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Real-Time Data Analytics in Clinical Trials
- 1. Welcome REAL TIME DATAANALYTICSIN CLINICAL TRIAL Rushikesh Gopal Bavaskar B. Pharmacy 105/072024 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 1
- 2. INDEX Introduction Whatis Real-Time Data Analytics? Benefits of Real-Time Data Analytics in Clinical Trials Components of Real-Time Data Analytics Case Studies Challenges and Considerations Future Trends in Real-Time Data Analytics Conclusion References 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 2
- 3. INTRODUCTION 04/08/2024 www.clinosol.com | followus on social media @clinosolresearch 3 Real-time data analytics :- Real-time data analytics in clinical trials refers to the immediate or near-immediate analysis of data as it is collected during a clinical trial. How to analysis Data Collection Data Integration Real-Time Monitoring Adaptive Trials: Safety and Efficacy
- 4. WHAT IS REALTIME DATAANALYSIS? Real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data. On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events happen. 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 4
- 5. BENEFITS OF REAL-TIMEDATAANALYTICS IN CLINICAL TRIALS Make Faster, Better Decisions :- Using a real- time analytics tool , you can have in-the-moment understanding of what’s happening in your business. Reduce Fraud, Cybercrime, and Outages :- Issues such as fraud, security breaches, production problems, and inventory outages Meet Customer Expectations :- Customers today rely on applications that deliver time-sensitive data–such as weather, navigation, and ride-sharing apps 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 5
- 6. COMPONENTS OF REAL-TIMEDATAANALYTICS Also include data distillation, model development, validation and deployment, real-time scoring, and model refresh. 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 6 Feed • Real time data stream entering the information system velocity • Incoming tabular point, polyline or polygon data Sources • Utilize to lode statistic or near real time detailing big time data analysis • Utilize the secondary data in application tool such as join features filters by geometry Tool • Processing or analysis of event received from feeds • Involve non or multiple tool
- 7. CASE STUDY objective 04/08/2024 www.clinosol.com |follow us on social media @clinosolresearch 7 Consen t manag ement . Drug accoun t-ability Adhere nce to protoco ls Reduce medical error To enhanc e care coordin ation . To improve health of population To minimiz e froud and abuse . Clinical systeme
- 8. Electronic HealthRecords :- the electronic health record is the electronic version of the client data found in the traditional paper record. Clinical Trial Management System :- The CTMS is a specialized productivity tool that helps your busy study team to plan, track and monitor the study effectively. 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 8 Implementation of Real-Time Data Analytics
- 9. CHALLENGES AND CONSIDERATIONS Challenges:- The integration of real-time data from various sources, including electronic health records (EHRs), wearable devices, and lab results, can be complex. Considerations :- Implement robust data validation protocols and standardized data formats. Use middleware or data integration platforms to consolidate data streams effectively 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 9
- 10. FUTURE TRENDS INREAL-TIME DATAANALYTICS 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 10 Integration of Advanced Analytics • Predictive Analytics • Natural Language Processing Wearables And Remote Monitoring • Continuous Data Collection • Real- Time Feedback Blockchain Technology • Data Integrity • Efficient Data Sharing
- 11. • Predictive Analytics:Using machine learning algorithms to predict outcomes and identify potential issues before they arise. • Natural Language Processing (NLP): To analyze unstructured data from clinical notes, research articles, and other sources, improving the ability to extract relevant information quickly. • Continuous Data Collection: Wearable devices and remote monitoring tools provide a steady stream of real-time data on patients’ vital signs, activity levels, and adherence to treatment. • Real-Time Feedback: These tools allow for immediate intervention if adverse effects are detected, improving patient safety and trial integrity. • Data Integrity: Blockchain can enhance the security and transparency of clinical trial data by creating immutable records, reducing the risk of data tampering or fraud. 04/08/2024 www.clinosol.com | follow us on social media @clinosolresearch 11
- 12. CONCLUSION 04/08/2024 www.clinosol.com | followus on social media @clinosolresearch 12 Real-time data analytics in clinical trials enhances the speed and accuracy of decision- making. By continuously analyzing data as it's collected, researchers can identify trends, detect safety issues, and assess efficacy more quickly. This approach allows for adaptive trial designs, where protocols can be adjusted based on interim results, potentially improving outcomes and reducing time to market for new treatments. In summary, real-time analytics in clinical trials leads to more responsive and efficient research processes.
- 13. ThankYou! www.clinosol.com (India | Canada) 9121151622/623/624 info@clinosol.com 04/08/2024 www.clinosol.com| follow us on social media @clinosolresearch 13