Automated Patient Quality Data Flow for CMS Reporting Accuracy
DOI:
https://doi.org/10.15680/IJCTECE.2025.0804020Keywords:
Automated patient data flow, CMS reporting, eCQM calculation, AI integration, Cerner PowerChart, real-time data validation, healthcare interoperabilityAbstract
The research focuses on the application of an AI-based eCQM Calculation Engine and real-time data integration to improve the precision and efficiency of CMS reporting. By utilizing the PowerChart of Cerner, the system is able to extract, validate, and calculate clinical quality measures (eCQMs) of Electronic Health Records (EHR) without manually entering data into the system and minimizes human error. Real-time feedback and AI-provided anomaly detection make the data very accurate and reported in time, which is in line with the CMS requirements. The paper indicates how the automation of patient quality data flow provided by the PowerChart system in Cerner enhances interoperability, enhances the workflows in reporting as well as compliance with regulatory considerations. This is further complemented by the scalability of the system, which is applicable in various healthcare environments, both small practices and big hotel systems. Finally, the resulting effect of this strategy is that it does not only increase the accuracy of CMS reporting but also provides improved healthcare decision-making and improved patient care outcomes
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