Predictive and Personalized Healthcare Scheduling by Integrating No Show Prediction with Artificial Intelligence

The no-show in clinics shows the inefficient use of the precious resources. It takes appointments away from needy patients as it deprives them of timely appointments. Here is a model that predicts and mitigates patient no shows in the hospital settings which is a longtime issue that influences resource utilization and patient access to care. Based on a large supply of data sources from patient demographics, appointments details, real time environmental data, social media insights, and patient engagement platform interactions, the model was built. Using the machine learning algorithms like XGBoost and deep learning models along with the clustering algorithms like K-Means and DBSCAN, the model can well predict the no-show risks and cluster patient groups with similar behavioral patterns. This helps to implement targeted interventions that include, for example, personalized reminders of an appointment or the ability to change a schedule and social support. There is continuous feedback loop and performance monitoring mechanisms for model updating and improved performance. Patient centric features including the use of personalised portals and AI powered chatbots help increase the engagement and communications. Overall, this model provides a way to prevent patient no-shows, optimize hospital scheduling and operations, and increase patient satisfaction.