Opportunities and Barriers to the Development and Use of Open Source Health Economic Models: A Survey

Pouwels, Xavier; Sampson, Christopher; Arnold, Renée J.G.; Janodia, Manthan D; Henderson, Raymond; Lamotte, M; Cowell, Warren; Borrill, J.; Huttin, Christine; Udupa, N.; Gong, Chen; Lan, Lee Shee; Brannman, Lance; Incerti, Devin; Ramanath, K.V.; Pribil, C.; Олещук, О. М.; Pokotylo, Olena; Schramm, Wendelin; Nuijten, Mark

doi:10.1016/j.jval.2021.10.001

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…• There is a risk that running the model remotely will result in the perception that the model is a 'black box'. The use of user-interfaces (such as those increasingly being created in shiny) to interrogate the model, as well as the increased transparency associated with being able to share code on sites such as GitHub, should reassure stakeholders that this framework is more transparent than the existing spreadsheet based solutions 19 .…”

Section: Discussionmentioning

confidence: 99%

Living HTA: Automating Health Technology Assessment with R

2022

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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.

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Section: Discussionmentioning

confidence: 99%

Living HTA: Automating Health Technology Assessment with R

2022

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“…Important arguments for this have been its potential for increasing knowledge-sharing, efficiency, consistency and, perhaps most importantly, transparency and credibility of evidence generation in cost-effectiveness research thereby reducing uncertainties. Here, transparency is achieved by providing full access and insight to all methods and assumptions made throughout the model ( 66 , 67 ). In addition to that, TiBipoMod includes additional background information sheets in the Excel model to ensure full disclosure on all sources used and subsequent assumptions made.…”

Section: Discussionmentioning

confidence: 99%

Designing and testing of a health-economic Markov model to assess the cost-effectiveness of treatments for Bipolar disorder: TiBipoMod

et al. 2022

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BackgroundBipolar disorder is an often recurrent mood disorder that is associated with a significant economic and health-related burden. Increasing the availability of health-economic evidence may aid in reducing this burden. The aim of this study is to describe the design of an open-source health-economic Markov model for assessing the cost-effectiveness of interventions in the treatment of Bipolar Disorders type I and II, TiBipoMod.MethodsTiBipoMod is a decision-analytic Markov model that allows for user-defined incorporation of both pharmacological and non-pharmacological interventions for the treatment of BD. TiBipoMod includes the health states remission, depression, (hypo)mania and death. Costs and effects are modeled over a lifetime horizon from a societal and healthcare perspective, and results are presented as the total costs, Quality-Adjusted Life Years (QALY), Life Years (LY), and incremental costs per QALYs and LYs gained.ResultsFunctionalities of TiBipoMod are demonstrated by performing a cost-utility analysis of mindfulness-based cognitive therapy (MBCT) compared to the standard of care. Treatment with MBCT resulted in an increase of 0.18 QALYs per patient, and a dominant incremental cost-effectiveness ratio per QALY gained for MBCT at a probability of being cost-effective of 71% when assuming a €50,000 willingness-to-pay threshold.ConclusionTiBipoMod can easily be adapted and used to determine the cost-effectiveness of interventions in the treatment in Bipolar Disorder type I and II, and is freely available for academic purposes upon request at the authors.

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“…As complex health systems models become Australian & New Zealand Journal of Psychiatry,57(11) increasingly popular, the transparency and validation standards they are expected to meet also grow (Feenstra et al, 2021). There is now increasing support for open-source approaches to model development (Long and Meadows, 2017;Pouwels et al, 2022), although barriers around resourcing, ethics and privacy, intellectual property considerations, knowledge and skills have so far prevented widespread adoption of this practice. More generally, improved access to data that is Findable, Accessible, Interoperable and Reusable (FAIR) (Wilkinson et al, 2016) is required to help improve the quality of mental health systems models.…”

Section: Validity and Legitimacymentioning

confidence: 99%

Mental health systems modelling for evidence-informed service reform in Australia

Whiteford

Bagheri

Diminic

et al. 2023

Aust N Z J Psychiatry

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Australia’s Fifth National Mental Health Plan required governments to report, not only on the progress of changes to mental health service delivery, but to also plan for services that should be provided. Future population demand for treatment and care is challenging to predict and one solution involves modelling the uncertain demands on the system. Modelling can help decision-makers understand likely future changes in mental health service demand and more intelligently choose appropriate responses. It can also support greater scrutiny, accountability and transparency of these processes. Australia has an emerging national capacity for systems modelling in mental health which can enhance the next phase of mental health reform. This paper introduces concepts useful for understanding mental health modelling and identifies where modelling approaches can support health service planners to make evidence-informed decisions regarding planning and investment for the Australian population.

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Opportunities and Barriers to the Development and Use of Open Source Health Economic Models: A Survey

Cited by 15 publications

References 35 publications

Living HTA: Automating Health Technology Assessment with R

Living HTA: Automating Health Technology Assessment with R

Designing and testing of a health-economic Markov model to assess the cost-effectiveness of treatments for Bipolar disorder: TiBipoMod

Mental health systems modelling for evidence-informed service reform in Australia

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