An analytic dashboard for financial performance and prediction in healthcare disciplines

Mohan Nathan, Priyadharshni (2024) An analytic dashboard for financial performance and prediction in healthcare disciplines. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

In the rapidly evolving healthcare industry, effective financial management is crucial to adopt innovative strategies to ensure financial viability. However, many healthcare financial administrators still rely on the conventional approaches which fails in providing the detailed insights necessary for financial planning dan decision-making. This project addresses the gap of the conventional method by developing and implementing analytics dashboards enhanced with machine learning model. The objectives were to evaluate and identify limitations of traditional financial management methods, develop machine learning model for revenue prediction and forecasting and design an interactive analytics dashboard to provide actional insights. The machine learning model are used for two purposes which are prediction using Random Forest Regression, XGBoost Gradient Boosting and Decision Trees and ARIMA model was used for forecasting next year’s revenue. For prediction, Random Forest Regression demonstrated the best performance with Training Mean Squared Error (MSE) of 0.0016 and a Testing MSE of 0.0020, with Training and Testing R^2 scores of 0.6384 and 0.5419, respectively. The interactive dashboard designs provide dynamic visualization and real-time analytics facilitates the financial performance monitoring and make informed decisions. This project underscores the importance of integrating advanced predictive analytics and interactive data visualization tools in the financial management practices of healthcare facilities.

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