Virtual Medical Record Implementation for Enhancing Clinical Decision Support

Faculty of Automation and Computers University ”Politehnica” Timişoara

Timișoara, Romania

Valentin Gomoi, PhD student Daniel Dragu, PhD student

Vasile Stoicu-Tivadar, Prof. dr. ing.

Goal:

Based on:

-  HL7 Standards (CDA, vMR)

-  a topic map as knowledge source

Increase access to data for Clinical Decision Support Systems

Content:

1. Introduction, Motivation

2. System Architecture

2.1 Tools and standards

2.2 Inference engine

2.3 HL7 CDA Component (Retrieve data)

2.4 Data manager

3. TM-VMR

3.1 Implementation

3.2 Communication

3.3 Results

Medical Guideline:

- systematically developed statements to assist practitioner and patient decisions about appropriate healthcare for specific clinical circumstances.

Medical Protocol:

- provides the information on duration, dosages, procedures which were omitted in the guideline.

Guidelines? Protocols? Recommendations?

Medical Recommendations:

- suggestions representing the result of the inference based on medical guidelines.

1

Why using Clinical Decision Support Systems ?

•  local adaptation of the guidelines •  compliance with the protocols and guidelines •  standardization of medical practice •  faster implementation of new practice •  better visualisation of medical information

1

Approaches regarding the implementation of the

computer-based guidelines and protocols: •  Asbru, •  PROforma, •  DeGeL, •  Arden Syntax, •  GUIDE. •  Egadss

Existing solutions for Clinical Decisions Support (CDS)

1

Local adaptation and data retrieving problems

ü  Existing solution present low adaptability

Adaptable solutions need standards for:

-  patient data transfer and representation – HL7 CDA,

vMR -  medical rules representation – Arden Syntax

1

2

Data Manager

Inference engine Egadss

TM-vMR

HL7 CDA Components

HL7 CDA Components

System Architecture for increasing CDS interoperability

Data Manager Is used to ensure the communication between the other

system components being called and calling different web

services

Has the roles to respond at different requests from:

•  interface,

•  medical data sources (HL7 CDA Components),

•  inference engine

•  vMR-TM

2.4

2

Data Manager

Inference engine Egadss

TM-vMR

HL7 CDA Components

HL7 CDA Components

System Architecture for increasing CDS interoperability

Inference (Egadss):

Egadss- is a clinical decision support system

Uses: - Arden Syntax (MLM) for the representation of medical rules; -  HL7 CDA level 3 messages as input; -  HL7 CDA level 2 messages as output; -  Clips as inference engine;

2.2

2

Data Manager

Inference engine Egadss

TM-vMR

HL7 CDA Components

HL7 CDA Components

System Architecture for increasing CDS interoperability

HL7 CDA Component

-  extracts information from different databases;

-  represents information in HL7 CDA format;

-  implementation: Visual Studio .Net 2008, C# language;

-  access to more complex information;

2.3

2

Data Manager

Inference engine Egadss

TM-vMR

HL7 CDA Components

HL7 CDA Components

System Architecture for increasing CDS interoperability

TM-vMR representing vMR with TM

•  TM – encoding knowledge and connecting this encoded

knowledge to relevant information resources

•  vMR – standard for the representation of medical

knowledge used in different CDS systems

3

vMR

•  standard model

•  appeared as a necessity for the interoperability

between different CDS and data sources

•  contains 131 medical data elements

•  improves communication between CDS systems

and other medical systems

3

TM Ø  ISO/IEC 13250:2003; Ø  a semantic technology; Ø  knowledge representation;

3

Ø  to qualify the content of topics;

Ø  to link topics together;

Ø  to filter information;

Ø  to structure unstructured information sets;

Ø  to merge topics and topic maps;

TM-vMR Implementation

Is realized by using Topincs open source software

Steps

o  create a topic type for every vMR class within the vMR

atomic terms;

o  model all relationships within the vMR

o  define the serialization names for all terms within the

schema

3.1

TM-vMR

3.1

Associations for vMR elements

Occurrences for vMR elements

TM-vMR with CDS Comunication through “tobjects”

•  topic map objects in “Topincs” are called “tobject”

•  tobjects – represent an data element from the vMR

•  tobjects – allow the insertion, deletion, modification and

many other types of special functions ()

3.2

•  realized with the help of the web services

•  The web services interact with the vMR through

“tobjects”

•  client server architecture

•  services are consumed from the Data Manager

•  NuSOAP – PHP technology

•  communication over HTTPS

3.2

TM-vMR with CDS Comunication through “tobjects”

TM-vMR Results

3.3

vMR element

Functions to work with the vMR element trough tobjects

TM-vMR - benefits

o  connection of any “vMR compatible” CDS

o  extensibility for vMR DAM

o  (web) services especially designed for CDS developers

o  easy to use knowledge base

o  development of a collaborative solution for the capturing of

medical information

3.3

TESTING TM-vMR

3.3

The test was done for the management of diabetes used in

Timișoara Emergency County Clinical Hospital, Timisoara:

- MLMs – where created – containing the medical rules

- PHP Web Services to extract the needed patient data from

the TM-vMR

Patient data needed for the

Medical Rules in Arden S y n t a x f o r t h e prescription of NaCl or glucose

3.3

TESTING TM-vMR

3.3

The test was done for the management of diabetes used in

Timișoara Emergency County Clinical Hospital, Timisoara:

- MLMs – where created – containing the medical rules

- PHP Web Services to extract the needed patient data from

the TM-vMR

Patient data needed for the

PHP web services to access patient data trough tobjects

3.3

$obiectul = Tobject::get('LaboratoryObservationCode'); $val = $obiectul->get_value(); return $val;

Creating to the needed tobject

Get the needed value

Patient data used

3.3

Data Type Values Units

Glicemia 40 – 350 mg/dl

Alkaline Reserve 6 – 26 mmol/L

pH 6.8 – 7.45 -

Sodium 120 – 150 mmol/L

Potassium 2 – 7 mmol/L

weight 70 – 120 Kg

Urea 20 – 70 mg/dl

Ketones 1 / 0 Boolean

Results

3.3

* The system was tested for these 30 patients data sets offering the expected recommendations.

Recommendation: Administrate 1000 ml NaCl

Concentration 0.9 %, 8 units nsulin

Patient data

Conclusions A method was developed to make medical recommendations more:

=>Complex =>Accurate

by allowing access to more complex data and increasing CDS interoperability • connection of any “vMR compatible” CDS systems • CDS connection with various types of data sources

-  Advantages resulted using these tools: ü  increase in quality of medical care ü providing more efficient treatments ü using new medical knowledge in current clinical practice

Thank You for Your attention !

This work was partially supported by the strategic grant POSDRU/88/1.5/S/50783, Project ID50783 (2009), co-financed by the European Social Fund – Investing in People, within the Sectorial Operational Programme Human Resources Development 2007-2013

valentin.gomoi@aut.upt.ro