active semantic electronic medical records an application of active semantic documents in health...
TRANSCRIPT
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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/