Free Text Narration: EMR Problem or Solution? Dan O Donnell, O Senior Advisor for Medical Informatics ehealth Summit,16 June 2011

(1)

Free Text Narration:

EMR Problem or Solution ?

Free Text Narration:

EMR Problem or Solution ?

Dan O

Dan O’’Donnell, M.D.Donnell, M.D.

Senior Advisor for Medical Informatics

eHealth Summit,16 June 2011

(2)

InterSystems in Healthcare

•

• North America

North America

: Principally known for core technology

products, Cach

é

and Ensemble, upon which major HIT

vendors and provider systems build their HIT applications

•

• International

International

: Provider of HIT applications at clinic,

hospital, community, regional, and national levels; 25

countries, multiple languages

•

• Combined

Combined

: International leader in HIT core technology and

point of care applications

(3)

InterSystems International

•

• US: VA US: VA –– AwardAward--winning EMR serving 5.5M Vets at 1400 siteswinning EMR serving 5.5M Vets at 1400 sites •

• US: MHS US: MHS –– CachCachéé: 72 facilities, 820 clinics, 2 hospital ships: 72 facilities, 820 clinics, 2 hospital ships •

• Sweden Sweden –– First nationwide health information exchangeFirst nationwide health information exchange •

• Scotland, National Health System, TrakCare nation wide Scotland, National Health System, TrakCare nation wide •

• UK NHS / CSC UK NHS / CSC –– Integration solution linking national systems and Integration solution linking national systems and 60% of England

60% of England’’s local health authoritiess local health authorities •

• Brazil: State of Brasilia Brazil: State of Brasilia –– TrakCare for all care settings for 5M people TrakCare for all care settings for 5M people in and around Brazil

in and around Brazil’’s capitals capital •

• Chile, national primary care, military and civilian hospital sysChile, national primary care, military and civilian hospital systemstems •

• Australia, stateAustralia, state--wide community systemwide community system •

(4)

InterSystems in HealthCare

Government

Software Companies Healthcare Providers

RHIOs and HIEs

(5)

Topics

•

• Medical free text in the age of electronic records

Medical free text in the age of electronic records

–

Friend or Foe?

•

• Traditional NLP approach to making use of free text

Traditional NLP approach to making use of free text

•

• Advantages of a fundamentally different method

Advantages of a fundamentally different method

•

(6)

Free Text in the EMR

The Herd of Elephants in the EMR

• Admission/Evaluation assessments

• Chief complaint

• Operation reports

• Pathology reports

• Radiology reports

• Discharge summaries

• Etc. . . .

(7)

The Assumed Dilemma:

•

• Imagine your emails the way clinicians are being

Imagine your emails the way clinicians are being

driven to enter vastly more complex information.

Free text

versus

entry as structured data

• Increasing pressure world wide to enter clinical information

as computable highly structured data, examples of use:

• Complex billing requirements

• Quality improvement, public health, reporting • Decision support: safety, outcome, efficiency

• Human limitation on ability to use pick-lists,

selection boxes, navigate through complex screens

(8)

Some often overlooked medical basics:

• Difference: traveling 1 mile versus traveling to the moon

• Medicine isn’t rocket science, it’s really hard

• Human beings are complex – extremely complex

• Medicine is based on biology, not physics or

engineering:

• Standard for statistical validity in physics: p = 1x10—7 = .00000001 = probability of result by chance

(9)

Knowledge in Medicine

• Clinicians know, in their hearts, that when we say

we are “certain” we mean we are pretty sure, based

on the best information available now, but . . .

• What we “know” today in medicine is based on

unstable uncertainty

• Medical decisions must be made now, no waiting

(10)

Free Text in Medicine

• Decades of attempts to make sense of

what’s in it: “Natural Language Processing”

• It’s always been there

• It always will be

• There’s massive amounts of it

(11)

Problem size:

How many coded concepts in the UMLS, in round numbers? _{2 million} How many discrete concepts in the 60 million Pub Med

abstracts?

60 million

UMLS is only 58 million short

Imagine a medical data model with 2+ million concepts.

(12)

Traditional “NLP”: Top Down

Traditional

“

NLP

”

: Top Down

• Build algorithms to better group words, understand

• Build domain specific ontology:

• Search for word level matches – try to find

(13)

iKnow: Bottom up, concept level

• Concepts separated by language specific relationship terms

• Concept-Relationship-Concept pattern (CRC), and CRC-CRC patterns • Generic, not domain specific

• Multiple languages – based on language specific linguistic rules • Find what is in the text, not just what you’re looking for

(14)

Combine

• “Smart map” and frequency analysis to other sources of free text • “Smart map” to any domain ontology

(15)

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

(16)

Smart Mapping

Congestive Heart Failure (Disorder) SNOMED ID: 42343007

The patient has a « congestive heart failure »

Congestive Heart Failure (Disorder) SNOMED ID: 42343007 Acute (Qualifier Value) SNOMED ID: 373933003

(17)

Using the Meaning in Text

Summarizing

Matching

Navigating

Contents Contents

(18)

iKnow Advantages

iKnow Approach

No predefinitions required

Fully domain-independent

Precision

and

performance

Classic Approach

Precision

or

performance

Domain-specific and

domain-dependent

Labor-intensive predefinitions

required

(19)

Language Quirks, some examples

•

• Multiple terms mean the same thing

Multiple terms mean the same thing

• Ambiguity

• Negation – extremely important in medicine

• Relationship to Voice Recognition, a few problems:

Emphasis/Tonality

(20)

Ambiguity

–

Is the school little?

–

Are the girls little?

–

Are the girls pretty?

–

Is the school pretty?

–

Is the school pretty (quite) little?

–

Are the girls pretty little?

Time flies like an arrow.

Fruit flies like an apple.

Pretty little girls school

(21)

VR: Emphasis & Tonality

–

– II never said she stole my moneynever said she stole my money –

– I I never never said she stole my moneysaid she stole my money –

– I never I never saidsaid she stole my moneyshe stole my money –

– I never said I never said sheshe stole my moneystole my money –

– I never said she I never said she stolestole my moneymy money –

– I never said she stole I never said she stole mymy moneymoney –

– I never said she stole my I never said she stole my moneymoney

There are no VR solutions available for tonal languages

I never said she stole my money.

(22)

Voice Recognition, Homonyms

The Cape Town

“

Rev. Desmond Tutu Ballet School

”

.

? ? ?

A “voice-to-text” message to their supplier:

(23)

Goals:

•

• Make use of existing terabytes of free text (e.g. 70 TB in MHS)Make use of existing terabytes of free text (e.g. 70 TB in MHS) •

• Make it possible for clinicians to enter rich patient informatioMake it possible for clinicians to enter rich patient information with n with analyzable free text

analyzable free text

•

• Make it easier to navigate through and understand content of larMake it easier to navigate through and understand content of large ge amounts of free text: individual patients and patient populatio

amounts of free text: individual patients and patient populationsns •

• Progressive, stepProgressive, step--byby--step development of more complex uses, e.g.:step development of more complex uses, e.g.: –

– Coding support, to autoCoding support, to auto--codingcoding –

– Decision support suggestions, to rules triggeringDecision support suggestions, to rules triggering –

– Grouping patients: protocols, Grouping patients: protocols, ““patients like thispatients like this””, complex , complex disease management.

disease management.

–

– Real syndromic monitoringReal syndromic monitoring –

(24)