Health-care reform in industrialised countries is usually motivated by ageing populations, shaky economic conditions and shifting demographics. Health budgets are finite, so decisions must be made about which programs, treatments and medications receive funding, and which don’t.
Priorities of value for money and equitable access to care invariably clash, so it’s important for the general public to have a say in how their health dollars are spent. But a number of barriers are currently preventing this flow of information from occurring in Australia.
Recent research suggests consumers view input into health-care prioritisation as a low priority. But rather than reading these study results as a lack of interest, it probably has more to do with the tools of engagement and the way the survey questions are framed. If they seem irrelevant and bureaucratic, people are less likely to want to be involved.
It’s time to change the way we seek consumer views on health-care prioritisation. The answer? Ask questions that align with real life decision-making.
Discrete choice experiments
Discrete choice experiments (DCEs) are used across many sectors to inform public policy formulation, particularly when public preferences are sought.
A discrete choice experiment asks consumers to prioritise a product, service or policy, based on its key features. It presents subsets of possible products defined by sampling the space of features through an experimental design. In each subset the respondent must make a discrete choice (for instance, I choose home A, from the set of homes A, B, C and D).
The researcher then observes how choices change in response to changes in features, and how strongly the respondent agrees with, or derives value from, those features.
DCEs have often been used to value features of consumer goods, such as mobile phones, housing, and holiday destinations, to name but a few. In the medical decision-making arena, research we are undertaking seeks to understand the value policy-makers place on different types of medical evidence when designing policy.
Using DCEs to understand health priorities
Suppose a new health intervention could generate an additional healthy year of life. Should everyone have the same chance of getting it – a “fair go” for all?
Currently new health interventions must demonstrate that, compared with a given competitor, the additional cost per healthy life year gained is less than some monetary threshold. It doesn’t matter to whom the life years accrue. And adding a single life year in full health is the same as adding two life years lived in a state considered to be only “half of full health”.
But once emotive situations are introduced – such as saving an extremely premature baby or a person unlikely to survive a serious injury – the public tends to abandon support for healthy-life-year model and favour the “rule of rescue”: prioritising saving a specific life over a more efficient use of resources in the wider population.
So, do Australians believe that factors such as age or past behaviour (such as smoking) should be used in prioritising interventions? In short, we don’t yet know – but it’s important we find out.
Lessons from Europe
German researchers recently used a discrete choice experiment to capture and model the decision about “who gets priority when there’s extra health care funding?”. They found the participants prioritised health-care access based on medical criteria such as the patient’s health and quality of life. Life-threatening diseases and acute conditions ranked as the highest priority for extra funding.
Socioeconomic factors were generally rejected as valid criteria: the patient’s age, socioeconomic status, and own health behaviour (lifestyle) had negligible effects on choices.
Another recent European project concentrated on the potential amount of health gained from treatment and the age of potential recipients. Treatment to avoid “instant and premature death” was not up-weighted, suggesting no “rule of rescue”.
But the participants thought resources should be prioritised towards those with less severe impairment, implying that the additional health is more “useful” when it gets some people back up to full health, or near to it.
Nevertheless, the authors noted that restrictions on how you could distribute a hypothetical amount of “potential additional health” to various subgroups of the population limited the model’s statistical power.
Time for research and debate in Australia
Suppose Australians espouse views that have adverse equity implications for some people. This doesn’t mean we must enact policies that conform with these preferences, but this information may be used to better inform public discussion.
If consumers are made aware that the current system penalises the poor, they might wish to prioritise those groups in order that everyone has a fair go in reality as well as in theory.
Online panels and smartphones offer cost-effective ways to elicit preferences and attitudes from large numbers of Australians. It’s time for the Commonwealth government to to use these tools to establish the public’s health funding priorities and ensure these views are heard, debated, and acted upon.