Optimal decision rules for HTA under uncertainty: a wider, dynamic perspective

Förster, Martin; Pertile, Paolo

doi:10.1002/hec.2893

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…The option of deferring treatment seems rather rare, however, and may be given in case of preventive activities. Recently, a model was published, which helps in deciding whether to defer treatment and conduct research first (Forster and Pertile, 2013).…”

Section: Discussionmentioning

confidence: 99%

A model to optimize investments in health technologies, quality of care and research

Gandjour

2015

Applied Economics

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Funds for health technologies compete with funds for implementing health technologies as well as funds for conducting research to reduce uncertainty around treatment and implementation cost-effectiveness. No study has yet shown how to allocate a combined budget for health technologies, implementation and research. The purpose of this work was to present an allocation model with the goal to maximize health. Based on a constrained optimization formulation, we show that considering opportunities to invest in implementation and research may justify considerable disinvestment in health technologies. This may reduce the willingness to pay for new health technologies significantly.

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Section: Discussionmentioning

confidence: 99%

A model to optimize investments in health technologies, quality of care and research

Gandjour

2015

Applied Economics

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“…The real options approach has been widely employed in the healthcare sector with a focus on optimizing outcomes. It finds application in diverse areas, investment analysis (Smyth and Swinand, 2002; Williams et al ., 2007; Levaggi et al ., 2009; Levaggi and Michele, 2004; Pertile, 2008, 2009; Pertile et al ., 2009), technology assessment (Eckermann and Willan, 2008; van Loon et al ., 2012; Thijssen and Bregantini, 2017; Oliveira and Zambujal-Oliveira, 2018; Tolga, 2020; Chalkidou et al ., 2008), decision-making in public health (Favato et al ., 2012; Pertile et al ., 2014; Park, 2016; Attema et al ., 2010; Megiddo et al ., 2019), and medical decision-making (Driffield and Smith, 2007; Grutters et al ., 2011; Meyer and Rees, 2012; Forster and Pertile, 2013b; de Mello-Sampayo, 2014, 2015; Shechter et al ., 2008; Zivin and Neideill, 2009). These earlier studies applying real options theory to decision-making in the healthcare sector often use the option to defer (Fornaro et al ., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…However, this may not hold if the patient's health state has a tendency to improve. Forster and Pertile (2013a) use real options theory to decide whether to adopt a new health technology instead of an existing one in patient treatment. de Mello-Sampayo (2015) explores the timing of switching to second-line antiretroviral therapy (ART) under health uncertainty and in the absence of viral load monitoring.…”

Section: Strategic Flexibility In Healthcarementioning

confidence: 99%

Strategic flexibility in healthcare: an exploration of real options

de Mello-Sampayo

2024

JES

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PurposeThis survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.Design/methodology/approachThis research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.FindingsThree different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.Practical implicationsThis research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.Social implicationsDecisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.Originality/valueThe originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

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“…Another example is that comparative effectiveness studies often report the probability that the sample treatment effect could have occurred by chance (the p value), but this quantity might have limited applicability within the context of a particular quality improvement project. 94 When developing complex interventions, 95 the decision is not between offering the intervention or not, but many other options including spreading the intervention, modifying the eligibility criteria, improving fidelity to the original intervention design or refining the theory of change. 96 97 Recent developments in analytical methods aim to assess the relative costs and benefits of alternative decisions, and relate these to the mechanism of action of the underlying interventions for the target population.…”

Section: Diffusing Data Analyticsmentioning

confidence: 99%

Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data

Deeny

Steventon

2015

BMJ Qual Saf

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Socrates described a group of people chained up inside a cave, who mistook shadows of objects on a wall for reality. This allegory comes to mind when considering ‘routinely collected data’—the massive data sets, generated as part of the routine operation of the modern healthcare service. There is keen interest in routine data and the seemingly comprehensive view of healthcare they offer, and we outline a number of examples in which they were used successfully, including the Birmingham OwnHealth study, in which routine data were used with matched control groups to assess the effect of telephone health coaching on hospital utilisation.Routine data differ from data collected primarily for the purposes of research, and this means that analysts cannot assume that they provide the full or accurate clinical picture, let alone a full description of the health of the population. We show that major methodological challenges in using routine data arise from the difficulty of understanding the gap between patient and their ‘data shadow’. Strategies to overcome this challenge include more extensive data linkage, developing analytical methods and collecting more data on a routine basis, including from the patient while away from the clinic. In addition, creating a learning health system will require greater alignment between the analysis and the decisions that will be taken; between analysts and people interested in quality improvement; and between the analysis undertaken and public attitudes regarding appropriate use of data.

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Optimal decision rules for HTA under uncertainty: a wider, dynamic perspective

Cited by 9 publications

References 19 publications

A model to optimize investments in health technologies, quality of care and research

A model to optimize investments in health technologies, quality of care and research

Strategic flexibility in healthcare: an exploration of real options

Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data

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