Active Semantic Electronic Medical

Recordsan Application of Active Semantic Documents in Health

Care Amit Sheth , S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, K.Gallagher

Athens Heart Center & LSDIS Lab, University of Georgiahttp://lsdis.cs.uga.edu

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Semantic Web application in useIn daily use at Athens Heart Center

– 28 person staff• Interventional Cardiologists• Electrophysiology Cardiologists

– Deployed since January 2006– 40-60 patients seen daily– 3000+ active patients– Serves a population of 250,000 people

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Information Overload

• New drugs added to market– Adds interactions with current drugs– Changes possible procedures to treat an illness

• Insurance Coverage's Change– Insurance may pay for drug X but not drug Y even

though drug X and Y are equivalent– Patient may need a certain diagnosis before

some expensive test are run

• Physicians need a system to keep track of ever changing landscape

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System though out the practice

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System though out the practice

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System though out the practice

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System though out the practice

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Active Semantic Document (ASD)A document (typically in XML) with the following

features:

• Semantic annotations– Linking entities found in a document to ontology– Linking terms to a specialized lexicon

• Actionable information– Rules over semantic annotations– Violated rules can modify the appearance of the document

(Show an alert)

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Active Semantic Patient Record

• An application of ASD• Three Ontologies

– PracticeInformation about practice such as patient/physician data

– DrugInformation about drugs, interaction, formularies, etc.

– ICD/CPTDescribes the relationships between CPT and ICD codes

• Medical Records in XML created from database

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Practice Ontology Hierarchy (showing is-a relationships)

encounter

ancillary

event

insurance_carrie

r

insurance

facility

insurance_plan

patient

person

practitioner

insurance_policy

owl:thing

ambularory_episod

e

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Drug Ontology Hierarchy (showing is-a relationships)

owl:thing

prescription_drug

_ brand_na

me

brandname_unde

clared

brandname_comp

osite

prescription_drug

monograph_ix_cla

ss

cpnum_ group

prescription_drug

_ property

indication_

property

formulary_

property

non_drug_

reactant

interaction_proper

ty

property

formulary

brandname_indivi

dual

interaction_with_prescriptio

n_drug

interaction

indication

generic_ individua

l

prescription_drug_ generic

generic_ composit

e

interaction_ with_non_ drug_react

ant

interaction_with_monograph_ix_class

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Drug Ontology showing neighborhood of PrescriptionDrug concept

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Part of Procedure/Diagnosis/ICD9/CPT Ontology

specificity

diagnosis

procedure

maps_to_diagnosis

maps_to_procedure

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Local Medical Review Policy (LMRP) support

Example – a partial list of ICD9CM codes that support medical necessity for an EKG (CPT 93000)

Data extracted from the Centers for Medicare and Medicaid Services

ICD9CM Diagnosis Name

244.9 Hypothyrodism

250.00 Diabetes mellitus Type II

250.01 Diabetes Mellitus Type I

272.2 Mixed Hyperlipidemia

414.01 CAD – Native

780.2-780.4

Syncope and Collapse Dizziness and Giddiness

780.79 Other Malaise and Fatigue

785.0-785.3

Tachycardia Unspecified - Other Abnormal Heart Sounds

786.50-786.51

Unspecified Chest Pain – Precordial

786.59 Other Chest Pain

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Technology - now

• Semantic Web: OWL, RDF/RDQL, Jena– OWL (constraints useful for data consistency), RDF– Rules are expressed as RDQL– REST Based Web Services: from server side

• Web 2.0: client makes AJAX calls to ontology, also auto complete

Problem:• Jena main memory- large memory footprint,

future scalability challenge• Using Jena’s persistent model (MySQL) noticeably

slower

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Design and Implementation Issues• Schema design• Population (knowledge sources)• Freshness• Scalability though client side processing• Rules: “Starting at instance A is it possible to get

to instance B going through these certain relationships, if so what are the properties of the relationship” (e.g., “Does nitrates or a super class of nitrates interact with Viagra or one of its super classes, if so what is the interaction level” )

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Architecture & Technology

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Demo

On-line demo of Active Semantic Electronic Medical Record

deployed and in use at Athens Heart Center

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Evaluation and ROI

• Given that this work was done in a live, operational environment, it is nearly impossible to evaluate this system in a “clean room” fashion, with completely controlled environment – no doctors’ office has resources or inclination to subject to such an intrusive, controlled and multistage trial. Evaluation of an operational system also presents many complexities, such as perturbations due to change in medical personnel and associated training.

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Athens Heart Center Practice Growth

400

500

600

700

800

900

1000

1100

1200

1300

1400

jan feb

mar ap

rm

ay jun jul aug

sep

oct

nov

dec

Month

Appointments

2003

2004

2005

2006

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Chart Completion before the preliminary deployment of the ASMER

0

100

200

300

400

500

600

Month/Year

Charts

Same Day

Back Log

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Chart Completion after the preliminary deployment of the ASMER

0100200300400500600700

Sept05

Nov 05 Jan 06 Mar 06

Month/Year

Charts Same Day

Back Log

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Benefits of current system

• Error prevention (drug interactions, allergy)– Patient care– insurance

• Decision Support (formulary, billing)– Patient satisfaction– Reimbursement

• Efficiency/time– Real-time chart completion– “semantic” and automated linking with billing

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Benefits of current system

• Biggest benefit is that decisions are now in the hands of physicians not insurance companies or coders.

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Technology - Future

• BRAHMS (with SPARQL support and path computation*) for high performance main memory based computation

• SWRL for better rule representation• Support for example user specified rules, possibly

for integration with clinical pathways:– If patients blood pressure is > than 150/70 prescribe this

medicine automatically. – If patients weight is > 350 disallow a nuclear scan in the

office because our scanning bed cannot handle such weight.

– If patient has diagnoses X alert, the user to suggest a doctor to refer patient to Y.

* Semantic Discovery http://lsdis.cs.uga.edu/projects/semdis/

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Value propositions & Next steps• Increasing the value of content, and

content in context – highly customized using one of the ontologies (not just CTP/ICD9, but also specialty specific), at the point of use; no separate search, no wading through delivered content

• Actionable rules• Possible trial involving alert services: “When a

physician scrolls down on the list of drugs and clicks on the drug that he wants to prescribe, any study / clinical trial / news item about the drug and other related drugs in the same category will be displayed. “

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Comments on EvaluationQuestions?

More? See Active Semantic Document Project (http://lsdis.cs.uga.edu/projects/asdoc/) at the LSDIS lab

Or resources (example ontologies, Web services, tools, applications):Google: LSDIS resources, orhttp://lsdis.cs.uga.edu/library/resources/