A Patient Centric Approach to Medical Archiving

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Welcome to Today’s Web Seminar!

July 28, 2011

2:00PM ET

Mike Perkowski is co-founder and partner of New Reality Media LLC. Mike has

nearly 30 years of experience following the computer industry as a reporter, editor,

publisher and marketing executive. Before co-founding New Reality Media, he was co-

founder and chief operating officer of Microcast Communications, a media company

covering the IT and municipal broadband marketplaces. Previously, he spent 10 years

at Ziff Davis Media in a variety of executive roles, including VP and general manager of

Ziff Davis‟ Market Experts Group; vice-president of corporate marketing; publishing

director of Smart Partner, and associate publisher for marketing at PC Magazine. Mike

also spent 13 years at CMP Media in various editorial and publishing leadership roles,

including editor-in-chief and publisher of Computer Systems News. He has directed the

development and implementation of numerous integrated marketing programs for his

clients at Ziff Davis, CMP and as an independent marketing consultant specializing in

new-product launches, and has led such disciplines as market research, customer

event programs and sales training.

Mike also was a member of the Board of Directors for the Computing Technology

Industry Association (CompTIA), the high-tech industry‟s largest trade association. He

received a B.A. in Journalism from Rider University (Lawrenceville, New Jersey).

.

As a member of Dell‟s Medical Archive Team, Tom Rose is involved in

several large scale client IT transformation projects for Dell in the North

Eastern US. Throughout a 20 year career in Healthcare IT, Tom has

developed deep experience in the planning for and execution of high impact,

complex information technology engagements designed to virtualize patient

information currently located underneath clinical applications. Tom has direct

experience in deployment of application neutral archives. Dell‟s offering in

this area is the Dell Unified Clinical Archive which can be configured to allow

medical imaging studies to be integrated with EMRs and even HIEs in a

patient centric manner.

Tom‟s technical competencies include: Clinical Information Systems • IHE

Technical Framework (XDS-I) • EMPI linking to Universal Clinical Archives •

Large Scale Enterprise Radiology and Cardiology Solutions • Application

Neutral Archives • Radiology & Cardiology PACS Conversions • Digital

Pathology Imaging • Endoscopy Imaging

Dr. Peter Aiken is Associate Professor of Information Systems/Founding Director Data

Blueprint. He has been employed since 1975 and in information technology/management

related positions since 1980. Following a career in retailing, he began his current avocation

during the early 1980's when he managed the Information Center of a major research

university, delivering consulting and end user support services to a large, diverse,

distributed user population numbering in the thousands. Later he was promoted to the

position of Computer Systems Senior Engineer, responsible for the distributed online

program development (CICS) and telecommunications systems (VTAM) for the University's

mainframe operations.

In addition to managing nationwide research and development projects, Dr. Aiken has

lectured internationally – giving invited talks in 17 countries. More than a decade & a half of

university level teaching and course/curricula development experience has resulted in his

chairing both doctoral and masters committees.

Many of these projects have also resulted in jointly authored refereed publications with the

students. In 1999, VCU spun off a for-profit company called Data Blueprint. The company

has developed an international reputation for expertise in the areas of data reverse

engineering, business and systems reengineering, systems integration/systems

engineering, strategic planning, information engineering, software requirements

engineering, human-computer interaction, and decision support systems.

He is the author/co-author of seven books on information technology. Dr. Aiken publications

have appeared in the Communications of the ACM, IEEE Computer, IEEE Software, the

IBM Systems Journal, Information Week and a number of others.

To Ask a Question:

1. Click Q&A pane

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Type a question for the

presenter field

3. Click the Ask button

Use the scroll button to scroll

to your answer if necessary

Creating an

Infrastructure for

Medical Archiving:

6 – datablueprint.com 8/15/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

Data Governance, Metadata

Practices, Enterprise Data

Architecture and Archiving

Peter Aiken, PhD

[email protected] +1 804 382 5957

Solution Component Overview (pretty version)


Metadata

Practices

Data

Governance

Archiving Enterprise

Architecture

Solution Component Overview (not so pretty version)


Metadata

Practices Data

Governance


Architecture

Basic Assumptions

• Large data volume (increasing by 10's of thousands/month)

– Unpredictable structures, lacking general predefinition

• Arduous and complex retention policies

– Regulatory environment

• Basic management is difficult

– Loose control and you cannot gain it back easily

• Now add-in the requirement for planned evolution

– System components must be designed for change

• Advantageous data management practices are required to transform the "tax" of data management into an advantage

– Data management facilitates data usage for business purposes

• Our goal is to transform the perception of the tax into an investment


• Increasing detail and precision mandates more robust, higher

storage requirements

– Enterprise capacities growing at 48%/year

• Non-exchangeable formats prohibits information

sharing/cooperation

• Evolving LCM regulations driven by regulation complicates

ongoing production

• Net result is an

underserved health-

care client unable to

benefit from image

association/sharing

• Disparate solutions prevent

the "aha moment"

Challenges


Typical Grown Curve


"workload"

"dataload"

• 50% providers overbuy archiving

technology (i.e., Fiber Channel)

• 40% attempt unrealistic recovery times

• 65% providers retain images forever due

to image disposition challenges IDC PACS and Storage Special Report, November 2010

12 - datablueprint.com 7/21/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

Why Data Projects Fail by Joseph R. Hudicka

• Assessed 1200

migration projects!

– Surveyed only

experienced migration

specialists who have

done at least four

migration projects

• The median project

costs over 10 times the amount planned!

• Biggest Challenges: Bad Data; Missing Data; Duplicate Data

• The survey did not consider projects that were cancelled largely due

to data migration difficulties

• "… problems are encountered rather than discovered"

Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31

Further basic data management system requirements include:

• Consolidated

– Chose to build an un-integrated system?

• Patient Centric

– Self service, low training

• Intelligent

– Forgiving

• Scalable

– Leverage from cost and

research perspectives

• Compliant

– Simplify and standardize

• Cost Effective

– Difficult to achieve in reality 13 – datablueprint.com 8/15/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

Data Management


Data Management


Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.

Engineer data delivery systems.

Maintain data availability.

Data Program

Coordination

Organizational

Data Integration

Data

Stewardship

Data

Development

Data Support

Operations

Data Management


Data Governance


from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Data Governance Applied to

Medical Image Data Management

• To define, approve, and communicate data strategies, policies,

standards, architecture, procedures, and metrics as applied to

medical images

• To track and enforce relevant regulatory compliance and

conformance with data policies, standards, architecture, and

procedures for medical image data

• To sponsor, track, and oversee the delivery of medical data

image management projects and services

• To manage and resolve medical image data

management related issues

• To understand and promote the value of

sharing medical image data 18 – datablueprint.com 8/15/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

• Data Governance – Policy level guidance

– Sets general guidelines and direction

– e.g., "All medical images not marked public must be considered confidential!"

• Data Management – Planning for and delivering medical

data information assets (images)

– e.g., "What else could be done to enhance the value of these images for our clients?"

• Auto insurance and war-fighters use real-time imaging

What is the difference between Data Governance and Data Management?


Enterprise Data Architecture

• Arrangement of data

assets to achieve specific

organizational objectives

• Most organizations focus

on the efficiency gained

by employing enterprise

data architecture

concepts

• Much more is to be

gained by employing

enterprise data

architecture concepts

strategically 20 – datablueprint.com 8/15/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

Medical Imaging Architecture Organization • Attributes are organized into entities/objects

– Attributes are characteristics of "things"

– Entitles/objects are "things" whose information is managed

in support of strategy

• Entities/objects are organized into models

– Combinations of attributes and entities are structured to

represent information requirements

– Poorly structured data, constrains organizational

information delivery capabilities

• Models are organized into architectures

– When building new systems, architectures are used to plan

development

– More often, data managers do not know what existing

architectures are and - therefore - cannot make use of them

in support of strategy implementation 21 – datablueprint.com 8/15/2011 © Copyright this and previous years by Data Blueprint - all rights reserved!

Metadata Practices • Metadata defined …

– Data describing various facets of a data

asset, for the purpose of improving its

usability throughout its life cycle

– Metadata unlocks the value of data, and therefore requires

management attention [Gartner 2010]

• Significant increased operational efficiencies by improving

organizational metadata practices

– Example: ~80% of system maintenance cost is spent

locating information and ~20% is spent making the change

• Metadata Management Practices

– Increasing amounts of data that are subjected to metadata

practices

– Not all data should be subject to metadata practices!


Metadata

Practices

Data

Governance


Architecture

Creating an Infrastructure for Medical Archiving


4. Organizational business

goals pertaining to archiving

1. Rules

required to

achieve

healthcare

objectives

expressed

as

metadata

2. Most

flexible/

adaptabl

e

medical

image

data

structures

3. Breadth/

depth of

value

unlocking

required

Savings Accumulate After Critical Mass Has Been Reached

2

4

$0.00

$2,000,000.00

$4,000,000.00

$6,000,000.00

$8,000,000.00

$10,000,000.00

$12,000,000.00

1 2 3 4 5 6

AccumulatedCosts

AccumulatedSavings

(Years)

Benefits IT-related

• Complexity Management

– Facilitate the scoping and coordination of programs and information systems projects

• Technical Resource oversight

– Identify and remove redundancy

• Knowledge management

– Manage and share knowledge modularity so it can be visualized across different levels

• IT visibility

– IT resources and systems are more aligned to business strategies and are better placed for responsiveness

Business-related

• Reduction in impact of staff turnover

– Capture knowledge from employees and consultants. Provide business solutions from third party organizations consistently so they can conform to the current model.

• Faster adaptability

– Facilitate knowledge acquisition necessary for changing systems and adopting new components.

• Operating procedures improvement

– Understand/model business prior to review and reengineer processes

• Decision making

– Represent an enterprise's layers and component's modularity to let the organization make business decisions in the context of the whole instead of a stand-alone part.


References



Contact Information:

Peter Aiken, Ph.D.

Department of Information Systems

School of Business

Virginia Commonwealth University

Snead Hall Room B4217

301 West Main Street

Richmond, Virginia 23284-4000

Data Blueprint

10124C West Broad Street

Glen Allen VA 23060

804.521.4056

http://datablueprint.com

mobile: +1.804.382.5957

e-mail: [email protected]

web: http://peteraiken.net

… well, I'll be darned,I guess he does have a license to do that …

http://peteraiken.net

Dell Healthcare & Life Sciences

Transforming the technology of healthcare

Dell‟s Application Neutral

Unified Clinical Archive

Tom Rose

Dell Medical Archiving SME


Application diversity

Radiology Dermatology - Visible Light Imaging

Cardiology – Cardiac Cath,

Echocardiology, Nuclear

Endoscopy Ophthalmology

Digital

Pathology

1GB per image

Endoscopic Surgery

Otolaryngology Neurosurgery Oncology

Imaging Modality

Depiction courtesy of Dr Rasu Shrestha

VP of Clinical Informatics

University of Pittsburgh Medical Center


Data Locked in Clinical Silos

Radiology Oncology Cardiology Women’s health Pathology

• Clinical Information bound to the application

• Expensive and redundant procedures are performed

• Diagnostic collaboration among departments is inefficient

• EMR systems are incomplete and lack certain critical patient

information

• Painful application migrations


Transformation of Clinical Path E

vo

luti

on

ary

Pra

cti

ces

Health

Care Today

Translational

Medicine

Healthcare

Information

Exchange

Systems Efficiency

Non-specific Episode by Episode

Reduce Duplicate Effort Patient Centric

View

Decision Support Systems

Information Correlation

Diagnosis


Begin at the file level Example of the file/object relationship

This file: Plus this image

= An object (Containing

One person’s

data grouped

together, in

context)

(+)

Object UUID: 12345.6789

File Type: .DCM (DICOM)

Patient Name: John B. Smith

Patient ID: 5534-58-7892

Age at time of procedure: 52

Procedure Date: 01-15-2010

Physician Name: Dr. Cardiologist

Physician Notes: .WAV File

Retention Period: 10 years

Data Type: 3D Reconstruction-rotatable

Prior 1: CT Scan .DCM

Custom Metadata Affinity: Cardiac Surgical Profile

File Name: CT SCAN J B SMITH

Created By: Technician 1

Created On: 01-15-2010

File Type: .DCM (DICOM)

Plus all this metadata


Object Storage Ecosystem Object/CAS Block/SAN File/NAS Tape

High Scale &

Extensibility

Exabyte scalability

128-bit flat address

space, RESTful

Protocol, HTTP

communication

Limited to

database scale

and transactional

data performance

requirement

To the extent that

the database

scales

Yes

Manageability Self Managing &

Healing,

Metadata-enabled

automation &

Compliance

included

Migration simple

LUNS, RAID

Groups, Replicas,

Backup

File System,

LUNS, RAID

Groups, Replicas

Backup. Metadata

Stored Separately

Risk of readability

Manual handling

Accessibility Online, HTTP,

location-

independent

Tied to physical

location

File tied to

physical location

Offsite, offline

Cost/GB X86 HW, Peer

Scale, Nearline

pricing

High Performance,

High Cost

File System

Scaling cost,

WORM?

Yes


DX Performance – Medium to Large Objects

• Large stream performance sufficient for environments needing rapid access to content

• 32-node cluster performs write speeds for 40MB files at over 1000MB per second and reads at approximately 900MB per second.

32

24

16

8

4

Writes Reads

0 200 400 600 800 1000 1200

Throughput by Stream in MB/sec

40 MB

5.6 MB

1.5 MB

350 KB

32

24

16

8

4

Nodes

0 200 400 600 800 1000 1200

Throughput by Stream in MB/sec

Nodes


Storage

Layer

Clinical

Applications PACS Workflow Clinical Source Modalities

CT Scanner

Medical imaging integration Current View

Proprietary

Scaling by frame


Storage

Virtualization

Layer

Integration

and

Interoperability

Layer

Application Neutral DICOM Archive

Clinical

Applications PACS Workflow Clinical Source Modalities

Netw

ork

Op

tim

iza

tio

n L

ayers

CT Scanner

Medical imaging integration Neutrality View

DX

Object

Storage

Node

DX

Object

Storage

Node

Scaling at the Node Level

DX

Object

Storage

Node

DX

Object

Storage

Node

DX

Object

Storage

Node


Cost of image communication

“ It is estimated that there are more than 785

million health care tests conducted each

year in the United States. The lack of

interoperable systems to effectively

communicate the results among the various

providers who need to review them

is consuming 1 billion hours of

administrative processing time just to get

the data in the right place, according to one

estimate.” - Scott McCabe, “EMRs vs. Information

Convergence”, April 2008

785+ Million Healthcare Tests/Year

CD/film handling cost IDN # 1 IDN # 2 IDN # 3 IDN # 4 Total – 16 hospitals CDs imported annually 3,600 852 3,600 3,600 CDs imported - 7 years 25,200 5,964 25,200 25,200 CD - cost per imported CD $2.50 $18.00 $4.00 $4.00 CD - import cost 7 years $63,000 $107,352 $100,800 $100,800 CDs exported annually 9,600 1,260 6750 12,276 CDs exported - 7 years 67,200 8,820 47,250 85,932 CD- cost per exported CD $4.50 $19.00 $4.50 $12.00 CD - export cost 7 years $302,400 $167,580 $212,625 $1,031,184 Films imported annually 600 285 2,640 2,640 Films imported - 7 years 4,200 1,995 18,480 18,480 Films - cost per imported film $3.00 $15.00 $4.00 $4.00 Films - import cost 7 years $12,600 $29,925 $73,920 $73,920 Film exported annually 5,880 101 600 178 Film exported - 7 years 41,160 706 4116 1,247 Films - cost per exported film $16.50 $30.00 $16.50 $20.00 Films - export cost 7 years $679,140 $21,168 $67,914 $24,948 Total CD/film handling cost $1,057,140 $326,025 $455,259 $1,230,852 $3,069,276


Universal Viewer EMR integration


Universal Viewer EMR integration


XDS for Imaging (XDS-I) Profile

Document

Consumer

Retrieve

Manifest

Query

Documents

Patient

Identity Source

Patient Identity

Feed

Document

Registry

Document

Repository

Provide & Register

Document Set

Register

Document Set

Retrieve Images

Image Document

Source

(Image Manager/

Image Archive )


XDS – Key Features

• Document centric

– Published clinical data is organized into “clinical documents” using agreed standard document types (HL7, PDF, DICOM)

• Document is „application neutral‟

– Document content is processed only by source and consumer systems

• Standardized registry attributes

– Documents are described by a standardized set of attributes.

– Standardized queries are used to discover the content


IHE Infrastructure Profiles: XDS, PIX/PDQ, ATNA

• Cross-Enterprise Document Sharing (XDS)

– Standards based architecture for sharing of clinical documents among healthcare enterprises

• Patient Identifier Cross-Referencing (PIX)

– Standards based architecture for reconciling patient ids from different domains

• Patient Demographic Query (PDQ)

– Standards based architecture for searching based on patient demographics.

• Audit Trail and Node Authentication (ATNA)

– Securing of any communication between endpoints and auditing of systems that receive/send patient information.


How does XDS-I work?

XDS document

registry XDS Study

repository

• Publish a “document of pointers to images” to a

“repository” Think of these as hyperlinks to the

actual study file

• Register the location and meta-data for the

document in a “registry” This is a DICOM

Manifest, or a catalogue of patient data on

hand.

PACS

PACS


How does XDS-I work?

XDS Document

Registry XDS Study

Repository

• Query for correct patient name (PDQ) and MPI (PIX)

• Click on „document‟ pointers from XDS registry

• Pull images from the appropriate PACS repository

• Query for available patient exams (DICOM Manifest)

Patient Admits to

Emergency Room-

Anywhere

PACS

PACS


Dell Unified Clinical Archive Helping Healthcare providers efficiently store, manage and share medical image data

Unified Clinical Archive

Medical Record

Radiology Endoscopy Digital Pathology Neurosurgery Cardiology

Clinical Data Management Data Consolidation and Information Life cycle Management

Dell Clinical Collaboration Portal Cross Enterprise Data Access and Distribution

Dell On-Premises Clinical Archive

Object based storage platform

Dell Cloud

Clinical Archive Managed Enterprise Archive

Clinical Collaboration Portal Universal View with Data Access and Distribution


Q&A Let‟s discuss your transformation For more information contact Susmit Pal http://content.dell.com/us/en/healthcare/healthcare-medical-archiving-unified-clinical-archive.aspx

Thank you!

[email protected]

http://content.dell.com/us/en/healthcare/healthcare-medical-archiving-unified-clinical-archive.aspx











Education

A Patient Centric Approach to Medical Archiving