Data Reconciliation Made Easy: The Power of Machine Learning

Data reconciliation is a critical aspect of managing clinical trials, ensuring that data collected from various sources are accurate, consistent, and aligned. This process involves verifying and harmonizing data across multiple systems and formats, which is essential for maintaining the integrity of the trial and meeting regulatory requirements. Traditionally, data reconciliation has been a manual, labor-intensive task prone to errors and inefficiencies.

1. Welcome DATA RECONCILIATION MADE EASY: THE POWER OF MACHINE LEARNING DIVYA. P M-PHARMACY CSRPL_STD_IND_HYD_ONL/CLS_079/062024 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 1

2. Index What is Data Reconciliation? How does reconciliation of data work? Importance of Data Reconciliation in CR Challenges in traditional DR Role of Machine learning in DR Practical applications Conclusion 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 2

3. What is Data Reconciliation (DR) Data reconciliation in clinical research is the process of ensuring the accuracy and consistency of data collected from various sources throughout a clinical trial. It involves comparing and aligning data from multiple databases, systems, and records to identify and correct discrepancies. This process is crucial for maintaining data integrity, ensuring regulatory compliance, and ultimately supporting the validity of the clinical trial results. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 3

4. How does Reconciliation of data work? 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 4

5. Importance of Data Reconciliation (DR)in CR Data Integrity : Ensures that the data used for analysis is accurate and reliable. Regulatory Compliance : Helps meet regulatory requirements and standards, reducing the risk of non-compliance . Patient Safety : Ensures that patient-related data is accurate, which is crucial for patient safety and the validity of trial results. Decision Making : Provides a solid foundation for making informed decisions based on accurate and consistent data. Trial Validity : Supports the overall validity and credibility of the clinical trial outcomes . 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 5

6. Challenges in traditional DR Time-consuming and labor-intensive Prone to human errors Difficulties in handling large and complex datasets Inconsistencies in data formats and entry errors 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 6

7. Role of machine learning (ML) in DR Automation : Reduces manual effort by automating data matching and cleansing . Accuracy : Improves data accuracy by detecting and correcting errors . Efficiency : Speeds up the reconciliation process, saving time and resources 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 7

8. Automating data matching with ML P attern Recognition : Machine learning algorithms can identify patterns and similarities in data from different sources, automatically matching records that traditional rule-based systems might miss . Fuzzy Matching : ML models can handle inconsistencies in data formats, such as typos, abbreviations, or variations in naming conventions, improving the accuracy of data matching. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 8

9. Anomaly detection Outlier Detection : Machine learning models can detect anomalies and outliers in data sets, highlighting discrepancies that need further investigation . Predictive Analysis : By analyzing historical data, ML models can predict potential reconciliation issues and proactively address them before they escalate 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 9 .

10. Data Cleansing and transformation Automated Data Cleaning : Machine learning algorithms can identify and correct errors, inconsistencies, and missing values in data sets, ensuring that the data used for reconciliation is clean and reliable. Data Standardization : ML can help standardize data from multiple sources, making it easier to compare and reconcile . Scalability Handling Large Data Sets : Machine learning algorithms can process and analyze large volumes of data much faster than manual methods, making it feasible to reconcile data from multiple sources in real-time. Adaptive Learning : ML models can continuously learn and adapt to new data patterns and reconciliation rules, improving their accuracy and efficiency over time. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 10

11. 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 the 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. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 11

12. Improved decision making Enhanced Reporting : ML-powered reconciliation tools can generate detailed reports and insights, helping stakeholders make informed decisions based on accurate and up-to-date data . Real-time Monitoring : Machine learning enables real-time monitoring of data reconciliation processes, allowing for immediate action when discrepancies are detected. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 12

13. Practical applications in CR Patient Data Matching : Ensuring consistency across different systems and trials . Adverse Event Reporting : Accurate reconciliation of adverse event data . Regulatory Compliance : Ensuring data integrity for regulatory submissions . Trial Monitoring : Continuous monitoring and reconciliation of trial data. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 13

14. Conclusion Summary : Machine learning enhances data reconciliation by improving accuracy, efficiency, and decision-making in clinical research . Future Outlook : Continuous advancements in ML will further streamline clinical data management and reconciliation. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 14

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