Data Reconciliation Made Easy: The Power of Machine Learning.pdf

Welcome
DATA RECONCILIATION MADE EASY: THE POWER OF
MACHINE LEARNING
J. HARISH
M PHARMACY
CSRPL_STD_IND_HYD_ONL/CL
S_028/05/2025
24/06/2025
www.clinosol.com | follow us on social media
@clinosolresearch
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Index
• What is Data Reconciliation (DR)?
• How does reconciliation work?
• Importance of data reconciliation in CR
• Challenges in DR
• Role of Machine learning in DR
• Practical Applications
• Conclusion
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What is Data Reconciliation (DR)?
Data reconciliation is the systematic process of comparing data from different sources to
ensure its accuracy, consistency, and completeness across systems. This practice is essential for
organizations that handle large volumes of data, as inconsistencies or errors in data can lead to
significant operational challenges, inefficiencies, and financial losses.
● The goal of data reconciliation is to identify discrepancies between data sets and correct
them to create a unified and accurate view of the data.
● Data reconciliation is often required when data comes from multiple sources, systems, or
departments that may have different data entry standards, formats, or update cycles.
● Data reconciliation techniques and technologies enable organizations to identify and fix
errors that occur when data is entered into systems, inaccuracies that are introduced over
time, and structural differences in source systems and data stores that compromise data
integrity.
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How does Reconciliation works?
Data reconciliation typically commences when data is transferred between systems or
databases. This could be during processes like data migration, system integration, or even
routine data transfer between departments. The main steps include:
• Comparison
• Identification
• Resolution
• Validation
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Importance of Data Reconciliation in CR
Data reconciliation plays a key role in maintaining accuracy, compliance, and reliability in clinical
trials. Without proper reconciliation, errors can slip through, leading to flawed conclusions or even
regulatory setbacks.
Preventing Data Discrepancies That Can Impact Study Outcomes
Clinical trials generate large volumes of data from different sources, including electronic case report
forms (eCRFs), laboratory systems, and wearable devices. If inconsistencies go unchecked, they can
distort study results, leading to unreliable conclusions. Reconciling data across multiple platforms
ensures consistency and minimizes the risk of inaccurate findings.
Meeting Regulatory Compliance Requirements
Discrepancies between datasets can raise compliance issues, delaying drug approvals or leading to
costly rework. With reconciliation steps in place, research teams can align with guidelines such as 21
CFR Part 11 and Good Clinical Practice (GCP), reducing the risk of regulatory concerns.
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Enhancing Participant Safety and Data Integrity
● Clinical data errors can lead to misinterpretation of serious adverse events or incorrect efficacy
assessments.
● When data is consistent across all the systems used in the clinical trial, researchers can identify and
correct errors that might compromise participant safety. Ensuring that all datasets match helps
maintain the integrity of the clinical trial data and improves the overall quality of the trial.
Refining Database Lock and Submission Processes
● A database lock is the last step in clinical data management. This means researchers need to make
sure the data set is complete, correct, and verified before analysis. A systematic data reconciliation
system makes this easier and also simplifies the submission process to regulatory agencies.
Improving Decision-Making for Sponsors and CROs
● Accurate data allows sponsors and contract research organizations (CROs) to make informed
decisions about a trial’s progress. If reconciliation is neglected, incorrect data can lead to misguided
conclusions, which can impact investment strategies and study continuation. Consistently verifying
data across all sources supports better strategic planning and trial management.
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Challenges in Data Reconciliation
• Data Migration
• Discrepancies between multiple data sources
• Delayed data transfers and synchronization issues
• Resolving queries and missing data
• Compliance and regulatory challenges
• Managing large and complex datasets
• Human errors
• Outdated systems
• Complex integration requirements
• Standardise workflows and training
• Coordinate with protocol amendments
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Role of Machine Learning in DR
● Connects to most/all data sources (the new source as well as existing sources to match,
plus existing structured data sources and ETL layer)
● Ingests data in a wide range of formats (csv, XML, feed, SQL, NoSQL, etc.)
● Processes the data in-memory to maximize speed and capacity
● Has a built-in data engine that automatically “learns” the data sources and patterns,
analyzes it for likely matches across multiple data sets, highlights reconciliation exceptions
/ mismatches, and presents actionable “to do” lists to resolve data issues
● Has an easy-to-use interface that helps analysts quickly build data control rules in a
central location with the ability to implement automated approval processes
● Records all activities in an auditable format
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Process automation:
Machine learning algorithms can be used to analyze
and understand existing processes and identify the
areas where automation can be applied. By learning
from historical data and patterns, machine learning
models can automate respective and rule-based tasks,
improving efficiency and reducing human
intervention.
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Intelligent decision making:
Machine learning algorithms can be trained on large datasets to make intelligent decisions.
They can be used to propose correction journal entries to rectify balances or clear open items.
Anomaly detection:
Machine learning models can be trained to identify anomalies or deviations from normal
patterns in data. they can be used to analyze trial balances to identify the anomalies
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Anomaly detection in ECG

Integration with existing systems
• Seamless Integration:
Machine learning tools can be integrated with existing data management and reconciliation
systems, enhancing their capabilities without requiring a complete overhaul of existing
infrastructure.
• API Integration:
Many ML platforms offer APIs that allow for easy integration with various data sources and
applications, facilitating a more streamlined reconciliation process.
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Practical applications in Clinical Research
The role of ML in preclinical drug discovery and
development research:
Successful clinical trials need extensive preclinical inquiry and
planning, during which viable candidate compounds and targets are
discovered and an exploratory approach for obtaining regulatory
clearance is created. Mistakes made during this phase might delay the
identification of potential medications or lead to the failure of clinical
trials. Researchers may use ML to use prior and ongoing research to
reduce inefficiencies in the preclinical phase.
The role of ML in clinical trial participant
management
The administration of clinical trial participants include selecting
target patient populations, recruiting them, and retaining them.
Machine learning techniques can help with more efficient and
equitable participant identification, recruitment, and retention.
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Data collection and management:
The application of ML in clinical trials may alter the data
gathering, management, and analysis methodologies
necessary. However, machine learning approaches can
assist overcome some of the challenges connected with
missing data and data collection in the actual world.
Precision Medicine:
ML helps to tailor treatment regimens by matching
patient biomarker profiles with expected treatment
results, resulting in more effective and ethical clinical trial
designs.
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Conclusion:
Summary:
Data reconciliation ensures accurate and consistent clinical data by comparing information from
different sources. It helps avoid errors, supports regulatory compliance, and protects patient
safety. Machine learning simplifies this process by automatically detecting mismatches and
speeding up corrections. This leads to better decision-making and reduced manual effort.
Future outlook:
Machine learning will make data reconciliation faster, smarter, and more predictive in the
future. Real-time validation and seamless integration with existing systems will improve
efficiency. Automation will minimize errors and support regulatory readiness. Overall, it will
enhance the quality and reliability of clinical trials.
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References:
• Data Reconciliation: An Introductory Guide; https://www.dock.io/post/data-reconciliation
• The role of machine learning in clinical research: transforming the future of evidence
generation; https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05489-x
• https://www.infosysbpm.com/offerings/functions/finance-accounting/insights/documents/re
conciliation-in-the-age-of-machine-learning.pdf
• Data Reconciliation Explained: From Basics to Best Practices;
https://www.tookitaki.com/compliance-hub/what-is-reconciliation
• Data Reconciliation in Clinical Data Management: An Overview;
https://cdconnect.net/data-reconciliation-in-clinical-data-management/
• The Role of Reconciliation in Clinical Data Management;
https://www.quanticate.com/blog/reconciliation-in-clinical-data-management
• The Smart Approach to Data Reconciliation: Docyt’s AI & Machine Learning Solution;
https://docyt.com/article/the-smart-approach-to-data-reconciliation-docyts-ai-amp-machine
-learning-solution/
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ThankYou!
www.clinosol.com
(India | Canada)
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info@clinosol.com
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