007 bytes1,000,000,000,000,000,000,000 bytes

yottabyte (YB) 10008 bytes1,000,000,000,000,000,000,000,000 bytes

http://techterms.com/help/data_storage_units_of_measurement

Big Data in Health Care

• Kaiser Permanente, the California-based health network which has more than 9 million members, is estimated to have between 26.5 petabytes and 44 petabytes of patient data under management just from electronic health record (EHR) data, including images and annotations. This amounts to the same amount of information contained in 4,400 Libraries of Congress.

• U.S. health care data alone reached 150 exabytes in 2011. Five exabytes (1018 gigabytes) of data would contain all the words ever spoken by human beings on earth. At this rate, big data for U.S. health care will soon reach zettabyte (1021 gigabytes) scale and even yottabytes (1024 gigabytes) not long after.

According to a Institute for Heath Technology Transformation Report ©, 2015

Advances in technology are creating an explosion of data across all industries

Variety of Information

▪ 80% of new data growth is unstructured content

▪ Emails, images, audio, video..

Volume of Digital Data

▪ Machine generated data: Sensors, RFID, etc

Velocity of Decision Making

▪ Rapidly changing business climate

▪ Need to get ahead of the curve : predict issues

and fix them

New Data New Information!

Enterprise Data Warehouse: Spencer and Merkel (2010), HIMSS 2010, Session 68.

Health Analytics Objective: Inquiry Centered Analytics Environment

Administrative

Research Clinical

Can I

access my

lab test

results?

Can I

reduce the

cost of

care?

30

Health

Analytics

Can I aggregate

data to ID public

health risks?

Can I provide

safer care?

Enterprise Data Warehouse: Spencer and Merkel (2010), HIMSS 2010, Session 68.

Challenges to the expert or experience-based practice

2000 20101990 2020

Analytics Requirement

Decisions for patients

with multiple conditions

Genetics

Proteomics and other

effector molecules

Decisions by

Clinical Symptoms

Diagnostic Imaging:

Functional and

Anatomical

Facts per

Decision

1M proteins

Gene Sequencing Data

$1K

10PB/Yr

Volume

Cost

2012

1500

16000

80000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

US CT/day

Human Cognitive CapacityEnterprise Data Warehouse: Spencer and Merkel (2010), HIMSS 2010, Session 68.

Five Focus Areas for Healthcare Analytics

Patient/Member Analytics

Quality of Care Analytics

Incentive Analytics

Wellness & Chronic Disease Analytics

Operational Efficiency

Da

ta G

ove

rna

nc

e

Enterprise Data Warehouse: Spencer and Merkel (2010), HIMSS 2010, Session 68.

Data Storage & Representation Standards

Relational model vs. multidimensional data

Data warehouse data model and applications

Secondary analysis of EHR data

Relational Structure

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

Relation = Table

Patient_ID Visit_Date Branch

Table Creation

Schema Generation

Normalization

Elimination of anomalies and redundancies

Normal forms by decomposition

Table Manipulation

Structured Query Language (SQL)

SelectInsertUpdateDelete, etc.

Using SQL: Schema Generation

Create SCHEMA s_nameAuthorization owner_namedomain definition table definition view definition, etc.

Schema owner can grant access to tables, columns, and views

Table without Normalization

Last_Name DOB Medication1 Medication 2

Faraday 1/1/1960 Acetaminophen Cough Syrup

Thomas 7/5/1975 Cimetidine Ibuprofin

Nemo 31/71966 Acetaminophen Aspirin

Coulomb 12/2/1980 Advil Saline

Nemo 31/71966 Lexapro Prozac

Improved Table …

Nurse_ID Nurse_Role Bonus_Rate

1235 Surgery 3.5

1412 Critical_Care 3

1311 ED 3.5

Nurse_ID-> Nurse_RoleNurse_ID- -> Bonus_Rate

Nurse_Role-> Bonus_Rate

Functional Dependencies (FD)

Normalized Table

Nurse_ID Nurse_Role

1235 Surgery

1412 Critical_Care

1311 ED

Nurse_Role Bonus_Rate

Surgery 3.5

Critical_Care 3

ED 3.5

Components of the Relational

Model

AttributesDomainDegree of relation

Tuples

KeysPrimaryForeign

Relational Model Trade-offs

AdvantagesEasy to express associations among tuples/recordsEasy to manipulate

DisadvantagesHard to express multi-dimensional relationships

Multi-dimensional Relational Structure

Example of Two- Dimensional vs. Multi- Dimensional

REGION

REG1 REG2 REG3

P123

P124

P125

P126

:

:

P

R

O

D

U

C

T

Two Dimensional Model

:

:

Three dimensional data cube

P

r

o

d

u

c

t

Fiscal Quarter

Qtr 1 Q

tr 2 Q

tr 3 Q

tr 4

Reg 1

P123

P124

P125

P126

Reg 2 Reg 3

R e g i o n

Multi-dimensional Schemas

• Multi-dimensional schemas are specified using:

– Dimension table•It consists of tuples of attributes of the dimension.

– Fact table•Each tuple is a recorded fact. This fact contains some measured or observed variable (s) and identifies it with pointers to dimension tables. The fact table contains the data and the dimensions associated with the data

Multi-dimensional Schemas

• Star schema:

– Consists of a fact table with a single table for each dimension

Data Warehouse: Multidimensional

Representation

Comparison with Traditional Databases

• Data Warehouses are mainly optimized for appropriate retrospective data access– Traditional databases are transactional and optimized for “real-

time” access

• Data warehouses emphasize historical data as their main purpose is to support time-series and trend analysis

• Compared with transactional databases, data warehouses are nonvolatile

• In transactional databases transactions usually change records in the database. By contrast information in data warehouse is relatively coarse grained and refresh policy is carefully chosen, usually incremental

CDW: Clinical Data Warehouse Architecture

ETL

ADS

Diabetes

InpatientETL

3

4

5

DSS

CDRSTAGE

ETL

1

2

1

2 34

5

CDR to Stage93 ETL Jobs Stage to ADS

175 ETL Jobs

ADS to IDM33 ETL Jobs

ADS to DDM17 ETL Jobs

18 – 20 hours from source data to application

DSS to Stage98 ETL Jobs

DSS: Decision SupportCDR: Clinical Data RepositoryETL: Extract Transform LoadADS: Atomic Data Store

CDW Data: Subjects

AccountAllergyAmbulatory ClaimChargeContact InformationCore MeasuresDiagnosisDrugDrug Order er

Health MaintenanceImmunizationsLabsMedicationsObservationOrderOrganizationPatientPatient Infection

Patient ReadmissionPatient Visit ProviderPayerPaymentProblemProcedureProviderVital Signs

Notes and Reports include:Ancillary Reports

Cardiology ReportsClinical NotesECG ReportsGI Reports orts

How Do We Use CDW?

Multiple ways

Scholarly Usage: Research Portal

Internal Quality and Performance Assessment

Standards review and reporting (accreditation and payor guidelines)

Research Portal: Cohort Discovery

CDW: Different Levels of Access

Level of Access Scope of Data Required Actions

De-identified & Aggregated Data Must not contain any HIPAA

defined data elements that may

potentially reveal identity

• No authorization needed, log into

research portal “Guest” login

De-identified Data with Ad-hoc Reporting Must not contain any HIPAA

defined data elements that may

potentially reveal identity

• Approval by CDW-H governance

committee

• Signed CDW-H Data Access

Agreement

• UNC IRB approval or waiver

Limited Data Set Largely de-identified PHI but may

include some identifiers

• Approval by CDW-H governance

committee

• Signed CDW-H Data Access

Agreement

• UNC IRB approval or waiver

Complete Data Set PHI that includes identifiers

beyond the limited fields

• Approval by CDW-H governance

committee

• Signed CDW-H Data Access

Agreement

• UNC IRB approval or waiver

• HIPAA Accounting of Disclosures.

Questions: Javed Mostafa, jm@unc.edu

Please take a look at:Carolina Health Informatics Program, UNC, http://chip.unc.edu

Thank you!

This presentation was produced with the support of the United States

Agency for International Development (USAID) under the terms of MEASURE

Evaluation cooperative agreement AID-OAA-L-14-00004. MEASURE

Evaluation is implemented by the Carolina Population Center, University of

North Carolina at Chapel Hill in partnership with ICF International; John

Snow, Inc.; Management Sciences for Health; Palladium; and Tulane

University. Views expressed are not necessarily those of USAID or the United

States government.

www.measureevaluation.org