Living HTA: Automating Health Technology Assessment with R

Abstract

Background: Requiring access to sensitive data can be a significant obstacle for the development of health models in the Health Economics & Outcomes Research (HEOR) setting. We demonstrate how health economic evaluation can be conducted with minimal transfer of data between parties, while automating reporting as new information becomes available. Methods: We developed an automated analysis and reporting pipeline for health economic modelling and made the source code openly available on a GitHub repository. The pipeline consists of three parts: An economic model is constructed by the consultant using pseudo data. On the data-owner side, an application programming interface (API) is hosted on a server. This API hosts all sensitive data, so that data does not have to be provided to the consultant. An automated workflow is created, which calls the API, retrieves results, and generates a report. Results: The application of modern data science tools and practices allows analyses of data without the need for direct access – negating the need to send sensitive data. In addition, the entire workflow can be largely automated: the analysis can be scheduled to run at defined time points (e.g. monthly), or when triggered by an event (e.g. an update to the underlying data or model code); results can be generated automatically and then be exported into a report. Documents no longer need to be revised manually. Conclusions: This example demonstrates that it is possible, within a HEOR setting, to separate the health economic model from the data, and automate the main steps of the analysis pipeline.

Issues, Challenges and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review

“…Continuous updating of cost-effectiveness models with new data is an unexplored opportunity, especially considering the necessity of post-launch monitoring or real-world data [ 12 , 60 ]. Such a framework has been referred to as a living HTA [ 18 , 72 ].…”

“…Continuous updating of cost-effectiveness models with new data is an unexplored opportunity, especially considering the necessity of post-launch monitoring or real-world data [12,60]. Such a framework has been referred to as a living HTA [18,72]. Furthermore, transparency may increase for other stakeholders who are not trained researchers because userfriendly interfaces, for example, Shiny apps in the software R, allow them to "safely" explore model scenarios without having to face backend code [73].…”

“…However, the literature is diverse, with researchers and policymakers looking for ways to alleviate the challenges for economic evaluations of orphan drugs [ 15 , 16 ]. Recent events include the introduction of the Innovative Medicines Fund in the UK that facilitates the collection of additional data for promising orphan drugs or a living HTA, which is the concept of continuous updating of economic models [ 17 , 18 ]. The existing reviews are limited in terms of their ability to synthesise the most recent policy, economic and clinical developments because they have been superseded by recent developments.…”

Improving the usability of open health service delivery simulation models using Python and web apps

“…Initiatives such as OpenSafely have emerged to tackle reproducible pipelines and could support deriving model parameters(Williamson et al, 2020). There is likely much can be learnt from the Health Economic Evaluation community and research in R where models access secure data and parameters remotely using a secure API(Smith et al, 2022). "…”