Enhancing Data Discrepancy Resolution with AI-Driven Chatbots

Enhancing Data Discrepancy Resolution with AI-Driven Chatbots

1. Welcome Enhancing Data Discrepancy Resolution with AI-Driven Chatbots K . Mallikharjun Rao B Pharmacy www.clinosol.com | follow us on social media @clinosolresearch

2. Index What is Data Discrepancy ? Introduction Process of Data Discrepancy Data Discrepancy with AI Solution Data Discrepancy workflow Conclusion www.clinosol.com | follow us on social media @clinosolresearch

3. What is Data Discrepancy ? Data discrepancy resolution in pharmacy refers to the process of identifying, investigating, and rectifying inconsistencies or discrepancies in various types of data that pharmacies manage. This includes patient records, medication orders, inventory levels, billing information, and regulatory compliance documentation Here’s a detailed look at how data discrepancy resolution is managed within the pharmacy context. Ensuring accurate data is critical to preventing medication errors, which can have severe consequences for patient health and safety www.clinosol.com | follow us on social media @clinosolresearch

4. Introduction Data discrepancy resolution in pharmacy is a critical process aimed at ensuring the accuracy, consistency, and integrity of various types of data that pharmacies handle. This includes patient information, medication orders, inventory records, billing details, and compliance documentation. The resolution of data discrepancies is essential for maintaining patient safety, complying with regulatory standards, and optimizing operational efficiency within pharmacy settings. www.clinosol.com | follow us on social media @clinosolresearch

5. Process of Data Discrepancy Identification of Discrepancy : Detecting discrepancies can happen through various checks: Inventory Checks : Discrepancies between physical and recorded inventory. Prescription Review : Discrepancies between prescribed medication and what was dispensed. Patient Profile Review : Discrepancies in patient information or history www.clinosol.com | follow us on social media @clinosolresearch

6. Data Discrepancy with AI Identification : AI systems in pharmacies often assist in tasks like inventory management, prescription analysis, and patient interaction. Discrepancies can arise from errors in AI algorithms, data input, or integration issues between AI systems and pharmacy databases. Documentation : Record discrepancies involving AI outputs or interactions. Document details such as the specific AI system involved, the nature of the discrepancy. Investigation : Investigate the root cause of AI-related discrepancies. Review AI algorithms and models to understand how decisions or recommendations are generated. www.clinosol.com | follow us on social media @clinosolresearch

7. Solution Managing data discrepancies involving AI in a pharmacy setting requires a systematic approach to ensure accuracy, patient safety, and regulatory compliance. Here's a detailed solution framework. Continuous Monitoring and Auditing :Implement regular audits of AI algorithms and systems to detect discrepancies early. Monitor data inputs and outputs to identify anomalies or inaccuracies. Root Cause Analysis : Conduct thorough investigations to determine the root causes of AI-related discrepancies. Analyze AI algorithms, models, and data sources to pinpoint where errors may have occurred. Enhanced Data Quality Control: Improve data quality management processes to ensure accurate inputs to AI systems Implement data validation checks and data cleansing techniques to maintain data integrity. www.clinosol.com | follow us on social media @clinosolresearch

8. Data Discrepancy Workflow www.clinosol.com | follow us on social media @clinosolresearch

9. Conclusion In conclusion, addressing data discrepancies involving AI in a pharmacy setting requires a multifaceted approach focused on accuracy, patient safety, and operational efficiency. By implementing robust monitoring, thorough root cause analysis, and enhanced data quality control measures, pharmacies can effectively manage and mitigate discrepancies associated with AI technologies. Collaboration with AI developers, transparent algorithm validation, and comprehensive documentation ensure timely resolution of issues and continuous improvement in AI system performance. Training pharmacy staff, ensuring regulatory compliance, and maintaining a proactive stance towards evolving technology further enhance the reliability and ethical use of AI in pharmacy operations. Ultimately, these efforts safeguard patient care, foster trust in AI-driven solutions, and support pharmacies in delivering optimal healthcare services. www.clinosol.com | follow us on social media @clinosolresearch

10. Thank You! www.clinosol.com (India | Canada) 9121151622/623/624 [email protected] www.clinosol.com | follow us on social media @clinosolresearch