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

2. Definition and objective Classification based on a. Efficiency b. Actions Integration Limitation References

3. 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.

4. H ARD 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:

5. CDSS tools These tools are again classified as follows based on their actions i. Acquisition and Validation of Patient Data ii. Modeling of Medical Knowledge iii.Elicitation of Medical Knowledge iv.Representation of and Reasoning About Medical Knowledge v. Validation of System Performance

6. I. A CQUISITION AND V ALIDATION Acquisition: variety of techniques for data entry Methods of entry: a. keyboard entry b. speech input c. scannable forms d. real-time data monitoring e. intermediaries – a person who transcribes written data for use by computers. Validation: to verify if the entered data exists

7. II. M ODELING : 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

8. III. E LICITATION 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.

9. IV. R EPRESENTATION AND R EASONING 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.

10. V. V ALIDATION OF S YSTEM P ERFORMANCE 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.

11. I NTEGRATION OF T OOLS The electronic linking of multiple machines with 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.

12. Acquisition ModelElicitationRepresentation & 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/p comfort

13. 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’.

14. REFERENCES www.ise.bgu.ac.il/courses/mdss/papers/CH16- FINALwww.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/imgreshttp://www.google.co.in/imgres.