Decision supportlinked to

Laboratory Information systems

Dr Gerard BoranAdelaide and Meath Hospital Dublin

Incorporating the National Children’s Hospital

Overview of presentation

• Definition of DSS

• What do they do?

• Target areas and users

• Methodologies

• Some examples of applications

Decision Support SystemsSupport for Health Care Professionals

• What is a decision support system?– "A DSS/KBS is any computer program designed

to help health professionals make clinical decisions" [Shortliffe, 1987] e.g...

• Information management• Focussing attention (alarms)• Consultation

Decision Support SystemsSupport for Health Care Professionals

• Desirable DSS Features:

– can be configured by the local users

– have measurable benefits for patients and staff

– control “data intoxication”

– promote cost-effectiveness and efficient use of resources

– improve co-operation between central and remote labs

– based on appropriate informatics and telematic standards

– can be integrated with existing LIS, order communication systems, and relevant clinical information systems

DSS versus KBS

• Knowledge-based systems (KBS) are computer programs which seek to imitate human intelligence and expertise through the use of symbolic reasoning

• DSS emphasise SUPPORT for the decision-making process

Do labs need DSS?

• Advances in laboratory technology – Automation– Integrated laboratories– distributed laboratories (satellite labs, point-of-

care facilities, etc)

• Increases in workload

• Limitations on staff and resources

What should they do?

• Have measurable benefits for patients and staff

• Measurable improvements in quality and efficiency

• Be configurable by local users

• Control data intoxication

• promote efficient use of resources

What do they do?

• Information management– e.g activity, financial reports

• Focusing attention– alarms on critical data

• Consultation– Looking up manuals, protocols

Target Users

• Medical Staff• Nurses, e.g. ICU nurses• General Practitioners• e.g...

– Test ordering protocols– Access to lab manuals– Alarms/alerts for critical data– Interpretative reports

Target Users

• Laboratory Scientists – QC procedures– instrument fault diagnosis– preventive maintenance

• Managers– Monitor changes in costs, activity,etc

Decision Support SystemsSupport for Health Care Professionals

• Module Development– Structured Software Engineering Approach

1. State of the art review2. Users requirements and specifications3. Selection of methodology4. Data, information and process modelling5. Prototyping with iterative feedback from users6. Telematics aspects of the prototypes7. Evaluation and transferability8. Integration with existing IT infrastructure

Decision Support SystemsSupport for Health Care Professionals

• Techniques available– statistical/mathematical/graphical– algorithms– biodynamic models– knowledge-based systems (KBS)– Neural networks– Hypertext markup language

Decision Support SystemsSupport for Health Care Professionals

• Features of KBS technology– Reasoning ability– Explanation facilities– Learning by experience– Sensory perception (vision, hearing)– Language understanding (speech, writing)– Motor functions (robots, speech synthesis)

Decision Support SystemsSupport for Health Care Professionals

• KBS Structure– Knowledge Base

• Rule List

• List of comments/interpretations

• Database

– Inference Engine• Human-computer interface

• Rule handling procedures

Decision Support SystemsSupport for Health Care Professionals

• Forward Chaining propagationRule (1)Rule (1)

IF ((Condition-1 is TRUE) (Condition-2 is TRUE) (...................))IF ((Condition-1 is TRUE) (Condition-2 is TRUE) (...................))

THEN ((Condition-3 is TRUE)THEN ((Condition-3 is TRUE)

(Output Solution-1))(Output Solution-1))

..

..

Rule (209)Rule (209)

IF ((Condition-3 is TRUE) (Condition-4 is TRUE))IF ((Condition-3 is TRUE) (Condition-4 is TRUE))

THEN ((Output Solution-1) (Terminate))THEN ((Output Solution-1) (Terminate))

Decision Support SystemsSupport for Health Care Professionals

• Support for ordering investigations

• Support for performing investigations

• Support for interpretation

Physician

Test RequestingResult

Interpretation

Sample Collection Result Reporting

AnalysisSample Preparation

Decision Support SystemsTotal Testing Cycle

Decision Support SystemsSupport for Health Care Professionals

• Support for ordering investigations– Scheduling of Investigations– Dynamic Scheduling of Tests– Lab Information

• Need to work with order communication systems

• Support for performing investigations– Advanced Instrument Interface– Remote Maintenance of Instruments– Instrument Fault Diagnosis/Troubleshooting– Quality Control– Validation of Results

Decision Support SystemsSupport for Health Care Professionals

Decision Support SystemsSupport for Health Care Professionals

• Support for interpretation– Alarms and Alerts– Graphical Presentation– Interpretative Reporting– Drug Alarms

• Feedback for use with order communication systems

Decision Support Systems Relevant Decision Support Modules

– Patient Result Validation

– Thyroid Function

– Lipid

– Alarm/Alert

– Acid-Base

– Drug Interference

– Haematology Image Interpretation

– MI markers

– Organ Profile interpretation

– Cytology applications

– Microbiology applications

Integration

• Integrate with routinely used IS

• Data collection a by-product of routine activity

• Absence of key data (often clinical data) hampers progress

Integration

• With LIS, e.g– HELP system

– OpenLabs

– Connolly

• With HIS, e.g.– Order Communication systems

• With other Clinical Systems, e.g.– Departmental systems (data feeds...)

– Shared Care system

API

GCICommunications

Handler

API

GCICommunications

Handler

ClientServer

Network access Network access

GUI

Host OperatingSystem

Host OperatingSystem

LocalLocal[DB] [DB]

`

Fig. 2. The OpenLabs Communications Architecture

P1Check Lab

Catalog

P2Construct

Task(work list)

P3Dispatch

Task

Care Org

D Task ConstructionRules

D Lab Protocols

D Lab InvCatalog

D Directory ofServices

D Service Status

P4Perform

OLService

P5Compile

Lab Report

Lab ServiceOrder

Lab ServiceComment

OL ServiceStatus

OLServiceOrder

OLServiceReport

Lab ServiceReport

D ServiceReport Log

D ServiceOrder Log

OLServiceReport

Fig. 3. OpenLabs Service Manager (OLSM)

AII AIW POST TELE PRE

DYNAMTELE

i/f

API

Service Manager

communications

API

OpenLabs

LaboratorySystem Manager

LocalUser

Existing LIS

LIS Interface

communications

comms comms

comms comms commscomms

comms

API

comms

API

API API API API API

API

Remote

UserInstr.

Fig. 4. Interconnection of OpenLabs modules and existing systems over a LAN.

Application Code

Client

Application Code

Server

API Layer API Layer

GCI Layer GCI Layer

Comms Handler Comms Handler

OpenLabs architecture

General Practicioner

GP

DMS

Patient

St. James

Consultant Synapses Server

Hos. DB

Laboratory

Renal Clinic

Diabetic Day Centre

Diabetic Clinic

Eye Clinic

Lipid Clinic

Consultant

Synapses Server

Hos. DB

Laboratory

Renal Clinic

Diabetic Day Centre

Diabetic Clinic

Eye Clinic

Lipid Clinic

Tallaght

SHARED CARE

Integrating Lab Data with other clinical systems

The Test Cycle

• PRE

• INTRAPOST

Investigate Interpret

NPT/Satellite Lab

1 3 42

4. Reporting

3. QC/Validation

2. Analysis

1. Sample Prep

Transport to Lab

PTS/Porters

Collect Sample

(Phlebotomy,etc)

Request Form/OCS OrderMain Lab

Clinician

Pre-laboratory applications

• Ordering protocols

• Order communications

• LUMPS/BUMPS (Peters et al, 1991)

• Dutch GP Guidelines

Order communications

Intra-laboratory Applications

• QA Server

• Patient Result Validation (Valdiguie, OpenLabs)

• Lab Watch

Table 1. Validation Toolbox

1. Delta checks. The patients previous results are compared with the current results usingvarious techniques.

2. Internal consistency checks. The consistency between pathophysiologically relatedvariables is examined.

3. Instrument-specific checks. These vary depending on the instrument and analytical processused to generate the result and are often cariied out either by the instrument itself ormanually by the instrument operator

4. Other specific errors checks, e.g..

The EDTA Artefact. This results from contamination of lithium heparin bloodcollection tubes with the contents of EDTA blood collection tubes.

Sample Mix-ups. This results when one sample is given the identity of another,usually an adjacent sample on the workbench.

Post-laboratory Applications

• Thyroid interpretation

• protein electrophoresis interpretation

• interpretive reporting (college guidelines)

• Alarm systems

• Data feeds to other DSS - e.g. diabetes register