UM PEGA + AI Integration for Dynamic Care Path Selection in Value-Based Contracts
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P106Keywords:
UM PEGA, AI integration, dynamic care path selection, value-based contracts, machine learning in healthcare, real-time clinical decision support, healthcare IT, clinical rules engine, predictive analytics, automated workflows, healthcare automation, care path optimization, value-based care models, decision-making in healthcareAbstract
Combining UM PEGA with artificial intelligence in the dynamic array of treatment options for value-based contracts addresses the challenges of maximizing care delivery while preserving efficiency and economy. Selecting the best course of action for patients is crucial in value-based care models as it directly affects results and pay-back. Conventional methods can rely on their set clinical procedures, which could not contain actual time patient data or situational change. By using clinical standards and ML algorithms to evaluate patient information in actual time, the integration of UM PEGA with AI enhances this process. While AI algorithms regularly analyze and propose the most suitable treatment paths based on their individual patient profiles, historical data & more predictive analytics, the clinical guidelines included into PEGA procedures help to ease decision-making. This quick more decision-making ensures that treatment plans are tailored to the particular needs of every patient, therefore improving the outcomes of therapy & reducing their unnecessary expenses. The case study shows that this integration greatly improves the general patient experience, reduces delays in service starting, and greatly increases their decision-making accuracy. Combining clinical knowledge with advanced AI improves treatment route choice and fits the goals of value-based contracts by means of a more tailored, more efficient, and affordable healthcare delivery model
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