Dr. S.V.S. Deo MS, FAIS, FACS
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1 Computerized Breast Cancer Data base Development Dr. S.V.S. Deo MS, FAIS, FACS Associate Professor Dept. of Surgical Oncology, BRA-IRCH All India Institute of Medical Sciences New Delhi
2 Computerized Breast Cancer Data base Development Last Two Decades Major transformation of Global Health care Scenario EBM : Approach to health care that promotes the collection, interpretation and integration in to clinical practice of valid important and applicable patient reported, clinician observed and research derived evidence. Bio Informatics: Biology + IT
3 Computerized Breast Cancer Data base Development Current Era of EBM - Data Is Power Enhances - Clinicians confidence and respectability Changing role of Physician From an unquestioned god to accountable production worker is a long way to fall in few short years Mcleod 2000
4 Computerized Breast Cancer Data base Development Changing Role of Physician Paradigm shift Authoritarian, dogmatic approach Rational,logical and scientific approach based on data
5 Computerized Breast Cancer Data base Development Need for a database in Oncology Epidemiology & Trends Routine clinical needs Audit, Census, out puts Discharge Summary Morbidity and Mortality analysis Survival Analysis Research & Publications
6 Computerized Breast Cancer Data base Development Problems - Existing system Lack of standardized clinical data documentation & collection Individual Variables House surgeon / JR / SR / Pool officer / Consultant Work up - Few lines - 4 pages Lack of major out come data Despite Rx large number of patients Treatment - Blindly follow western protocols Lack of Internal Audit & quality control
7 Computerized Breast Cancer Data base Development Problems - Existing system Idea-Data Mining Gather File numbers Meet MRO 70 to 75% File retrieval 25 % - Disorganized & Incomplete clinical data. Complete clinical information available - < 50% Waste of Energy & Enthusiasm - Data Cooking
8 Computerized Breast Cancer Data base Development Types of Data Base General purpose Hospital Data base Demographics, Census, Laundry, Dietary,Billing,etc Clinical data base - Disease Specific Clinical, Treatment, Morbidity,Mortality, Survival
9 Who should develop Database? Not a Computer expert or a Statistician Team work Core clinical team Consultants Trainees Computer Engineers Statistician 20 % Input 80 % Input
10 Who should develop database? Clinician is the core member - Preferably a consultant with an in depth understanding of the specialty engaged Aware of the strengths & weak points of the field Vision / Flexibility
11 Who should develop Database? Computer experts Residents Consultant Statisticians
12 Where To develop data base? Possible Levels of database development Institute Hospital Center Department/ Unit Individual
13 Where To develop data base? Macro Level Hospital / Center Total Online PCS with Net working - Paper less system - Ideal What is ideal is not practical
14 Where To develop data base? Difficult to implement in a old and established hospitals Smooth co - ordination & Precise execution Mandatory Clinical departments Hospital administration Computer experts Nursing &Technical staff Too many cooks spoil the broth Possible in New Hospitals
15 Where To develop data base? Micro level Ideal for Indian Institutions Department / Unit / Consultant Feasible - Limited co-ordination, few people to convince and educate Number of patients is Not a problem India
16 How to plan a data base? Preliminary Planning Identify Core Clinical team + IT Team General details & Layout of Database Design proformas & Queries Prepare database Modules Out put requirements Quality control issues
17 Stages of Data base development Brain storming sessions Blank paper Prelim draft Revisions Fine tuning Digital Drafts Dummy Entries Fine tuning Final Draft Simultaneous Interaction with soft ware experts
18 Stages of Data base Development Planning a Proforma Design Queries What is an Ideal Size - 2 to 3 pages Each page 10 to 15 queries
19 Stages of Data base development Pattern of queries Structured queries : Site of Breast cancer Site of Lesion : UOQ/UIQ/LOQ/LIQ/CQ/Others Semi structured queries : Operative Findings Primary Tumor - Findings LN - Findings
20 Stages of Data base development Open Ended queries Special Investigations - Type: Report: Majority of queries should be analyzable - Y/N Numeric code Biggest problem in life is try to answer every thing in Yes or No Albert Einstein
21 Breast Cancer Database Modules Demographic module Name Age Sex Religion Address Clinical data Module Signs/ Symptoms Duration Risk factors Details of disease Investigations Module Haematology Pathology Radiology Treatment Module Surgery Radiotherapy Chemotherapy Out come Module Morbidity Mortality Survival Additional Modules Census Sorting Discharge summary Operation notes
22 General Layout of Data Base Common General Fields - Linked - Core area specific fields H&N Gastro Esophageal General Data Base Sarcoma Gynae Breast Common / Differential out put Colorectal
23 Hardware and Software Requirements Hardware Requirements Stand alone PCs with UPS & Printers LAN - Local area networking 4-5 PCs Total Online PCS system Paperless system
24 Hardware and software Requirements Soft ware requirements: Front End On Screen Display Visual Basic Back End Data base MS-Access EpiInfo SPSS
25 Hardware and software Requirements Front End Visual Basics User Friendly Input Masks for data Modules Demographics Clinical Module Invest.Module Treatment module Out come Module Other Utility Input Masks Print Discharge Find Record Generate Census
26 Hardware and software Requirements Ideal soft Ware User friendly Mouse driven Robust Inbuilt safety features Web enabled Data Backup Easy transfer to Analytical programs (Excel/ SPSS) Up gradable without loss of data
27 Location of work Stations End user accessibility - Important Ideally as close to the place of work as possible. OT, Doctors Room, office Computers should not be treated as luxury Lock and key luxury items Residents should have free access Educate the resident periodically
28 Quality Control Real problem starts now Multiple users / Chance for misuse / Improper entries Corruption of Files / loss of data Data protection & back up
29 Quality control Periodic Audit of Data Monthly / Annual Check Entries Complete blank fields Update follow up Take Backup of records Generate census Paper copy of proforma is a must
30 Breast Cancer Data Base Development AIIMS- Surgical Oncology Experience Idea Standardize treatment protocols Stream lining entries Formation of Core Clinical team + IT Team Breast Cancer proforma finalized Acquiring computers
31 Breast Cancer Data Base Development AIIMS- Surgical Oncology Experience Paper drafts were finalized & Entries started in both Retrospective and prospective fashion Retrospective fashion Entries started Prospective fashion till date 2006
32 Breast Cancer Data Base Development AIIMS- Surgical Oncology Experience Current Protocol Entries are made by residents Three Stage Entry as part of Normal resident duty - Work up of in patients on Proforma - Computer - Entry of Operative details on Proforma - Computer - Prepare Discharge & Complete entries - Computer Over all Supervision - Consultant Monthly Audit HPE &update adj Rx Annual Audit - Update FU
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41 Breast Cancer Data Base Development AIIMS- Surgical Oncology Experience Current Status: Total S.O data base 4500 Patients Breast Cancer Oral cancer Soft tissue sarcoma -310 Colorectal Gastro Esophageal- 280 Ovarian cancer-115
42 Advantages of data base Data Bank Patient / Disease /Treatment profile patterns Saves of clinicians precious time Improve Quality of care Census, Morbidity, Mortality analysis Change Treatment Protocols - Survival data
43 Advantages of Data Base Academic and Research spin-offs Publications Data based AIIMS SO 40 Breast cancer related publications Clinical Trials & Research Projects Data for conferences / Grand rounds
44 Conclusions Data base Integral part of academic Surgical Oncology practice. Major commitment Team job Initial hard work set backs Long term gains are worth taking the initiative EBM - Ego Based Medicine to Evidence Based medicine
45 Thank You Prof.N.K.Shukla Dr.Madhabananda Kar Dr.Sonal Asthana Dr.G.Srinivas Dr.Joydeep Dr.Das Dr.Sridhar
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