Incorporating Quantitative Patient Preference Data into Healthcare Decision Making Processes: Is HTA Falling Behind?
A recently published editorial by OHE’s David Mott considers whether quantitative patient preference data has a role to play in HTA decision making. A recently published editorial by OHE’s David Mott considers whether quantitative patient preference data has a role…
A recently published editorial by OHE’s David Mott considers whether quantitative patient preference data has a role to play in HTA decision making.
A recently published editorial by OHE’s David Mott considers whether quantitative patient preference data has a role to play in health technology assessment (HTA) decision making.
Evidence on patient preferences can be incorporated into HTA processes either on an ad hoc basis (for example, by asking individual patients to appear before an HTA committee to describe their symptoms and their impacts) or by systematically collecting evidence from patients. Quantitative methods are available for this purpose: patient preference data can be generated using a range of stated preference methodologies including, but not limited to: time trade-off; contingent valuation; discrete choice experiments; and best-worst scaling. The data can be used within, or considered alongside, analyses that aim to inform healthcare decision making, such as benefit-risk assessments and health technology assessments (HTAs).
In the United States, the Food and Drug Administration (FDA) has taken major steps forward to encourage the incorporation of the patient perspective into regulatory decision making. With related research projects currently ongoing, regulatory agencies in Europe may follow the FDA’s lead in the near future. However, despite the potential and the progress being made in the regulatory space, there is currently little to suggest that HTA agencies will encourage greater utilisation of quantitative patient preference data.
In an editorial recently published in The Patient, OHE’s David Mott considers why this might be the case and puts forward some ideas as to whether HTA can ‘catch up’ with the regulatory space. David argues that quantitative patient preference data are not as regularly utilised in HTA, relative to the regulatory space, because:
Cost-utility analysis (CUA) is dominant in HTA and therefore the opportunities for patient preferences to be considered are limited to the quality of life component of the quality-adjusted life year (QALY).
There are normative arguments suggesting that patient preferences are not necessarily appropriate for consideration in HTA decisions. For example, in a publicly-funded system, the preferences of the general public may be more appropriate.
Preference studies are often condition-specific, whereas the methodologies and outcome measures typically used in HTAs are kept relatively generic and consistent to improve comparability across HTA decisions.
It is common for patient preferences to be considered in HTA in other ways, such as having patient representatives on decision making committees providing qualitative evidence.
In order for patient preferences to play a bigger role in HTA decision making, and to therefore potentially ‘catch up’ with the progress that has been made in the regulatory space, David argues that:
A change of stance from influential agencies would be required with respect to their willingness to consider quantitative patient preference data; or
A new approach to HTA could be considered as an alternative to CUA, such as multi-criteria decision analysis, which may provide greater opportunities to consider the patient perspective in a quantitative manner.
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