Hey everyone. Firstly I would like to thank. Thank queue for this opportunity to present my research on LLM powered self-auditing frameworks for healthcare data pipelines. So this presentation will share how fundamental breakthrough in healthcare data quality assurance has been achieved using LLMs with novel prompting strategies that represent a paradigm shift from traditional auditing approaches. So coming to the problem I would like to discuss about the problem first. So currently healthcare organizations manage incredibly complex data ecosystems like modern healthcare institutions process millions and billions of. EHR electronic health records lab results imaging studies prescriptive data and admin data through intricate webof interconnected systems like Epic, Cerner, and so on. As these systems grow in complexity maintaining data pipeline integrity has emerged as a critical challenge. The consequences extend far beyond technical inconveniences data pipeline inconsistencies can directly affect patient safety treatment efficacy, and regulatory compliance. So current research shows that manual audits examine only 35% of trans transformational logic while manual methods detect merely like 52% of known pipeline issues in controlled environments. This represents a substantial gap that this LLM based framework addresses. Coming to current limitations we discussed about the problem. And also I would like to highlight about the current limitations. So traditional auditing approaches face significant limitations. Manual reviews are incredibly labor intensive, requiring specialized expertise spanning clinical. Knowledge database architecture and statistical analysis. So they typically examine only a fraction of the total data transformations. And rule-based verification systems offer greatly greater scalability, but focus primarily on structural and syntactic validations rather than complex semantic relationships. Or clinical appropriateness. So most critically, these methods consistently fail to detect logical inconsistencies related to clinical guidelines and business rules. Precisely. This is where the highest risk errors occur in the healthcare settings. Now since we have discussed about the problem and the re limitations, now I want to highlight the solution part. So this LLM powered framework addresses these limitations through sophisticated prompt engineering that enables expert level semantic analysis. The core innovation is a hierarchical prompting strategy combined with a chain of thought reasoning workflows that systematically decompose complex healthcare auditing tasks while maintaining awareness of cross domain dependencies. The methodology achieved a 42% improvement in error detection sensitivity, and a 35. Percent reduction in false positive rates compared to standard prompting approaches with along with this, there is a remarkable 58% growth in detecting complex multi condition clinical protocols. Now I would like to talk about the framework architecture. So this framework implements a multi-layered architecture with five integrated components. So the data extract layer interfaces with existing healthcare systems using read-only connections and API based interactions. The Prompt Generation engine formulates contextually appropriate instructions, incorporating clinical guidelines historical data patterns, and organization specific business rules. The LLM orchestration service manages model interactions and response parsing the analysis, interpret interpretation module, and translates LLM outputs into actionable insights while the audit management system tracks findings, recommendations, and remediation activities. This comprehensive architecture processed substantially more distinct data transformations per pipeline compared to the traditional sampling based approaches. So that's about the architecture. Now I would like to highlight the prompting strategy. So there is a three tier prompting hierarchy represents a significant methodological advancement. Structural prompts guide the LLM to analyze syntactic and architectural. Elements like SQLs and taxes, validation, ETL, pipeline connectivity and data type, consistencies and so on. And semantic prompts focus on clinical appropriateness, like guideline adherence verification, temporal logic validation, and cross domain consistency checking. So the comparative prompts evaluate consistency between components through version comparison and cross system reconciliation. The chain of thought technique enables structure reasoning that mirrors expert analysis patterns significantly improving complex logical inconsistency de detection. That's about the prompting strategy. Now I would like to discuss about the results implementation results. So the quantitative results demonstrate transformative improvements. The trans, the fa, the framework achieved, and 87% detection rate compared to 52% for manual methods, which is like a 67 person improvement. And also 63% for role-based systems, which is like a 30. 38% enhancement. The resource requirements decreased by 70%, like from 2,400 to 720% hours annually, while simultaneously increasing pipeline coverage from 35% to 92%. So this represents four 85% compound efficiency gain economically organizations achieve around like. $340,000 in annual savings with a 4.4 month ROI payback period which by delivering a two 72% annual return on investment. And. There are some case study highlights that I would like to discuss. So the implementation across multiple healthcare organizations revealed the frameworks real world impact at Northeast Regional Health System. The audit discovered. Incomplete medication reconciliation logic affecting patient safety alerts. Which is a critical issue that had evaded routine manual reviews. Western academic medical centers sepsis bundle compliance dashboard contained discrepancies between documented specifications and implemented calculations. And the Southern Community Health Network had outdated heart failure protocols that did not reflect latest a HA or a CC guideline updates. So these findings demonstrate the frameworks ability to identify high impact issues across diverse healthcare environments that I just mentioned. Coming to the comparative analysis performance comparisons reveal superior effectiveness across all categories for complex logic detection. The framework achieved 94% accuracy compared to 34% for manual auditing, and 41% for rule-based approaches which is like a 176 percentage improvement over manual methods. Perhaps Bo. Most importantly, guideline update lag decreased from 47 days for rule-based systems to just two days for this framework, which is like a 96% reduction, that prevents an estimated five 40 days of cumulative exposure to outdated protocols per organization annually. So that's about the comparative analysis. And there are some ethical considerations and integration. Responsible implementation requires thoughtful govern governance. The research developed an ethical framework comprising human oversight with clear responsibility delineation transparency in model limitations, equity and pipeline coverage, and robust privacy protections. Privacy impact assessments demonstrated full HIPAA compliance with no protected health information exposure. Integrated strategies focused on enabling within CICD pipelines for automated audits and alignment with existing governance committees achieving positive return of investment most quickly with the CICD approach. So now I want to talk about the future directions. So the future research directions expand capabilities in several areas like realtime monitoring through streaming, streaming architectures, which enables continuous validation rather than periodic assessment. And domain specific fine tuning using healthcare specific data sets shows, promise for further performance improvements. The research also explores shift left approaches that identify potential is issues during design phases rather than like post implementation stage. These advances position the framework for broader application beyond healthcare to other regulated industries as well, requiring rigorous data governance. So we can use the same framework for other industries as well. Not, yeah, that's that's the future direction. So finally, in conclusion this LLM. Auditing framework represents a paradigm shift in healthcare data governance. The four 85% compound efficiency gains exceed typical healthcare id project improvements of 15 to 25% performance, places the framework and top 5% of the healthcare data quality tools and also beyond. Immediate benefits implementation catalyzes broader organizational changes, which increased cross-functional collaboration, improved documentation quality, and cultural shifts toward. Continuous quality improvement and this methodology enables healthcare organizations to ensure data integrity at a scale and consistency impossible with traditional. Approaches directly supporting better patient outcomes and operational excellence. Yeah, that's about it. Thank you so much for your attention and questions about this research and its implications for healthcare data governance are welcome. Yeah, thank you so much.