Upgrading the SACT dataset and EBMT registry to enable outcomes-based reimbursement in oncology in England: a gap analysis and top-level cost estimate

• Main Authors: Jesper Jørgensen, Panos Kefalas
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-01-01
• Series: Journal of Market Access & Health Policy
• Subjects: cell and gene therapy; advanced therapy medicinal products (atmps); oncology; outcomes-based reimbursement; patient access; systemic anti-cancer therapy (sact) dataset; european society for blood and marrow transplantation (ebmt) registry
• Online Access: http://dx.doi.org/10.1080/20016689.2019.1635842

Background: Outcomes-based reimbursement (OBR) can reduce decision uncertainty and accelerate patient access to cell and gene therapies, however, OBR is rarely applied in practice in England. Oncology is the therapy area with the most cell and gene therapies in late-stage development, and the Systemic Anti-Cancer Therapy (SACT) dataset and The European Society for Blood and Marrow Transplantation (EBMT) registry are two data collection infrastructures that could potentially act as conduits for implementing OBR in cancer in England. Objective: To perform a gap analysis to identify the key requirements for upgrading the SACT and EBMT databases for the purposes of enabling OBR, and a top-level estimation of how much this upgrade may cost, using either a manual (staff-heavy) workaround or part automation (technology-heavy) approach. Methodology: The analysis of current data capture and gaps is informed by secondary research, while the assumptions and data used to derive the top-level cost estimates were informed by consensus-based primary research with experts in healthcare information technology (IT) systems integration and platform development, as well as experts of SACT and EBMT. Findings: In its current form, the SACT dataset in isolation is largely unfit for enabling OBR in oncology, whether through clinical, economic or humanistic outcomes. The EBMT registry has a greater potential; however, this relates to key clinical outcomes only, not economic or humanistic outcomes. Part automation requires a higher upfront investment than the manual workaround (~£1.8 million vs. ~£400k); however, lower annual costs (~£200 vs. ~£260k-£850k) mean that part automation becomes a more cost-effective approach over time. Conclusions: An appropriately automated and scalable data collection infrastructure should be implemented, with the ability to integrate clinical, economic and humanistic outcomes with healthcare cost data and payment systems, to enable OBR not only in cancer but also in other therapy areas.