PREDICTIVE TOOLS FOR CLINICAL DECISION SUPPORT 09BMD024 M.H.BINDU

PREDICTIVE TOOLS FOR CLINICAL DECISION SUPPORT

09BMD024

M.H.BINDU

Definition and objective Classification based ona. Efficiencyb. Actions Integration Limitation References

CDSS

Def: A clinical decision-support system is any computer program designed to help health professionals to make clinical decisions.

OBJECTIVE

CDSS tools are the devices which help in completing the required task without occurring any change in the process.

HARD VS SOFT TOOLS

HARD tools Hard tools act in

a)analyzing. b)structuring c)organizing

Aim to be more efficient

SOFT tools Soft tools goes on

a)inspiring b)stimulating

Aim to be more effective.

• These are classified into hard and soft tool based on efficiency as:

CDSS tools

• These tools are again classified as follows based on their actions

i. Acquisition and Validation of Patient Dataii. Modeling of Medical Knowledgeiii.Elicitation of Medical Knowledgeiv.Representation of and Reasoning About Medical

Knowledgev. Validation of System Performance

I. ACQUISITION AND VALIDATION

Acquisition: variety of techniques for data entry Methods of entry: a. keyboard entryb. speech input c. scannable formsd. real-time data monitoringe. intermediaries – a person who transcribes written

data for use by computers.

Validation: to verify if the entered data exists

II. MODELING:

It is creating a structure that helps in translating the usual text entered into a logical application of that data(text) by a computer.

It plays a major role as it includes identification, interpretation and application.

Eg: KADS (knowledge acquisition and documentation structuring) dss built by schreiber

III. ELICITATION

This tool is to develop and to maintain the medical knowledge

Eg: ONCOCIN (chemotherapy advisor) built by Shortliffe

 meta tool for elicitation- graphical interface (a way for humans to interact with) of the existing models.

Eg: PROTÉGÉ built by Musen.

IV. REPRESENTATION AND REASONING

Representation should be clear, simple and should be well-defined depending on what we require.

Reasoning is performed by implementing ‘rules’ and ‘frames’ for the stored data.

These fastens the observation and diagnosis of the process.

V. VALIDATION OF SYSTEM PERFORMANCE

When a gold standard of performance exists, formal studies can compare the program’ advices with that accepted standard of “correctness.”

Eg. : the program already has the information about the PRK,LASIK techniques (eye refractive surgery) will be compared with the new methods like LASEK,EPI-LASEK to check which is more comfortable.

It also helps in correcting if the reasoning given is logical or not.

INTEGRATION OF TOOLS

The electronic linking of multiple machineswith overlapping functions and data needs.

It helps in: a)Reporting: whenever a function is operated.b)Triggering the alarm: incase of any internal

or external disturbance.

Acquisition Model Elicitation

Representation & reasoning

(Compare)Validation of system performance

Validation of patient data

Figure.1. Tools of clinical decision support system

Block diagram representing integration of CDSS Tools

o/pcomfort

LIMITATIONS

Unable to make the program, in a way computers can interpret the input.

The Rapid evolution of medical knowledge makes the maintenance, of data a problem.

These programs lack in good clinical judgment i.e., ‘How to use? what is known’.

REFERENCES

www.ise.bgu.ac.il/courses/mdss/papers/CH16-FINAL.

Chap16.Clinical Decision-Support Systems by Mark A. Musen, Yuval Shahar, Edward H. Shortliffe.

http://www.google.co.in/imgres.