Private health insurance (PHI) companies have a growing claims dataset that could be used to identify leading indicators for hospitalisation. This study is primarily focused on addiction, mental health, obesity and musculoskeletal disorders disease groups. Leading indicator analysis employed three methods: association rule mining, sequential rule mining, and a heuristic method. Evaluating program effectiveness was performed using propensity score-based matching. PHI professionals were working alongside the research to aid in comprehending data and essential information from a PHI perspective. Analysis has broken down into four major disease groups addiction, obesity, musculoskeletal diseases and mental disorders. Association rule mining uncovers frequent but little known comorbidities of obesity such as male infertility, cellulitis and mesenteric adenitis. Heuristic method uncovered that 0.9% of members undergoing joint replacement developed sepsis, a life-threatening condition.