The Potential of Generative AI for Value-Based Care

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The value-based care (VBC) market is expected to reach $174 billion by 2032, with various challenges hindering its widespread adoption. These challenges include unstructured medical data, data gaps, and data privacy concerns. To address these issues, Natural Language Processing (NLP) and Machine Learning (ML) algorithms can be employed to digitize and process data, while Generative AI can generate synthetic datasets for testing and analysis. Federated Learning (FL) offers a privacy-preserving approach to collaborative machine learning.

Gen AI, or Generative AI, can play a crucial role in the VBC landscape. It can streamline the time-consuming VBC contract-building process by utilizing past contracts and large language models (LLMs) to generate new contracts. Additionally, Gen AI can enhance care management processes by personalizing outgoing communication, automating non-clinical and administrative tasks, and creating more patient-centered Internet of Medical Things (IoMT) devices.

In patient care, Gen AI aids in personalized communication, care plan preparation, and device monitoring. It can automate tasks like medication reminders, appointment scheduling, and daily exercise prompts. The technology also helps engage patients through conversational AI based on past interactions, translation preferences, and educational levels.

Beyond patient care, Gen AI contributes to the IoMT by creating personalized devices with preventive maintenance features. Furthermore, it assists care teams in navigating the complexities of the healthcare system, optimizing workflows, and providing insights for timely interventions.

Various use cases of Gen AI are in progress, ranging from automation of administrative tasks to medical education and clinical decision support. In the context of VBC, Gen AI is already impacting the acceleration of new value-based payment models by improving contract processes and care management workflows.