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

Abstract

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.

Barriers and Opportunities for Implementation of Outcome-Based Spread Payments for High-Cost, One-Shot Curative Therapies

“…Spreading payments over multiple years may conflict with the standard 12-months financial cycles of both the payer and manufacturer ( Edlin et al, 2014 ; Faulkner et al, 2016 ; Thompson et al, 2016 ; Danzon, 2018 ; Faulkner et al, 2018 ; AMCP, 2019 ; Jorgensen and Kefalas, 2019 ). Payer’s yearly budgets may not be equipped to implement spread payments over time since the complete cost of the one-shot therapy will have to be budgeted in the year of administration ( Touchot and Flume, 2015 ; Faulkner et al, 2016 ; Schaffer et al, 2018 ; Alliance for Regenerative Medicine, 2019 ).…”

“…However, these data do not provide detailed information on clinical outcomes which might limit their usefulness ( Garrison Jr et al, 2015 ; Seeley and Kesselheim, 2017 ). Another option to increase data collection capabilities in Europe is enabling cross-country collaboration by coordinating multi-country clinical data collection with interoperable registries to reduce the burden of data collection and avoid duplication of collection efforts ( Towse and Garrison Jr, 2010 ; Ferrario and Kanavos, 2013 ; Morel et al, 2013 ; Marsden et al, 2017 ; Jorgensen and Kefalas, 2017 ; Kanavos et al, 2017 ; Bouvy et al, 2018 ; Alliance for Regenerative Medicine, 2019 ; Jorgensen et al, 2019 ; Jorgensen and Kefalas, 2019 ; Maes et al, 2019 ). This collaboration could be organised by aligning the evidence needed for follow-up requirements of the European Medicines Agency (EMA) with identical requests for post-reimbursement evidence by several national payers ( Ferrario and Kanavos, 2013 ; Marsden et al, 2017 ; Gerkens et al, 2017 ; Jorgensen and Kefalas, 2017 ; Geldof et al, 2019 ; Jorgensen et al, 2019 ; Maes et al, 2019 ).…”

“…A first step toward international collaboration has been made by the EMA that will develop the Data Analysis Real World Interrogation Network (DARWIN) to access and analyze healthcare data from across the EU ( Hines et al, 2020 ). Currently, only the Italian national health service possesses a national registry for both regulatory and reimbursement purposes and joint scientific advice will be needed from EMA and the different European health technology assessment bodies to enable collaboration between the regulatory agency and multiple national payers ( Bouvy et al, 2018 ; Jorgensen and Kefalas, 2019 ).…”

Outcomes-based reimbursement for gene therapies in practice: the experience of recently launched CAR-T cell therapies in major European countries

“…This contrasts the schemes operated in Germany, Italy, and Spain, where the rebates or milestone payments are based on individual patient data (collected in the real-world setting), rather than cohort data. In the previous work, we have shown how cohort schemes can be less costly to implement than those based on individual patient data [68], however, in a situation where the therapies underperform, payers in Germany, Italy, and Spain are unlikely to overpay (as payments are tied to individual patient outcomes), while payers in France and the UK will risk overpaying, as reimbursement has been granted for the initial period before the price is reassessed.…”

“…The AIFA and Valtermed registries used in Italy and Spain are examples of data collection infrastructures that are national, and disease/indication-agnostic, however, where the entry of clinical data essentially is a duplication of data already collected elsewhere. Another option is to utilise data already routinely collected in existing standalone structures such as the SACT dataset in England, however, the data collected in this one structure may not provide the clinical outcomes needed for the purposes of informing OBR schemes (as we have shown in the previous work [68]). Another approach is to draw on a more comprehensive range of data by integrating different existing data sources used routinely, an example of which is that of the National Cancer Registration and Analysis Service (NCRAS) operated by PHE in the UK to support cancer epidemiology, public health, service monitoring, and research [69].…”