The United States devotes a larger share of its GDP to health care and spends more on health care per capita than any other country. The sheer size of the total spending on health care, at approximately $3.5 trillion in 2017, puts significant pressure on all payers and crowds out other forms of public and private spending.
In this brief commentary the authors suggest that, as part of the effort to deal with this pressure, the United States should look at borrowing a cost containment strategy from other countries: the use of hard caps on spending growth. The authors draw on our their experience of working with decision-makers over the last 20 years on the topic of priority setting to put forward some ideas on whether there is potential for application of trade-offs in the United States.
As hard caps force choices to be made, a necessary condition for successful implementation of this policy is the presence of an effective priority-setting framework to ensure that the right choices are made in operationalizing spending limitations. Work on this topic elsewhere can provide some insight into the use of a criteria-based framework for priority setting that purports transparency in decision-making to achieve value-based decisions.
Other countries still have much work to do, but there is a substantial track record of using formal priority-setting approaches that could potentially inform practice in the United States. We suggest that there are key segments of the US healthcare system where the adoption of formal priority-setting frameworks to guide trade-off decisions is feasible. Piloting such activity in these contexts is the next natural step in this line of inquiry.
Mitton, C. and Dionne, F. (2020), "Priority setting and resource allocation in the US health system: is there a place for hard caps?", Journal of Health Organization and Management, Vol. 34 No. 4, pp. 379-384. https://doi.org/10.1108/JHOM-01-2020-0016Download as .RIS
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Copyright © 2020, Craig Mitton and Francois Dionne
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The United States devotes a larger share of its gross domestic product (GDP) to health care and spends more on health care per capita than any other country (Sawyer and Cox, 2018). In 2000, the United States spent 12.5 percent of its GDP on health care while the average for comparable OECD countries (those with GDP above the median of all OECD countries) was 8.2 percent. In 2016, it was 17.9 percent for the United States and 10.7 percent for OECD countries. The gap increased from 4.3 percent to 7.2 percent in 16 years (Sawyer and Cox, 2018). In per capita terms, the United States spent $10,348 in 2016 while the average for comparable OECD countries was $4,908 or less than half (Sawyer and Cox, 2018). Over the entire US population, this represents approximately $1.75 trillion more annually spent on health care than would be spent if the United States spent the same amount per capita as the average of the comparable OECD countries. In other words, the implications of the extra spending on health care in the United States are financially very significant and the sheer size of the total spending, at approximately $3.5 trillion in 2017, puts significant pressure on all payers and crowds out other forms of public and private spending such as other public services or wages. In developing a response to this ongoing pressure, it is useful to look at what is happening in other comparable countries.
Clearly ever-escalating healthcare costs are not a problem unique to the United States. There have been many comparisons of the methods to control or contain healthcare costs across countries (The Commonwealth Fund, 2019; Park et al., 2007; Stabile et al., 2013). However, a review of such comparisons yields one obvious difference between the United States and comparable countries: in every other comparable OECD country, government imposes limitations on the rate of growth of at least some parts of the spending on health care. This is known as a form of “supply-side” controls. In Canada, for example, the federal funding for health care is capped at a growth rate of either 3 percent or a three-year moving average of nominal GDP growth, whichever is greater (Government of Canada, 2020). In the United Kingdom, the total budget of the National Health Service (NHS) is set annually, in advance, by the central government (The King's Fund, 2017). This type of supply-side control is largely missing from the US healthcare spending management and in our view explains in part the run-away costs in the United States.
While hard spending caps are effective in controlling spending growth, their implementation in the face of an ever-growing demand for services leads invariably to difficult choices having to be made. Consequently, a policy around limiting healthcare budget growth needs to be accompanied by an effective framework to make the required tough choices, that is, sound priority-setting practice to ensure that the right choices are made in operationalizing the spending limits. Therefore, to inform the debate on the merits of hard spending caps in the US healthcare system, we provide a perspective on how to do priority setting drawing primarily on our own experience over the last two decades of doing work within this field (in contrast to “just” thinking or writing about it). We conclude that indeed there are segments within the US healthcare system where the tools discussed herein would be highly relevant.
How is prioritization done effectively?
A priority-setting and resource allocation (PSRA) framework has two basic components: a mechanism to assess the value of interventions and a mechanism to guide the prioritization activity (i.e. making trade-offs between interventions). Our own research in this area covers key aspects of priority setting in health care from value assessment to decision-making including ethical and social considerations. The focus of our work has been across all areas of clinical care and public health and spans regional and local providers of care both within the acute sector and beyond. The prioritization framework we have used in this work is known as program budgeting and marginal analysis (PBMA). PBMA has been used in healthcare systems across the world for over 40 years (Mitton and Donaldson, 2004). This framework is designed to take an organization from the identification of a priority-setting challenge, that is, how to keep total spending within the prescribed cap, to a recommended solution (Mitton et al., 2014). While it is true that there has been mixed uptake of PBMA over the years across countries, there has been quite reasonable success in Canada in particular (Mitton et al., 2014). Here we provide a few details on the two aspects of priority setting before putting forward a perspective for moving forward with these tools in the United States.
While there are several approaches to value assessment, in our experience we have found no single approach that will apply in most situations. As recently stated in one review, “At this time, it is clear that there is no perfect model or framework for value assessment, or even one that will garner consensus across all stakeholders” (Sorenson et al., 2017). While the most commonly cited approach for value assessment in the literature is the single criterion cost-effectiveness analysis (CEA), we have found that multiple-criteria organization-specific approaches are gaining in popularity, perhaps reflecting the reality that decision-makers typically deal with a broad set of objectives (Cromwell et al., 2015). As for the data to which those approaches are applied, the accepted forms of evidence are expanding from the traditional Grade A level of evidence (i.e. RCTs). In fact, many of the approaches reported in the literature explicitly consider patient input as part of the process for determining value. This certainly fits with broader trends toward patient-oriented research and the inclusion of patient input in healthcare decision-making (Wilson et al., 2014; Mitton et al., 2011). In our own experience in Canada, there is actually a pronounced movement away from reliance on CEA and the resulting incremental cost-effectiveness ratio (ICER), toward methods that are inclusive of broader aspects of value and produce organization-specific value estimates.
The second aspect of priority setting is the process of making choices, or deciding on trade-offs, based on the value estimates for each option being considered. Just as there are many ways to assess value, there are many ways to use value estimates to make choices. Based on our experience, we have developed lessons for what works and what does not work in terms of organizational structure and in terms of the process for prioritization. Tables I and II outline these lessons, which can serve two purposes. First, they can be used to determine if successful prioritization is possible in a given organization. And second, they can be used as a guide to the establishment of conditions or of a process to prioritize effectively.
These lessons apply to organizations where prioritization is required and feasible, that is, organizations where: 1. budget constraints exist, meaning that either the organization has an annual budget that is predetermined and that the organization must stay within or that growth in the organization's budget leads to crowding out of other forms of spending by the funder (e.g. salaries for employees of a corporation or spending on public services for governments), which makes prioritization a requirement; 2. there is an operational level where the organization has at least some discretionary authority over what services it provides (or covers) and to who, which makes prioritization feasible. Having briefly reviewed the two basic aspects of priority setting – that is, value assessment and choice making – and further provided some thoughts on what can work and what may not work both organizationally and in terms of process deployment, we now turn to the key question of whether there is potential in the United States for hard caps and subsequent choice making using an explicit priority setting.
Will this work in the US?
We fully acknowledge that spending limits are not of themselves the solution that will curb the growth in healthcare spending in the United States, but they can be an important tool. There are many settings within the US system where spending limitations could be implemented, settings where the party holding the financial risk has the authority to prioritize services and make the tough choices resulting from spending limitations. For example, employer group plans and self-insured employers, which together cover approximately 56 percent of Americans (Berchick and Barnett, 2018), and Medicaid managed care, covering another 17 percent of Americans, are good examples. These contexts are essentially quasi-fixed budgets where choices can be made about what to fund and what not to fund. Making explicit choices provides the opportunity to reallocate resources from one area of care to another, preferably using value assessment tools and a formal decision-making process where these choices are considered.
Our intent is not to be prescriptive about MCDA or even PBMA but rather to draw attention to these tools through their use elsewhere. While there is nothing inherently wrong with devoting a larger share of GDP to one sector like the United States does relative to other countries, this undeniably leads to some crowding out of other spending. Since other OECD countries seem to have similar or better health outcomes as the United States with significantly less spending (Sawyer and McDermott, 2019; Peter G Peterson Foundation, 2019), it seems relevant to consider their means of controlling healthcare costs. One such policy instrument is hard caps and through this making the trade-offs explicit through application of a choice-making framework. The aim of this commentary is to pull together current thinking in terms of value assessment, choice making, and lessons learned for application of a formal approach to priority setting in contexts where hard caps and trade-offs are to be considered. The next step should be the piloting of prioritization in such settings to assess the actual impact of the resulting trade-offs in terms of health outcomes.
|What works (or what is needed)||What does not work|
|Leadership that strongly supports priority setting and a culture conducive to making trade-off decisions, with support from clinical champions (Peacock et al., 2006; Ruta et al., 2005)||Preset budgets with no flexibility and no support. In other words, hard caps alone and strict adherence to a cost-effectiveness threshold are not the solution (Donaldson et al., 2002)|
|Strong, credible health technology assessment (HTA) to provide key information to the organizations that manage care and further, to facilitate consistency in the measurement of the clinical impact across organizations||Overreach of central HTA agencies as they move from evidence assessment to wanting a greater role in choice making (Sawyer and McDermott, 2019). HTA is best seen as an input into a broader priority setting framework|
|Project management to support priority-setting activity coupled with regular communication with internal and external stakeholders (Smith et al., 2016) to gain credible commitment, i.e., organizational trust (Jan, 2003)||Copayments as a form of incentive to ensure proper use of covered services and legislating cost control measures such as ways to increase efficiencies by specifying certain practices or incentives to compensate for market shortcomings|
Process and methods
|What works (or what is needed)||What does not work|
|Comprehensive framework applying economic and ethical principles to ensure process is as fair as possible while simultaneously considering value within available resources (Mitton et al., 2014)||Decision-makers not having budget authority over full spectrum of services, thus making systemwide reallocation or trade-offs challenging (Mitton and Donaldson, 2004)|
|Explicit criteria and formal proposal scoring tool, including clearly defined and weighted criteria to guide relative value trade-off decisions (Wilson et al., 2007). By measuring the expected impact of any possible trade-off with the criteria, an organization measures the extent to which a given trade-off moves it toward or away from its mission and objectives||Budgets that are simply a roll-over of the previous year, adjusted for cost increases and population changes, with no real assessment of relative value and ad hoc threshold ICER approaches|
|Transparency in the process of decision-making coupled with stakeholder engagement; both of these pieces are key drivers for process fairness, which in turn results in credible results that are acceptable to key stakeholders (Jan, 2003)||A lack of transparency in regard to the actual elements that guide decisions and who influences the process and further a lack of support for process-based recommendations by those bearing the financial risk, leading to a lack of buy-in by politicians or higher-level decision-makers (Waldau, 2015)|
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This commentary was based on a project that was funded by the National Pharmaceutical Council (NPC). The authors would like to thank Michael Ciarametaro and Robert Dubois for their comments and support.