Optimization Model For Healthcare Processes Using Process Mining

This paper presents a model for optimizing healthcare processes using process mining to reduce waiting times. Healthcare services often suffer from high levels of dissatisfaction due to long waiting times for appointments and medical consultations. This leads to patients seeking urgent care elsewhere, resulting in a lack of confidence in healthcare services in Peru. The proposed model collects data from medical information systems and analyzes them using the Celonis tool to identify bottlenecks and violations in the processes. The model focuses on two critical processes, appointment scheduling and consultation, which take an average of 135 minutes and cause patient dissatisfaction. The model consists of four main phases: 1. Definition of objectives and data processing; 2. Inspection of pattern records; 3. Process mining phase; and 4. Results phase. To validate the proposal, a test scenario was defined in a Peruvian public healthcare organization (ESSALUD) in Satipo, Peru. Where it reduced appointment scheduling by 98% and medical consultation process time by 64%. Additionally, the optimization results increased by 45% and 46% for appointment scheduling and medical consultation, respectively. Overall, this model has the potential to improve healthcare services and reduce patient dissatisfaction caused by long waiting times.