Gestión de Negocio

Chapter 4 includes an extensive discussion on the strengths and limitations of the methodology and policy implications of the marginal value of hospital spending in the Netherlands. In this section we discussed the applicability of supply-side thresholds in new technology assessment, reflecting upon mechanisms to improve efficiency and reduce risk of displacement.

The use of a threshold based on supply-side estimates of opportunity costs assumes that these opportunity costs are fixed. However, opportunity costs in the hospital sector may vary with the stringency of the budget, defined as the relation between the height of the

budget and the demand for hospital care. In turn, demand depends on socio-demographic factors (demand for regular care) and technological advances (the number of new technologies entering the market). More specifically, opportunity costs increase as the budget is reduced, as the need for regular care increases (e.g. ageing) and as the number of new technologies entering the market increases. These factors predict that the opportunity costs of a given new technology are rather flexible, while the assumption of fixed opportunity costs may not hold.

To illustrate, consider a standardised model of new technology (NT) adoption presented in figure 8.1. In a given year, a number of new technologies enter the market, ordered from high marginal value (cost-effectiveness) to low marginal value. Adding technologies to the benefit package under a fixed budget requires funding from within the healthcare sector. Liberating funds may displace valuable existing care. The chance of displacement will be small when the budget impact of all combined technologies is low, as funds may be liberated by productivity gains and waste reduction. For the first, very cost- effective new technologies entering the benefit package, value lost due to displacement is low, while net value gain is high (at the left side of figure 8.1). However, as more and more technologies are added to the benefit package the total budget impact will increase and displacement of valuable care becomes more likely, with opportunity costs increasing. Beyond point NT*, these opportunity costs become higher than the gains of the last new technology added to the benefit package. Any additional approved new technology will reduce total health, displayed as the coloured triangle ABC in figure 8.1. Total value gained in a year is equal to the integral of the new technology curve minus the displacement curve, displayed as the trapezoid in dark. For excessive NT close to NT*, the total net value gain to the health system is likely to be positive. In optimum, new technology will be adopted until the marginal value of the new technology equals the marginal value of displacement, which is at NT*. Suboptimal market outcomes could be a reason for government intervention. Total health may be increased by setting a threshold for new technology adoption equal to point A. This automatically ensures that new technologies to the right of NT* will be rejected.

In this model, a reduction in the hospital budget would shift the displacement curve to the left, since the budget reduction adds to the reduction in existing care. Hence, displacement costs will be higher when the hospital budget is reduced (Paulden, 2016). An increase in patient demand would make reductions in existing care more valuable, while also shifting the displacement curve to the left. More new technologies entering the market would shift the new technology curve to the right, increasing the opportunity costs in optimum. The chance that a specific new technology finds itself to the right of NT* therefore

depends on the amount of other new technologies that enter the benefit package that year as well as the stringency of that year’s budget. Because new technologies are generally only assessed once and in isolation, a supply-side opportunity cost approach to a new technology assessment may not result in socially optimal decisions. Instead, a demand-side threshold might be preferred as being more stable over time.

Figure 8.1: Implementation of new technologies and value lost due to displacement

In a semi-fixed budgeted system such as the Netherlands, thresholds may increase over time due to diminishing marginal returns, making it increasingly costly to improve population health (Barro, 1996; Murphy and Topel, 2003). This is for example demonstrated by Cutler et al, showing that in ten years the marginal cost per life year gained for a 65-year old increased by $20,000, from $121,000 in 1985 to $141,00014 _{in 1995 (Cutler and McClellan,} 2001). Recent estimates for cardiovascular cost-effectiveness in the Netherlands in 2010 also show sharp increases in thresholds over time (van Baal et al., 2018). Based on the author’s calculations, a mean elasticity of spending on mortality of 0.19 renders marginal cost to save a life at age 70-74 between €800,000 and €1,000,000 in 2010, which is comparable to other estimates (Felder, 2006; Hall and Jones, 2004). However, using 1994 values given in the paper, significantly lower costs to save a life can be obtained of between

14 _{This is 203,724.08 US Dollars of 2014, or 153,176 Euros of 2014 per life year saved. Using a QALY per year}

ratio of ~0.6 this would be €255,293 per QALY. In comparison, we find for this age category a marginal value of € 65,000 per QALY.

€260.000 and €280.000. Furthermore, extrapolating the elasticity from Van Baal et al. (2018) using Dutch cost of illness data of 201515_{, a marginal value to save a life of age 70-74} between €1.7 and €4.6 million is obtained, a relative increase of 113%-450%. This demonstrates the high plasticity of a supply-side threshold.

Opportunity costs do provide valuable information for policy makers with respect to the stringency of the budget. An example would be the Dutch society being willing to pay €50,000 for a QALY. A marginal value of care of €74,000 per QALY would then imply that the budget is too high and that cost containment could improve total welfare. While a new technology of €60,000 per QALY does not lower total welfare at the moment, a first-best solution would be to reject the new technology and reduce health spending. Similarly, if society’s willingness to pay would be €100,000 per QALY, a first-best solution would require both an expansion of the budget and an adoption of all technologies up to €100,000 per QALY. Summarising, we advise policy makers to use a demand-side threshold for new technology assessments and opportunity cost estimates to set appropriate hospital spending limits. Despite opportunity costs being more suited for budget setting than for the use of a cost-effective threshold, they may still provide an argument for rejecting reimbursement of very cost-ineffective treatments, especially until robust demand-side thresholds become available.

Reducing opportunity costs

The use of cost-effectiveness as a criterion for new technology assessment implies that the hospital sector is inefficient in displacing care with the lowest value in response to budget reductions (Eckermann and Pekarsky, 2014). If it were efficient, cost-ineffective new treatments would not be adopted in practice in the first place. This also implies that displacement effects could be reduced by improving efficiency of decision making at a micro level.

Consider figure 8.2, introduced in chapter 1. New technologies require additional funds at the micro level, as the new technology will be adopted by the health professional. A practitioner can obtain funds by reducing existing care (rationing) which, depending on the value of the care reduced, can be considered a displacement. Health professionals may demand extra funds from provider boards and CEOs in response to new technologies, transferring cost pressures to a higher level. This is what these boards often experience in practice. Provider boards in turn may demand extra funds from purchasers, adding to budget pressure on a further higher level, or use prioritisation to distribute budget

15 _{In 2015, spending on CVA was €1916 (€1365), while mortality rates were 0.59% (0.16%) for males (females)}

pressures amongst health professionals. In turn, health professionals confronted with budget pressures may revert to rationing of care to patients. As a result, the effects of new technologies will spread throughout the health system.

So far, little is known on how these decisions are taken in practice on the level of providers and health professionals. In practice, explicit prioritisation seems to be sparsely used. Since 2015, 14 cases have been identified for Australia, Canada, the UK, New Zealand and Sweden (Polisena et al., 2013; Rooshenas et al., 2015). A potential barrier is that even low- value care may provide value for a subset of patients (Garner and Littlejohns, 2011). No relation was found between a new technology uptake and prioritisation or rationing in either the UK or the Netherlands (Adang et al., 2018; Appleby et al., 2009; Schaffer et al., 2015;

Schaffer et al., 2016). Absence of sufficient information was mentioned as a limiting factor in taking prioritisation and rationing decisions (Marks et al., 2013). In order to reduce value lost due to budget restrictions and uptake of new treatments, more research will be necessary on how to improve decision making on the micro level. Measuring cost effectiveness of existing treatments and empowering providers to prioritise based on cost effectiveness may improve the efficiency of prioritisation and rationing mechanisms. When faced with budget restrictions and new technologies, efficient rationing and prioritisation may allow providers and health professionals to reduce negative effects on health. Recently, initiatives have been aiming to identify low-value care (Wammes et al., 2016). Chapter 4 provides additional guidance through calculation of disease-specific and age-specific thresholds, showing substantial divergence. Disease categories with relatively low marginal values, such as diseases of the blood, pregnancy and neonatal care may require further studying to identify cost-ineffective treatments.