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Active Semantic Electronic Medical Records

Chapter 6

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Introduction

• Most cumbersome aspect of healthcare is the extensive documentation that is legally required for each patient.

• 30% of physician’s assistant is spent on this.• Medical practices are investing heavily in

Electronic Medical Records (EMR)• This chapter discusses the design of an EMR

that utilizes semantic web/web services.• It is based on collaboration between

physicians, Athens (GA) Heart Center, LSDIS Lab at UGA.

• Utilizes active semantic documents (ASDs) developed at LSDIS lab.

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News

• Google is getting in health care: Google health + Cleveland Clinic announcing a joint venture for what? For managing health records of patients

• W3C is heavily involved healthcare area.

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Active semantic documents

• ASDs get their semantic feature by automatic annotation of documents with respect to one or more ontologies.

• The documents are now “active”• They are termed “active” since they support

– automatic and dynamic validation– decision making on the content of the document– apply contextually relevant rules to components of

the documents– accomplished by executing rules on semantic

annotations and relationships that span across ontologies.

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Active Semantic Electronic Medical Record (ASEMR)

• ASEMR is an application of ASDs in healthcare which aims to– Reduce medical errors– Improve physician efficiency– Improve patient safety and satisfaction in medical

practice– Improve quality of billing records leading to better

payment– Make it easier to capture and analyze health

outcome measures

• Specification of rules along with ontologies play a key role.

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ASEMR Rules

• Rules include prevention of drug interaction• Ensuring the procedure performed has supporting

diagnoses• ASEMR

– semantic and lexical annotations can be displayed in a browser,

– show results of rule execution, – provide ability to modify semantic and lexical entities in a

constrained manner, say, according to existing lexicons– offer suggestions when rules are broken or exceptions

made• Currently in use by Athens GA Heart Center (AHC)• This is an add on Panacea electronic end-to-end medical

records and management system

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ASMER Approach

• Development of ontologies in healthcare (cardiology) domain

• Development of an annotation tool that utilizes the developed ontologies for annotation of patient records

• Development of decision support algorithms that support rule and ontology based checking/validation and evaluation.

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Motivating Scenario and Benefits

• Physicians face many challenges– Patient care– Clinical care pathways– Medical billing: insurance pays for care

• Any error in a number or reporting may result in refusal to pay

– Preferred drug recommendations: formularies

– Auditable oversight– Abide by government regulations

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Knowledge and Rules representations

• ASMER employs a combination of OWL ontologies with RDQL rules

• Three ontologies:– Practice ontology

• medical practice, facility, physician, assistant, and nurse

• Parts of the existing databases were used to populate this ontology

– Drug ontology• Drugs, classes of drugs, drug interactions, drug

allergies, formularies• License content (Gold Standard Media) equivalent to

physician’s drug reference was the primary source for populating this ontology

• See Fig. 6.1

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ASMER Ontologies (contd.)

• Diagnosis/procedure ontology– Medical conditions, treatments, diagnosis

(ICD-9), and procedures (CPT)– Licensed SNOMED (Systemized Nomenclature

of Medical-clinical terms)

• Key enhancements involved linking this ontology to the drug ontology.

• Customizability for each area involved assigning code to practices and diagnosis

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Rules supported by ASEMR

• Drug interaction check• Drug formulary check (whether the drug is covered by the

insurance company, if not, provide alternatives)• Drug dosage range check• Drug-allergy interaction check• ICD-9 (International Classification of Diseases) annotations

choice for physicians to validate and choose the best possible code for treatment choice

• Preferred drug recommendation based on drug and patient insurance information

• Rules allow for more flexibility, enhanced reasoning power and extensibility

• Rules allow for addition of knowledge declaratively; for ex: adding a relation “cancel_the_effect” to ontology and addition of rule indicating the drugs affected by this rule extends the decision making capacity.

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Application: Scenario 1 Front end

• ASEMR is installed at 8 beta sites.• Active semantic documents

– ASEMR both expedites and enhances the patient documentation process.

– Enables physicians office to complete documentation while the patient is still in the office

– Lets analyze a sample ASD (Fig.6.2)

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Analyzing an ASD

• Document is annotated with ICD, other technical terms automatically

• Medications after visit section:• Level 3 (l3) interaction warning on one of the

drugs– Mouse over on it will pop up details

• Warning F (yellow) indicates that drug is not covered under the patient’s insurance

• Warning A (green) warns that the patient is allergic to this drug

• Simply clicking on the drug prints a prescription• Explore the drug to get more details about the

drug (see an example in Fig. 6.3)

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ASEMR: Scenario 2: Semantic Encounter Sheet

• See fig.6-4• When a physician decides on a diagnosis and

plan for treatment, he/she will have to justify it by specifying code for the reasons.

• This can be automatically done.• Semantic encounter sheet:

– as the user selects orders (ex: EKG), the next column populates the screen with diagnostic code which support medical necessity.

– The doctor is required to validate this choice and the ontology enables him/her to easily consider alternatives.

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Implementation details

• The Panacea database holds all information about the patient:– Patient’s visits, past and present problems,

diagnoses, treatment, doctors seen, insurance information, text description of the visit.

– Data entry creates a well structures XML document– Document is annotated using annotation module– After the annotation, rules module applies rules to

the annotations; rules are written in RDQL– Information is exposed using WS and REST based

messages– XML+XSLT HTML exposed to the client

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ASEMR Architecture

DRUG Practice ICD-9

Owl_files

Static ontology holder/Jena

Jenaapi

Tomcat

DrugWS PracWS ICD9WS

RDQL XML

Active Semantic DocumentJavascript & C#

Lexicalannotations

Semanticannotations

PanaceaDatabase

ClientWeb Browser XSL

xml

REST WS call

REST WS calls

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Deployment and Evaluation

• AHC is the main site of deployment• About 80 patients per day in a 4 hours

time frame• 2 physicians, 2-4 mid-level providers, 8

nurses, 4 nuclear/echo technicians, relies on Panacea/ASEMR web-bases paperless operation for all functions except billing.

• Everything done realtime, where as after visit time was used in earlier approaches.

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Results

• Patient volume increased: See fig.6-6 as seen from appointments

• Charts completed before deployment of ASMER and after deployment: See fig.6-7, 6-8

• Increase in patient satisfaction• Increase in income to the

organization• Improved patient care

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Future Directions

• ASEMR approach can be extended to provide decision support on a deeper level.– Can discover obscure relationship between

symptoms, patient details, and treatments.

• Semantics alerts can be injected into the system about trials etc.

• Higher degree of integration into billing system.

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Demos

• http://www.openclinical.org• ASMER demo:

http://lsdis.cs.uga.edu/projects/asdoc

• Google + Microsoft: healthcare giants?

• W3C: http://www.w3.org/2005/04/swls/