Predicting resource utilization in a comprehensive center: an evaluation of three alternative methods

Area of Science:

• Health Services Research
• Mental Health Services
• Health Economics

Background:

• Accurate prediction of resource utilization is crucial for effective management of comprehensive mental health centers.
• Current methods may lack the precision needed for optimal resource allocation and planning.
• Understanding patient flow and treatment completion is key to resource management.

Purpose of the Study:

• To propose and evaluate alternative methods for predicting resource utilization in mental health centers.
• To identify the most effective approaches for refining resource utilization predictions.
• To compare different sample selection and statistical techniques.

Main Methods:

• Comparison of three distinct methods based on sample selection and statistical analysis.
• Utilizing an admission cohort versus a discharge cohort for patient sampling.
• Employing chi-square and multiple regression statistical techniques.

Main Results:

• Refined predictions were achieved by using an admission cohort and excluding patients who left against medical advice.
• No significant differences in prediction accuracy were found between chi-square and multiple regression methods.
• Multiple regression analysis demonstrated superior flexibility in prediction.

Conclusions:

• Alternative methods, particularly those focusing on admission cohorts and specific exclusions, enhance prediction accuracy.
• While statistical techniques showed similar accuracy, multiple regression offers greater adaptability for resource utilization forecasting.
• These findings support improved planning and resource allocation in mental health services.