Economic Evaluation of Different Organizational Models for the Management of Patients with Hepatitis C - Fagiuoli, Stefano; Pasulo, Luisa; Maggiolo, Franco; Spinella, Rosaria; Del Poggio, Paolo; Boldizzoni, Roberto; Di Marco, Mariella; Aronica, Alessandro; Benedetti, Chiara; Correale, Paolo; Garavaglia, Chiara; Nicora, Carlo (Farmeconomia. Health economics and therapeutic pathways, 2019)
BACKGROUND: Access to Directly Acting Antivirals (DAAs) for Hepatitis C Virus (HCV) treatment in Italy was initially restricted to severe patients. In 2017, AIFA expanded access to all patients, to achieve elimination by 2030. AIM: To investigate the impact of different hospitals’ organizational models on elimination timing, treatment capacity and direct costs. METHODS: Most Regional healthcare systems in Italy deploy a Center of Excellence (CoE) organizational model, where patients are referred to a single major hospital in the area, which is the only one that can prescribe and deliver DAAs. The study was conducted at Bergamo’s (Lombardy, Italy) Papa Giovanni XXIII hospital (PG-23), which deploys a Hub&Spoke model: the Hub (PG-23) prescribes and delivers DAAs while Spokes (four smaller hospitals) can only prescribe them. The study compares the two models (CoE vs. H&S). Patient journey and workloads were mapped and quantified through interviews with hospital stakeholders. Cost data were collected through the hospital’s IT system; the sample comprised 2,277 HCV patients, over one year. RESULTS: The study calculated the average cost to treat HCV patients (~ € 1,470 per patient). Key cost drivers are lab tests (60%) and specialist visits (30%). Over one year, H&S can treat 68% more patients than CoE. As deferred patients absorb up to 40% of total costs, the “Optimized” model was designed by streamlining specialists’ visits and involving general practitioners during follow-up. “Optimized” model increases treatment capacity and reduces costs of deferred patients by 72% vs CoE. CONCLUSION: The study demonstrates the importance of organizational models in efficiently achieving 2030 elimination.