Active Semantic Electronic Medical Records an Application of Active Semantic Documents in Health Care

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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
  Georgia
• http//lsdis.cs.uga.edu

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Semantic Web application in use

• In 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
• Practice
• Information about practice such as
  patient/physician data
• Drug
• Information about drugs, interaction,
  formularies, etc.
• ICD/CPT
• Describes 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)
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Drug Ontology Hierarchy (showing is-a
relationships)
interaction_ with_non_ drug_reactant
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Drug Ontology showing neighborhood of
PrescriptionDrug concept
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Part of Procedure/Diagnosis/ICD9/CPT Ontology
specificity
maps_to_diagnosis
procedure
diagnosis
maps_to_procedure
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Extraction and Annotation using an ontology
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Local Medical Review Policy (LMRP) support
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

• 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

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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 Jenas 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
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Chart Completion before the preliminary
deployment of the ASMER
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Chart Completion after the preliminary
deployment of the ASMER
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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 gt than 150/70
  prescribe this medicine automatically.
• If patients weight is gt 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/proje
cts/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 labOr resources (example ontologies, Web
services, tools, applications)Google LSDIS
resources, orhttp//lsdis.cs.uga.edu/library/reso
urces/
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Active Semantic Doc with 3 Ontologies
Annotate ICD9s
Annotate Doctors
Lexical Annotation
Insurance Formulary
Level 3 Drug Interaction
Drug Allergy
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Explore neighborhood for drug Tasmar
Explore Drug Tasmar
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Explore neighborhood for drug Tasmar
classification
classification
belongs to group
brand / generic
classification
belongs to group
interaction
Semantic browsing and querying-- perform
decision support (how many patients are using
this class of drug, )