An integrated optimization and analytics approach for planning mental healthcare services

Mental health services worldwide, including in the UK, face significant constraints that necessitate effective resource planning for delivering high-quality care. The application of analytics in healthcare offers the potential to enhance efficiency and improve care quality. However, achieving this vision is particularly challenging in the context of mental healthcare. This paper focuses on the evaluation and redesign of a Primary Care Mental Health (PCMH) service located in Kent, UK. To address this problem, we propose an analytics-driven approach that integrates the three stages of descriptive, predictive, and prescriptive analytics with an optimization model. Through a comprehensive case study, we illustrate how this integrated approach serves as a valuable tool for experimentation within the PCMH service. We explicitly detail how data analysis and stakeholder engagement informed model development. The findings of our novel multi-skill multi-location model demonstrate the benefits of utilising optimised workforce planning to reduce unmet demand while ensuring equitable workload distribution among clinicians. We also discuss the adaptability of the analytics approach and the potential applicability of the optimization model in mental health and other care settings.