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Bushaj, Sabah
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Fall 2024
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Abstract
In healthcare, extensive data is collected daily to track patient appointments, whether for routine checkups or evaluations of specific symptoms. However, missed appointments are a common issue with significant implications for both patients and healthcare providers. For patients, missed appointments can delay the diagnosis and treatment of health conditions, potentially worsening outcomes. For clinics and hospitals, no-shows lead to inefficient use of resources and lost opportunities to deliver care to other patients who may need it urgently. This study aims to leverage predictive analytics to identify patterns in missed appointments. Specifically, it will explore whether certain health conditions, such as hypertension and diabetes, or demographic factors, including age, gender, and neighborhood, are associated with a higher likelihood of missing appointments. By analyzing these correlations, the study seeks to provide insights that can inform strategies to reduce no-show rates, enhance patient care, and optimize resource allocation within healthcare settings.