A Patient Centric Data Retrieval and 

Visualization Program

David Eibling MD

VA Pittsburgh Healthcare System

David.Eibling@va.gov

Augie Turano PhD

IT Director VINCI

Augie.Turano@va.gov
VISTA still Near Top of the Deck

http://www.medscape.com/features/slideshow/EHR2012
Healthcare decisions have always 

been Information-Based

• Ancient writings

– “If a patient has this, then you should do that”

• Traditional healers – until 1950’s??

– Apprenticeship – learn what to do by watching 

teachers

• Modern medicine

– Scientific principles, data driven

– Compensate for imperfections of human 

memory

– Data necessary for decision-making

• Healthcare System

– Decisions about resource allocation, oversight, 

etc

Photo from: http://www.flickr.com/photos/chris-warren-photos/2220257496/sizes/z/in/photostream/

Bay Pines VA 1967
What Information do We Need?

• Information about This patient

– History of this disease – a narrative

• “Tell the story”

– Other facts about this patient

• PMH, FH, SH, etc

• Information about This disease

– Etiology

– Natural history

– Diagnostic tools

– Ameliorating factors

– Optimal Treatment

• Information about This treatment

– Selection criteria

– Probabilities of success, failure, other effects

http://www.flickr.com/photos

/79996062@N08/734493008

8/sizes/m/in/photostream/

Time April 1965

“If you install an EHR make sure you put a shredder by every printer” Dan O’Donnell MD
What Information do THEY* Need?

• Information about this patient

– Nature of this disease

• What is it, exactly?

• Coded information, not a story

– Other facts about this patient

• Other illnesses

• Who is paying for this care?

• Information about this treatment

– What was done?

– Where, by whom, with what resources?

• Information about the outcomes of the treatment

– Did it work?

– Were there complications?

– What did the patient think?

* “They” = payers, govt agencies, patient advocacy groups, researchers and many others
Why so much Information?

There is too much to know

• Increasing complexity of patients

– Living longer

– More illnesses

• Increasing complexity of care

• And interactions between aspects of care

• More specialty care

– More and more specialists know more and more about less and 

less

• More options – medications, treatment choices, etc

• Dramatic increase in literature

– Up to 25,000 clinical trials published yearly

– Thousands of Clinical Practice Guidelines

– Even specialists can’t know it all
“The power of the unaided mind 

is overrated....(Its) real powers 

come from devising external aids 

that enhance (its) cognitive 

abilities.”

Norman. Things that Make us Smart 1993

So we rely on External aids to acquire, 

organize, and access information
“External Aids” we use every day
“IT applications appear designed 

largely to automate tasks or 

business processes. They are 

often designed in ways that 

simply mimic existing paper-based 

forms and provide little support 

for the cognitive tasks of 

clinicians or the workflow of the 

people who must actually use the 

system”

Stead and Lin, eds. Computational Technology for Effective Health Care: 

Immediate Steps and Strategic Directions. National Research Council. National 

Academy Press Washington DC. 2009

Report Released Jan 

2009

EHR Systems Fail to support Physician 

Cognitive Tasks
What is the Effect on the Physician?

• Increased time requirement

– More work shifted to physician

– Estimated 30-40% reduction initially

– Learning curve impact

• Interference with Physician-Patient interaction

– Looking at computer rather than patient

• More than 50% in my case

• Impact on cognitive resources

– “Treating the computer” trumps treating the patient

• Frustration, discontent with role, generalized unhappiness

– Burn-out symptoms in 50% !*

– May be difficult to dissect out which is due to EHR itself or 

which is new administrative requirement

Health Data Management 23August2012

*Archives Int Med Online 20Aug2012
How did this Happen?

–App Designers misinterpret intended use

– Poorly designed user interfaces

– Inadequate testing

– Too many administrative tasks

Image from MicrosoftTM
The most critical role of EHR Systems is

Clinical Decision Support

• Often defined as view alerts

• But much more than that . . . . .

• “Global” definition – to provide all possible 

information to assist decision-making at the 

point of care

– Patient information

• (this one)

– Aggregated patient information

• Information about all similar patients – predictive (pop stats) 

analysis

– Evidence-based recommendations
• Web-based, secure, high speed, User-Centric 

Information Retrieval system for physician queries

• Accesses information within the VA VistA database

• Currently in prototype only

Our Current Research 
“to exploit highly heterogeneous 

data effectively, users . . . need 

the ability to ask queries that 

span multiple data sources 

without requiring the data to be 

standardized . . . The user wants 

a single interface through which 

any query can be posed”

Stead and Lin, eds. Computational Technology for Effective Health Care: 

Immediate Steps and Strategic Directions. National Research Council. National 

Academy Press Washington DC. 2009

Stead Report 

Released Jan 2009
Features

• Clean “GoogleTM-like” interface

• Data domains not limited to VistA – seamless 

internal and external data access

• No training required

• Natural language queries

• Direct data access, no menu navigation required

• Aggregated displays of multiple data fields

• User-directed display paradigms

• User-defined single-word groups (macros)

Voogle off-loads the search task !
Digital Evolution

Data

Portals

Search

Engines

Natural

Language

Knowledge

Bases

Semantics

Knowledge

Understanding

http://www.flickr.com/photos/ibm_research_zurich/5410545854/sizes/m/in/photostream/
Knowledge Base

• Knowledge base is key to Voogle functionality

• “Knows” where data located, or whether data 

present.

• Populated by a “crawler” that crawls through data 

structure and builds a “map” of data locations

• Can be configured to find information outside of VistA

– “display expense form”

– Could be programmed to access Pubmed

• Default display parameters can be overridden by user 

queries.

– “ . . . as timeline , datagrid, chart”
Methodology

Step 1:Crawl Data

Step 2: Create Metadata

Knowledge Base

Step 3:Messaging and 

Visualization

*Image from http://blogs.law.harvard.edu/pkeane/2007/10/29/dase-metadata/

*
Getting Data

• FileMan Fields - FM API calls

– Single value fields

– Multiples

– Pointers

– Word Processing

• Documents – SF171, 9957 etc

• Table oriented data – SQL, Excel, etc

• RPC function calls

• Web Service calls – Bing, Google
FileMan Calls

• Set DIC=70000102,DIC(0)=“QZM” D ^DIC

• D GETS^DIQ(DIC,DA,DR_”*”,”E”,”ANS”, “ERR”)

• S DIC=70000102,DR="4",DA=123 S 

DR(70000102.02)=".01:7",DA(70000102.02)=1 

D EN^DIQ1

 Use indirection for dynamic program execution

VistA uses File Manager DB for data storage both structured and unstructured
Benefits

• Direct queries not a series of menus

• User adaption, retention, and satisfaction

• Decrease medical errors

• Decrease support and training costs

• Auto completion can help guide users

 For example:
Mechanics

• Parse input - tokenize 

• Isolate data fields call and quantifiers 

• Consult knowledge base to get context and frames 

of reference

• Call web services, FileMan, RPC’s calls from 

knowledge base index and search

• Choose best output format-table, chart
Architecture

• Commodity processors and mainstream 

operating systems

• Knowledge base to locate data and data 

concepts

• Fast retrieval implemented via new network 

(Infiniband) that is 30x faster than Ethernet

• SSD (solid state disks) that deliver 10x the data 

rates of SAN (storage area network)
Enhancing EHR Infrastructure

• Update infrastructure – new OS, New Filesystem, Fast IO

• Why is this important?

– Web Services

– Commodity Processors, JBOD’s – low cost

– Fast dual networks (IB-Ethernet)

– Objects

– Fast performance

– Encryption/security

– Backups

– High availability – paired db server mirrors
Hardware

• 12 Nehalem Servers (Quad)

• SATA JBODS with RAID Controller

• OS:Windows, Centos

• Networks:Infiniband (40gbs), Ethernet (1GeE)

• Horizontally scalable (2,400,000 gloref/sec typical

VistA system does 250,000 gloref/sec)

• Storage:60 terabytes

• Cost:$98,000
Hardware

All commodity servers – no blades 

just rack mounts – swap them out if 

they break – no maintenance 

contract
Network Architecture

G-K

A-F

Ethernet Infiniband Infiniband

L-O

P-S

T-Z

Clients App Servers DB Servers Mirror Servers
Security

• VistA log in credentials

• Encrypted session identifier

• Knowledge base has authorization levels for 

each field

• User has authorization level – restricted by 

KB/User combination

• AES encryption (128 bit key)
Summary

• Powerful and simple

• High speed data retrieval

• Web-based

• Natural language interface

• Multiple data sources

• Adaptive knowledge base

• User-directed data displays

Voogle Demo 
Mining large amounts of Unstructured 

Data Using iKnowTM Software

• Natural “concept extraction”

• Enhancement to aid Decision Support

• iKnow TM can “search” text on a single patient – or 

millions of patients

• Does NOT require structured data entry or rule 

building

• iKnow TM will be incorporated into Intersystems

CacheTM as of 2013
Unstructured Text

• Far superior in “Telling the Story”

– “All my patients look the same”

• Lots of information buried in free 

text

• Need a way to extract 

facts/concepts

• Discover correlations and 

relationships

• Can combine with structured data

• Classic word searchs are not 

always effective

Image from MicrosoftTM
Concepts and Relations

Input: A Brown dog barked at the mailman.

Concept:Brown dog

Relation:Barked

Concept:Mailman

No rule creation or dictionary is necessary before indexing.

Barked

Images from MicrosoftTM
Ambiguity ????

BANK

• Of a river?

• Bank in the road

• Where you keep money

• Bank of computers

In Sentences --

• Breast cancer in women mushrooms.

• Teacher strikes idle students.
Process Breakdown

Classic Approach i.Know Approach

Split text into sentences. Split text into sentences.

Use dictionary-based 

statistical models or syntactic 

parsing to guess which words 

might belong together.

Relational elements naturally

separate meaningful concept 

clusters.

Use a dictionary or ontology 

to correct and optionally 

enrich results.

Optionally use a dictionary or 

ontology to enrich results.

Split sentences into words.

Identify relational elements 

based on compact language 

models.
Capabilities

The iKnow approach to identifying concepts and 

relationships in unstructured text is only the 

starting point for more advanced analysis:

• Smart Indexing

• Smart Matching

• Smart Interpretation
Smart Indexing

Smart Indexing (concepts and relations):

Two patients are suffering from congestive heart failure

relation 

detection

concept 

detection

Two patients Are suffering from Congestive heart failure

Smart Index

Concept

Are suffering from

Two patients

Relation

Concept Congestive heart failure

Two patients are suffering from congestive heart failure.
Smart Mapping

Congestive Heart Failure (Disorder) SNOMED ID: 42343007

The patient has « congestive heart failure »

Congestive Heart Failure (Disorder) SNOMED ID: 42343007

Acute (Qualifier Value) SNOMED ID: 373933003

The patient has an « acute congestive heart failure »

This can aid with ICD-10, UMLS mapping
Choose Subject
Note Entities Column
Examine Source Sentence
Can Drill to Original Documents
Gain Insight

• Index unstructured data to get to know 

what’s in this other 85% without reading it

• Match the result to what you already knew 

to find out what you should have known

• Interpret the result to know what to do 

next
Application To Any Domain

• iKnow TM is not domain specific

• Input can be text from any domain and can 

feed a web service

• If the text size is reasonable the conversion of 

unstructured data to structured data can 

occur in real time

• Developers can design apps to use these 

features
Conclusions

• Healthcare is Information-rich, and the quantity 

and complexity of this information is increasing 

at exponential rates

• Information needs far exceed human cognitive 

abilities

• Current EHR designs are poor at matching 

physician patient care needs with tasks, 

overburdening physicians with system needs.

• Current emphasis should be on decreasing 

physician work by off-loading data entry and 

simplifying information access.