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Ready Clinical Intelligence – Deriving Clinical Knowledge From Medical Data Using IT Dr. Suman Bhusan Bhattacharyya MBBS, ADHA, MBA.

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Presentation on theme: "Ready Clinical Intelligence – Deriving Clinical Knowledge From Medical Data Using IT Dr. Suman Bhusan Bhattacharyya MBBS, ADHA, MBA."— Presentation transcript:

1 Ready Clinical Intelligence – Deriving Clinical Knowledge From Medical Data Using IT Dr. Suman Bhusan Bhattacharyya MBBS, ADHA, MBA

2 Data – Knowledge – Intelligence Patient has a blood pressure of 120/80 torr [data] Normal population has been found to have a blood pressure of 120/80 torr [knowledge or information] This patient is normotensive [intelligence]

3 Most Important Areas of Use Clinical trials –Pharmaceutical –Clinical Evidence based medicine Development of clinical protocols Maintenance of clinical protocols

4 Clinical Intelligence Requires anonymized patient data Epidemiology studies Uncertainty and probability Evidence based medicine Outcomes analysis Clinical data warehousing Clinical data mining

5 Methodology Project formulation Observation Data collection Data tabulation Data analysis Results publication Results review Validate entire process

6 Formal Paper-based Process Formulate Project Tabulat e Data Medical Records Enter Data Publish Results Statistical Analysis

7 Current Paper-based Intelligence Sources Research Journals Other publications Evidence based medicine

8 Associated Problems Formalized process Resource hungry –Cost –Time –Personnel Inefficiencies in data analysis Cumbersome information publication

9 How IT Helps True and accurate capture of data Efficient warehousing of data that may easily be manipulated Opportunity of mining data in a variety of ways Easy backward referral to data that led to the intelligence

10 Fallacies of IT Inaccurate/improper data capture –Remedy – proper requirements Improper formulation of question –Remedy – clear project objectives Inaccurate data warehousing –Remedy – correct implementation of objectives Incorrect data mining –Remedy – correct project requirements

11 The IT Process Formulate Project Clinical Data Capture Clinical Documentation Medical Record ETVL Data Warehouse Data Storage Data Mining Results

12 Comparative Analysis ItemPaperIT Project formulationFormalAd hoc/Formal ObservationManual Data collectionManualManual/Automated Data tabulationManualAutomated Data analysisManualAutomated (Interventional) Results publicationManualAutomated/Manual Results reviewManualAutomated (Interventional) Process validationManualAutomated (Interventional)

13 Thanks


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