Oracle Clinical is a data capture and validation system that allows for quick and easy data entry across globally distributed study sites. It supports single, double, and blinded double data entry. Any unexpected data is flagged as a discrepancy for later review. Discrepancies are tracked in tables like DISCREPANCY_ENTRIES and can be assigned to data clarification forms. The system also takes advantage of password protection and access controls for security.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Overview of Validation in Pharma_Katalyst HLSKatalyst HLS
Introduction to Validation Concepts in Pharma, Bio-Pharma, Medical Device, Cosmetics, Food, Beverages industry.
Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com
Over the past decade, CDISC data standards have become the FDA preferred method for the data submission. In fact, the FDA is considering a proposed rule change that would mandate the submission of data in CDISC Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) formats for all new marketing applications. However, the implementation of this standard has proved to be intimidating to many with only a very small percentage of drug companies actually developing and submitting data in this format.
During the webinar, Thomas Kalfas, an experienced data management professional and CDISC subject matter expert, shared his knowledge and strategies for implementing CDSIC. Topics included a brief review of CDISC, implementation challenges, and insight into the best timing for implementation.
Migrating clinical studies from one database to another (such as Oracle Clinical to Oracle Clinical or Oracle Clinical to Oracle InForm or Medidata Rave), is a complex process that requires a thorough understanding of clinical data management, technology, and the regulations that govern clinical trials.
In this SlideShare on clinical study migrations we:
Discuss reasons to migrate a clinical study
Provide an overview of the clinical study migration process
Look at validation, technical, and business considerations for migrating a clinical study
Present real-world case studies
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
Distributed database notes in this note all important topics covered.DDB notes especially belong to computer science and IT students. Every topic explains very precisly.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Introduction to Oracle Clinical Overview in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Overview of Validation in Pharma_Katalyst HLSKatalyst HLS
Introduction to Validation Concepts in Pharma, Bio-Pharma, Medical Device, Cosmetics, Food, Beverages industry.
Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com
Over the past decade, CDISC data standards have become the FDA preferred method for the data submission. In fact, the FDA is considering a proposed rule change that would mandate the submission of data in CDISC Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) formats for all new marketing applications. However, the implementation of this standard has proved to be intimidating to many with only a very small percentage of drug companies actually developing and submitting data in this format.
During the webinar, Thomas Kalfas, an experienced data management professional and CDISC subject matter expert, shared his knowledge and strategies for implementing CDSIC. Topics included a brief review of CDISC, implementation challenges, and insight into the best timing for implementation.
Migrating clinical studies from one database to another (such as Oracle Clinical to Oracle Clinical or Oracle Clinical to Oracle InForm or Medidata Rave), is a complex process that requires a thorough understanding of clinical data management, technology, and the regulations that govern clinical trials.
In this SlideShare on clinical study migrations we:
Discuss reasons to migrate a clinical study
Provide an overview of the clinical study migration process
Look at validation, technical, and business considerations for migrating a clinical study
Present real-world case studies
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
Distributed database notes in this note all important topics covered.DDB notes especially belong to computer science and IT students. Every topic explains very precisly.
Tips and Techniques for Improving the Performance of Validation Procedures in...Perficient, Inc.
Ensuring the validity of patient data in your clinical data management and EDC system is essential. However, without a way to programmatically identify discrepancies and inconsistencies, such a task can inadvertently leave bad data in your system. Through validation procedures, an endless assortment of expressions and formulas, Oracle Clinical offers the powerful ability to clean and compare patient data.
In this slideshare, Perficient's Dr. Steve Rifkin, a leading expert in Oracle Clinical, demonstrates the structure of validation procedures, as well as provides various tips and techniques for developing procedures that improve the performance of edit checks.
Presentation given to the BCS Data Management Specialist Group by Steve Higgins of CSC on healthcare data management
A video of the presentation is available at http://youtu.be/Fqm4XDyA6fI
Extracting a Force Readiness picture from your big dataOcean Software
Grant McHerron delivers this presentation at MILCIS 2017, on how to extract a Force Readiness picture from the big data, within your existing systems.
He covers what sort of questions are being asked, where to look for the information, and how to present it.
This seminar will provide a review of the leading mid-market accounting systems in one power packed session (Microsoft Dynamics GP & SL, Sage, Intacct). If you need to be updated on the current crop of ERP systems, this seminar is ideal. We will walk you through the evaluation process while providing you with key insights on organizational strategy in selecting the best solution for your organization.
Building a mind map for test data management.
Overview
1. Test data source
2. Extract or create data
3. Transform data
4. Provision
5. Target
Source: http://debasishbhadra.blogspot.com/2013/12/create-your-own-mindmap-for-test-data.html
The Search for the Single Source of Truth - Eliminating a Multi-Instance Envi...eprentise
Changes in financial reporting requirements have transformed the fixed asset accounting framework. International Financial Reporting Standards (IFRS) require fixed assets to be recorded at cost, but there are two accounting models – the cost model and the revaluation model. So what’s the difference, and when should you use each? This session will address fixed asset accounting and reporting under both models and how each is accounted for in Release 12.
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Similar to Data Capture And Validation_Katalyst HLS (20)
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
All about Clinical Trials_Katalyst HLSKatalyst HLS
Introduction to All about Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
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2. 2
AGENDA
• DATA CAPTURE AND VALIDATION
• DE-NORMALIZING THE NORMALIZING STRUCTURE
• DISCREPANCY MANAGEMENT
3. 3
DATA CAPTURE AND VALIDATION
• OBTAINING "CLEAN" DATA IS FASTER AND SIMPLER WITH ORACLE
CLINICAL. THE ORACLE CLINICAL DATA ENTRY SYSTEM IS BOTH QUICK
AND EASY TO USE.
• GLOBALLY DISTRIBUTED DATA MANAGEMENT WITH ORACLE CLINICAL
MEANS:-
• THE SAME STUDY CAN BE PERFORMED AT SEVERAL LOCATIONS AROUND THE
WORLD WITH NO MORE EFFORT THAN CONDUCTING THE STUDY FROM A
SINGLE LOCATION.
• STUDY DEFINITIONS, INCLUDING AMENDMENTS, ARE AUTOMATICALLY
PROPAGATED TO ALL LOCATIONS WHICH ARE CONDUCTING THE STUDY.
• EACH LOCATION INDEPENDENTLY MANAGES ITS LOCAL STUDY SITES,
INCLUDING DATA VALIDATION AND CORRECTION. FURTHERMORE, DATA CAN
BE INCREMENTALLY REPLICATED TO ANY LOCATION FOR QUERY AND
ANALYSIS.
4. 4
DATA CAPTURE AND VALIDATION
• OC AS A DATA ENTRY TOOL
• THE SYSTEM SUPPORTS THREE MODES OF DATA ENTRY:
• SINGLE ENTRY
• DOUBLE ENTRY WITH ONLINE VERIFICATION, AND
• A BLIND DOUBLE ENTRY.
• THERE IS AN INTERACTIVE RECONCILIATION PROCESS FOR BLINDED
ENTRIES WHERE THE CONFLICTS ARE HIGHLIGHTED AND THE REVIEWER IS
GUIDED TO EACH QUESTION THAT NEEDS RECONCILIATION.
5. 5
DATA CAPTURE AND VALIDATION
• ORACLE CLINICAL TAKES ADVANTAGE OF MOST OPERATING SYSTEM’S
PASSWORD PROTECTION, AS WELL AS OF ORACLE8I AND ORACLE FORMS
SECURITY FEATURES IN ORDER TO CONTROL ACCESS TO THE APPLICATION ITSELF
AND TO VARIOUS MODULES WITHIN THE APPLICATION.
• ORACLE CLINICAL ALLOWS USERS TO CAPTURE ALL THE DATA AS RECORDED ON
THE CRF, THEN ADDS ITS OWN LEVEL OF SECURITY TO CONTROL THE ACCESS OF
INDIVIDUALS TO SPECIFIC STUDIES.
• ANY UNEXPECTED DATA (FOR EXAMPLE, THE VALUE “TRACE” IN A
NUMERICALLY DEFINED DATA FIELD) IS ACCEPTED AND TRACKED AS A
DISCREPANCY FOR LATER REVIEW.
• IT IS ALSO POSSIBLE TO CREATE MANUAL DISCREPANCIES AND ENTER
INVESTIGATOR COMMENTS, WHICH ARE TRACKED WITH ALL OTHER
DISCREPANCIES.
• IT IS ALSO POSSIBLE TO PRE-DEFINE “ALPHA DATA CODES”, WHICH ARE
ALLOWABLE CHARACTER VALUES IN NUMERIC OR DATE FIELDS.
• IF THE USER ENTERS ONE OF THESE ALPHA CODES, NO DISCREPANCY IS
CREATED. THIS ALLOWS FOR STANDARD PROCESSING OF DATA SUCH AS
“ND” (FOR NOT DONE) IN A NUMERIC FIELD OR “CONT” (FOR CONTINUING)
IN A DATE FIELD.
8. 8
DATA CAPTURE AND VALIDATION-ENTRY OF DCM
DATA
• THE RESPONSES TABLE CONTAINS ALL THE RESPONSES TO ALL QUESTIONS IN ALL STUDIES
IN THE SYSTEM.
• RECEIVED_DCM_ID POINTS TO RECEIVED_DCMS WHICH PROVIDES THE STUDY, PATIENT
AND EVENT TO WHICH THE RESPONSE APPLIES. CLINICAL_STUDY_ID IMPROVES VIEW
PERFORMANCE.
• DCM_QUESTION_ID PROVIDES THE QUESTION TO WHICH THE RESPONSE WAS GIVEN VIA
THE DCM_QUESTIONS TABLE. DCM_QUESTION_GROUP_ID IS DE-NORMALIZED BUT
IMPROVES VIEW PERFORMANCE.
• REPEAT_SN GIVES THE REPEAT NUMBER IF A REPEATING QUESTION GROUP OR IS 1 IF A
NON-REPEATING GROUP.
9. 9
DATA CAPTURE AND VALIDATION-ENTRY OF DCM DATA
• DISCREPANCY_INDICATOR FLAGS IF THERE IS A DISCREPANCY ON THE RESPONSE.
• VALUE_TEXT CONTAINS THE RESPONSE TO THE QUESTION WHICH WILL CONVERT AS
DEFINED.
• EXCEPTION_VALUE_TEXT CONTAINS THE TEXT AS ENTERED IF THERE WOULD BE A
CONVERSION PROBLEM.
10. 10
• SECOND_PASS_INDICATOR SHOWS ENTRY STATUS OF THE DATA FOR INTERNAL USE OF
SYSTEM FOR AUDIT TRAILS AND REPORTS.
• VALIDATION_STATUS IS A THREE BYTE INDICATOR INDICATING FURTHER DISCREPANCY STATUS
INFORMATION
• DATA_CHANGE_REASON_TYPE_CODE HAS THE CODED REASON FOR THE CHANGE
• AUDIT_COMMENT_TEXT HAS FREE TEXT REASON FOR THE CHANGE
• DATA_COMMENT_TEXT IS ANY INVESTIGATOR COMMENT ENTERED FOR THE RESPONSE
DATA CAPTURE AND VALIDATION-ENTRY OF DCM DATA
11. 11
DE-NORMALIZING THE NORMALIZED STRUCTURE
• FOR REASONS OF BOTH DATA INTEGRITY AND PERFORMANCE, IT IS THE GOAL OF A
PRODUCTION DATABASE, TO REPRESENT EACH FACT OR DATA ITEM IN ONLY ONE
PLACE.
• THE DATA REDUNDANCY NOT ONLY CAUSES POTENTIAL ERRORS IN DATA
MAINTENANCE; IT ALSO REQUIRES ADDED STORAGE.
• NORMALIZATION IS THE TECHNICAL NAME FOR THE PROCESS THAT REVEALS AND
THEN ELIMINATES DATA REDUNDANCY. THE NORMALIZATION IN RDBMS IS A KEY
FACTOR OF A GOOD DATABASE DESIGN.
• THE NORMALIZED DATABASE IN THE RELATIONAL DATABASE SYSTEM GIVES
ADVANTAGES SUCH AS ELIMINATION OF REDUNDANCY, EASE OF USE, UPDATE, AND
MODIFICATION.
• EACH TABLE IN A NORMALIZED DATABASE HAS A PRIMARY KEY, WHICH IS A FIELD OR
FIELDS THAT UNIQUELY IDENTIFIES EACH RECORD IN THE TABLE.
• NORMALIZATION IS DONE BY MATCHING THE TABLES WITH FIRST, SECOND, AND THEN,
THIRD NORMAL FORM.
12. 12
• CDISC RECOMMENDS THAT THREE, (LABS, VITAL SIGNS AND ECG), OF THE STANDARD
SAFETY DOMAINS BE IN A NORMALIZED FORMAT FOR STATISTICAL ANALYSIS AS WELL
AS A DE-NORMALIZED, VITAL SIGNS AND ECG ONLY FORMAT FOR ONLINE REVIEW
USING CDISC ODM (OPERATIONAL DATA MODEL) XML.
DE-NORMALIZING THE NORMALIZED STRUCTURE
13. 13
DE-NORMALIZING THE NORMALIZED STRUCTURE
• EXTRACT VIEWS ARE GENERATED BY THE VIEW BUILDER AND DE-NORMALIZE
THE RESPONSE DATA RECORDED IN THE DCM
• THE ACTUAL EXTRACT VIEW IS MORE COMPLEX
• RESPONSES DOES NOT HAVE QUESTION NAME - ONLY A POINTER TO
DCM_QUESTIONS - AND MAY HAVE MULTIPLE VERSIONS FROM SELF AUDITING
• ACTUAL VIEW JOINS MULTIPLE TABLES
• RESPONSES
• RECEIVED_DCMS
• DCMS
• DCM_QUESTIONS
• DCM_QUESTION_GROUPS
17. 17
DISCREPANCY MANAGEMENT
• WHEN WE ENTER INTO DISCREPANCY_ENTRIES DISCREPANCY IS CREATED BY ASSIGNING
DISCREPANCY_ENTRY_ID
• VALUES RELATED TO UNIVARIATE DISCREPANCIES ARE FOUND IN THE RESPONSES TABLE THROUGH
THE RECEIVED_DCM_ID AND REPOSNSE_ID
• PROCEDURES WHICH GENERATES A MULTIVARIATE DISCREPANCY ARE MARKED AS REPORTED IN THE
PROCEDURE AND IT IS LISTED IN THE VALIDATION_REPORTED_VALUES TABLES
• DCF_ID IN DISCREPANCY_ENTRIES INDICATES TO WHICH DCF THE DISCREPANCY IS ASSIGNED
A NULL VALUE FOR DCF_ID INDICATES DISCREPANCY IS NOT PART OF ANY DCF
18. 18
DISCREPANCY MANAGEMENT
DISCREPANCY_ENTRY_REVIEW_HISTORY MAINTAINS AUDIT TRAIL OF CHANGES MADE
TO
DISCREPANCY COMMENTS AND REVIEW OR RESOLUTION STATUS FIELDS
TABLE IS POPULATED BY A DATABASE TRIGGER
ENHANCED DISCREPANCY MANAGEMENT SYSTEM IN VERSION 4.0 ALLOWS FOR
CREATION OF UP TO THREE COMMENTS WITH EACH DISCREPANCY
THESE COMMENTS CAN BE CONFIGURED USING “STANDARD TEXTS” TO ALLOW
BETTER MESSAGES FOR ALL TYPES OF DISCREPANCIES
DCFS WHICH ARE PART OF VERSION 4.0 ARE EASIER TO CREATE AND EDIT WITH RESPECT
TO THE DEFAULT TEXTS
TEXTS FOR EACH DISCREPANCY IS STORED IN THE NEW DCF_DISCREPANCIES TABLE
19. 19
QUESTIONS
• DATA CAPTURE AND VALIDATION MEANS OBTAINING "CLEAN" DATA IS
FASTER AND SIMPLER WITH ORACLE CLINICAL. THE ORACLE CLINICAL
DATA ENTRY SYSTEM IS BOTH QUICK AND EASY TO USE. [TRUE / FALSE]
ANS = TRUE
• ORACLE CLINICAL TAKES ADVANTAGE OF MOST OPERATING SYSTEM’S
PASSWORD PROTECTION, AS WELL AS OF ORACLE8I AND ORACLE
FORMS [TRUE / FALSE] ANS = TRUE
• THE RESPONSES TABLE DO NOT CONTAINS ALL THE RESPONSES TO ALL
QUESTIONS IN ALL STUDIES IN THE SYSTEM. [TRUE / FALSE] ANS = FALSE
• VALIDATION_STATUS IS A THREE BYTE INDICATOR INDICATING FURTHER
DISCREPANCY STATUS INFORMATION [TRUE / FALSE] ANS = TRUE