Acetabular fracture in institutionalized older persons is a clinical problem that is currently difficult to predict. It typically occurs after low-energy falls in patients with advanced hip osteoarthritis and extreme frailty, and its surgical management remains complicated by a lack of precise technological planning. This study analyses data from EDAD questionnaire with 969 institutionalized persons diagnosed by osteoarthritis, diabetes, arthritis, chronic kidney disease, or other associated comorbidities. All of these conditions can lead to dysfunctionalities of the connective tissue or collagen which can restrict mobility and significantly decrease the functionality in older adults. The mean age is 88 years, and 76% are women. The results show that the functional limitations most strongly correlated with very poor perceived health are: being unable to travel as a passenger, even with assistance, walking outside the facility, dressing independently, maintaining prolonged posture, or avoiding dangerous situations. These seemingly “subjective” variables turn out to be potential clinical predictors of the actual risk of severe hip osteoarthritis and acetabular fracture. Based on this finding, a hybrid framework is proposed for future analyses. This framework combines the automatic selection of functional variables in older adults using the Quantum Approximate Optimization Algorithm, risk classification with variational quantum classifiers and classical machine learning, and, finally, patient-specific 3D biomechanical modeling of the acetabulum from actual CT scans. The ultimate goal is to provide orthopedic professionals with personalized risk assessments and classifications that allow them to predictively decide whether to intensify physiotherapy, change assistive devices, or plan preventive surgery.