Presentation (CTI)

OBJECTIVES: Organizations across industries face a persistent challenge: non-standardized source data templates from multiple clients often become inconsistent and unusable without complex transformation or manual intervention. Although data begins in a fixed template, it evolves through multiple lifecycle stages, with frontline supervisors making ad-hoc changes such as adding new segments or modifying spreadsheets without adhering to guidelines. These uncontrolled variations create multiple versions of master templates, undermining standardization and forcing businesses to introduce corrective steps before ingestion into downstream systems like analytics, claims processing, workforce management, or inventory control. This increases operational complexity and reduces efficiency.
METHODS: The proposed Generative AI based solution with multi-shot prompting is designed to ingest any unstable template of data and convert them into a JSON format which then identifies the multiple headers and corresponding values and convert it into an expected structured format. With minimum configuration the tool would also be able to identify only the target segment of data or set of columns from a non-standardized data set and re-format in standard form, ready to be consumed by downstream processes/ systems.
RESULTS: The testing of Standardization capability was performed across different document types. Fee Schedule, Rates, provider demographic data and another complex excel document containing Fee Schedules and rates data from various providers is received by HealthCare organizations. Before this data can be ingested by downstream systems for contractual loads and Claims processing must be converted into a pre-defined template, this is tested with our solution and gave us 98.5% accuracy.
CONCLUSIONS: This GenAI technique accelerates clinical workflows by creating a precise single source of truth from multiple input sources. In healthcare, standardized provider and fee schedule data enables faster claims adjudication, accurate contract management, and streamlined interoperability across electronic health record systems significantly reducing manual intervention and improving patient care efficiency.