The document discusses the revolutionizing of clinical trial data quality through intelligent query management and emphasizes the importance of data accuracy and integrity. It highlights advancements in technology such as AI, blockchain, and natural language processing, which enhance data management and streamline processes. The future direction includes patient-centric trials, real-world evidence, and decentralized clinical trials, positioning technology as a key facilitator of progress in clinical research.

1. Welcome
Revolutionizing Clinical Trial Data Quality through Intelligent Query
Management
B.Sudha Rani
M. Pharmacy
CSRPL_STD_IND_HYD_ONL/
CLS_075/062024
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2. Index
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• Introduction to Clinical trials
• Data Management in Clinical trials
• Importance of Data quality in Clinical trials
• Technology advancements in Clinical trial Data Analysis
• Benefits of Intelligent Query Management
• Advantages and Limitations of Using artificial intelligence(AI) in Clinical Trials.
• Future Direction
• References
• Conclusion

3. Introduction to Clinical trials
Clinical trials are studies performed with human subjects to test new drugs or combination of drugs, new
approaches to surgery or radiotherapy or procedures to improve the diagnosis of disease and quality of life
of patient.
Clinical trials in nutshell:
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Introduction
• Clinical research often focuses on finding approaches for early diagnosis of
disease, preventing health issues, or enhancing quality of life for those dealing
with chronic diseases. An effective clinical data management (CDM) is
paramount in order to establish the accuracy, reliability and integrity of research
outcomes.
• Traditional query systems often manual and labor intensive, struggle to keep
pace with increasing volume and complexity of clinical data.
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Data Management in Clinical trials
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Clinical
Data
Managem
ent
Systems
Data
Managem
ent Plan
Case
Report
Forms
Data
Validation
Module
Data
Coding
Data base
lock
Clinical
trial
database
design

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Importance of Data quality in Clinical trials
Several data quality metrics can be used to assess the quality of data in clinical
trials. Some of the most important metrics include:
 Accuracy
 Completeness
 Consistency
 Timeliness
 Relevance
 Error ratio
 Uniqueness
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Technology advancements in Clinical trial Data Analysis
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Artificial
Intelligence
(AI)
Block Chain
Technology
Data Lakes
And Cloud
Computing
Natural
Language
Processing
(NLP)

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Technology advancements in Clinical trial Data Analysis
Artificial Intelligence(AI):
AI excel inefficiently processing extensive datasets, detecting patterns, forecasting results, and even
proposing tailored treatment strategies based on patient information.
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Technology advancements in Clinical trial Data Analysis
Block Chain Technology:
 Ensures Data Security and transparency.
 Builds trust among Stakeholders &simplifies data sharing.
 Effectively used in electronic trial Masterfile(eTMF)
clinical document management.
Data Lakes And Cloud Computing:
 Facilities the storage and analysis of massive datasets.
 Accessible from everywhere.
 Enhances collaboration and data sharing among
researches.
Natural Language Processing (NLP):
 Extract valuable insights from unstructed clinical notes,
allowing researches to utilize previously untapped information.
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Benefits of Intelligent Query Management
The Adoption of Intelligent Query Management offers numerous benefits:
 Improves Accuracy by automating data checks , reducing the likelihood of human error.
 Higher data Integrity and more reliable trial outcomes. Another major advantage is efficiency with
faster query resolution times accelerating the overall trial process.
 Consistency of data handling and quality is obtained by standardization process .
 Intelligent systems are inherently scalable , making it easier to manage large volumes of data in
extensive clinical trials.
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Advantages and Limitations of Using artificial intelligence(AI)
in Clinical Trials.
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Future Direction
The horizon of clinical trial data is brimming with excitement and harbors great potential for
advancements in healthcare technology. Numerous emerging trends warrant close attention.
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Future Direction
Patient-centric trials:
Incorporating patient-
generated data through like
wearables digital health
solutionsand mobile apps
allows a more holistic view
of a patient's health. This
trend paves the way for
patient-centric trials
prioritizing individual needs
and preferences.
Real-world evidence
(RWE):
RWE from sources like
electronic health records and
insurance claims is
increasingly used to
supplement traditional
clinical trial data. It provides
insights into how treatments
perform in real-world
settings.
AI-driven drug
discovery:
AI and machine learning
(ML) are poised to play a
more significant role in drug
discovery by predicting drug
interactions, identifying
potential side effects, and
streamlining the drug
development process.
Decentralized clinical
trials:
Advances in remote
monitoring and telehealth
enable decentralized clinical
trials reducing the need for
patients to visit physical trial
sites. This not only increases
participation but also
improves data collection.

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Conclusion
• In conclusion intelligent query has the potential to
revolutionize data quality in clinical trials.
• By leveraging the advanced technologies ,it addresses the
limitations of traditional methods enhances the accuracy and
efficiency.
• While challenges persist, technological advancements are
transforming the landscape, making clinical trial data more
accessible and valuable than ever before.
• In the future, patient-centricity, AI, ML, RWE, and
decentralized studies will shape the next era of clinical
research, bringing closer medical miracles that can change
lives and improve healthcare.
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References
https://www.linkedin.com/pulse/data-quality-metrics-paradigmittechnologyservice
s-v2ezc/
https://imagecorelab.com/the-importance-of-data-management-in-clinical-trials/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10720846/
https://www.clinvigilant.com/revolutionizing-electronic-data-capture-in-clinical-tri
als-a-game-changer-for-researchers/
https://pharmanewsintel.com/features/revolutionizing-clinical-trial-data-tracking-a
nalysis-with-technology
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15. ThankYou!
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