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Telehealth has become an essential part of modern healthcare, offering patients timely access to medical care while alleviating the strain on healthcare facilities. Despite its growing adoption, optimizing telehealth services for both efficiency and quality remains a significant challenge due to varying patient needs, resource constraints, and diverse clinical requirements.
Our project aims to develop a comprehensive data-driven model using SAS Viya to optimize telehealth operations. By analyzing diverse datasets, including patient interaction logs, clinical outcomes, appointment scheduling, and telehealth services, our model will identify key patterns, inefficiencies, and bottlenecks within the telehealth system.
The model will generate actionable insights to optimize appointment scheduling algorithms, ensure equitable workload distribution among providers, and tailor interactions based on patient preferences and historical data. These insights will help healthcare facilities streamline operations, enhance care quality, and improve overall patient satisfaction and engagement with telehealth services.
By implementing these optimizations, healthcare providers can not only reduce operational costs but also achieve better health outcomes, thereby delivering a more efficient, patient-centered telehealth experience.
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