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Cylus J, Papanicolas I, Smith PC, editors. Health system efficiency: How to make measurement matter for policy and management [Internet]. Copenhagen (Denmark): European Observatory on Health Systems and Policies; 2016. (Health Policy Series, No. 46.)
Anita Charlesworth , Zeynep Or , and Emma Spencelayh .
All health care systems are looking for ways to improve efficiency as they come under increasing pressure to control the growth in health care expenditure. To support sound decision-making, the use of efficiency metrics in assessing and evaluating system reform and policy interventions is critical. However, the use of robust evidence and, more specifically, the use of efficiency metrics in policy formation, vary across countries. In all countries policy decisions are based on a mixture of things, including societal values, fiscal priorities, public opinion and political ideology. Moreover, compared with other sectors, measuring efficiency in the health sector is complicated because of market characteristics specific to health. A proper efficiency evaluation in the health sector requires an analysis of health outcomes as well as service outputs, but this is not always straightforward.
This chapter looks at the role that efficiency metrics can play in shaping and evaluating policy choices in middle- and high-income countries using a conceptual policy development framework against which a number of country examples are appraised. Country examples compare the role of efficiency metrics across the stages of the policy cycle, following the ROAMEF (rationale, objectives, appraisal, monitoring, evaluation and feedback) model, which is a stylized framework for rationale policy development (Figure 8.1).
The ROAMEF cycle. Source: HM Treasury (2011).
In practice, policy development diverges from this cycle, which is highly stylized and excludes key factors such as political context, values and events (Hallsworth, Parker & Rutter, 2011). The model is used here as a theoretical framework rather than a description of policymaking in practice. Following the six stages of the ROAMEF cycle, we examine the formation and implementation of a number of policies which feature commonly in strategies designed to improve health system performance in middle- and high-income countries.
The OECD (2010) has distinguished three broad sequences of reform over the last few decades, separating macroeconomic policies aimed to restrain expenditure (prices and volume); microeconomic policies tackling demand (gatekeeping, care coordination, disease prevention); and supply (improving provider payment, purchasing systems, and so on). With regard to improving system performance, Roberts et al. (2004) identified five main policy levers: finance, payment, organization, regulation and provider behaviour.
In this chapter we examine the role of efficiency metrics in relation to five policy domains that are common issues in most health care systems and cover a broad range of the policy levers identified by the OECD and Roberts et al. (2004):
the definition of the publicly funded health basket (regulation); cost sharing arrangements (finance); hospital reorganization (organization); provider payment reforms (payment); and public reporting of health care data (provider behaviour).In each domain, we first outline recent or common policies introduced with the goal of improving technical and allocative efficiency (TE and AE), recognizing the rationale and objectives for each. We then examine the available evidence on the efficiency of these policies before their implementation and the process of evaluating the impact afterwards in selected countries. We have not conducted a systematic review of the evidence on efficiency but highlight key findings from recent literature and evidence scans. We focus on the efficiency metrics that are available in different countries, how/if these metrics are used at different stages of the policy development cycle, whether there are any common features of policies where efficiency metrics have a more or less prominent role in policymaking, and what implications this may have for the future development of efficiency measurement.
As resources are limited, it is important to ensure that the money being spent on health care is providing adequate quality and value. The definitions of the health services and products reimbursed from the public purse, and their prices, are crucial for assuring AE. Comparative effectiveness analysis is a way to better assess the value of health care treatment options (see Chapter 6). The assessment of costs and benefits of different health services and products can help identify the most cost-effective courses of treatment and thus increase the quality and value of health care with a given budget. However, reimbursement decisions take place in a social and political context and the consideration of efficiency is not always obvious. For example, in considering whether to introduce population health screening programmes (Box 8.1), policymakers have to weigh up the potential benefits against the potential harms, and consider the opportunity costs involved in introducing a resource-intensive intervention. There are a range of tools such as HTA which can help policymakers to determine the relative effectiveness and cost–effectiveness of different interventions.
This section focuses specifically on the role of the HTA and the different international approaches to dealing with cost–effectiveness and effectiveness in defining and regulating the health care included within the publicly funded benefit basket.
The WHO defines the HTA as the systematic evaluation of the impacts of health technology (including drugs, medicines, vaccines, procedures and systems). Its main purpose is to inform technology-related policymaking in health thereby improving the uptake of cost-effective technologies and prevent the uptake of those which are of less certain value (WHO, 2011).
The use of the HTA is common place in developed countries. In Europe, a HTA Network has been established to enhance cooperation between countries. 16 However, it can be argued that there is a constant tension between economic and scientific considerations given the need to control costs (Pugatch & Ficai, 2007).
Population screening programmes.
The HTA plays a crucial role in providing evidence to inform policy decisions on the reimbursement of health technology. At a basic level, the HTA seeks to understand whether a new technology is effective. In some countries the analysis will go further, to consider whether the technology is cost-effective when compared to other interventions for a similar condition. In other countries, such as the United Kingdom, the analysis is further extended to consider whether a given technology is cost-effective when compared to interventions for any condition, through comparing QALYs and analysing the cost–effectiveness of different interventions by determining the cost per QALY. This section does not focus on the methodological limitations of QALY and other cost-utility measures, but instead focuses on the higher-level policy issues regarding the relative importance of HTA in policy decision-making (see Figure 8.2 for a range of considerations for HTA).
Range of considerations for HTA. Note: HTA = health technology assessment.
On the surface, the questions outlined in Figure 8.2 should lend themselves easily to using efficiency metrics as part of the decision-making process. The questions relate to a single issue, that is, the effectiveness or cost–effectiveness of an intervention. However, different approaches to the HTA have been adopted internationally.
One of the greatest areas of contention is the extent to which QALYs are used to determine the availability of different technologies and the extent to which it is fair to limit access based on a cost-per-QALY threshold. Table 8.1 shows some of the different approaches to determining reimbursement.
Use of cost–effectiveness thresholds in selected developed countries.
Ireland: use of efficiency metrics in the HTA for ipilimumab.
Decisions on whether to fund new, innovative (but expensive) drugs can attract significant media attention and public pressure. For example, in Ireland in 2011, there was widespread controversy over the decision not to recommend ipilimumab for reimbursement. See the case in Box 8.2.
NICE is one of the best-known organizations assessing the cost per QALY gained when determining whether a new technology should be adopted by the NHS in England. NICE currently uses the QALY measurement to compare how much someone’s life could be extended and/or improved before considering the cost per QALY gained. Generally, if a treatment costs more than £20 000–30 000 per QALY, it would not be considered cost-effective (NICE, 2010). However, NICE has not been immune to criticisms of rationing, and the concept of applying a value threshold in determining the availability of technologies is not universally accepted. On coming to power in 2010, the Conservative and Liberal Democrat coalition government proposed the creation of a Cancer Drugs Fund 17 to provide access to some cancer drugs for patients at the end of life. To date, £1 billion has been spent on drugs through the fund and four drugs – bevacizumab, abiraterone, bendamustine and cetuximab – have accounted for around half of the spending. Most of the drugs have been examined by NICE and rejected on the grounds that they do not reach NICE’s cost–effectiveness threshold level because of a combination of low effectiveness and high cost (HM Government, 2010).
Unlike the United Kingdom system, some countries have taken an active stance against the QALY threshold. For example, in the USA, the Patient Protection and Affordable Care Act (2010) prohibits the Patient-Centered Outcomes Research Institute from developing or employing dollars-per-QALY as a threshold to establish what type of health care is cost-effective or recommended, nor can a QALY threshold be used to determine coverage, reimbursement or incentive programmes under the Medicare Programme. It could also be argued that, aside from ideological issues, a cost–effectiveness threshold would be less likely to work in the USA given the diverse spread of payers and the complexity of health care delivery (Sullivan et al., 2009).
In Germany, the Institute for Quality and Efficiency in Health Care (IQWiG) examines the benefits and disbenefits of interventions. 18 IQWiG’s functions are advisory in nature and its recommendations are not binding. Constitutionally, statutory health insurance (SHI) beneficiaries may not be deprived of access to beneficial health technologies on cost alone and, before 2007, IQWiG’s remit was limited to the assessment of clinical benefit (IQWiG, 2009). The Act to promote competition among the SHI funds came into force on 1 April 2007 and gave IQWiG additional powers to assess the benefits and costs of drugs. However, the Federal Joint Committee – Gemeinsamer Bundesausschuss (G-BA) 19 – requested that the assessment of benefits and costs be carried out by comparing competing health technologies in a given therapeutic area. This meant that decisions about the relative importance of conditions and funding decisions would not lie with the Institute (IQWiG, 2009).
In practice, new drugs and medical interventions were covered by default and were only assessed by IQWiG if the G-BA requested an evaluation, which resulted in Germany having more new drugs available compared to other European countries but paying a high premium as a result (Nasser & Sawicki, 2009). From 1 January 2011, the G-BA and IQWiG were required to conduct benefit assessments of newly authorized drugs. Within three months of market authorization, the G-BA assesses the benefit of the new drug against appropriate comparators (in practice, this is delegated to IQWiG). After another three months, the G-BA makes a decision on the benefits case and pricing structure for the new medicine. Within six months, if additional benefit is proved, the reimbursement price is negotiated. If a medicine is not found to have additional benefit, it is allocated to a reference price group. Reference prices determine the maximum amount statutory health insurers will pay (G-BA, 2011).
While it is acknowledged that there are multiple approaches to HTA, there appears to be relatively little evidence relating to the effectiveness of HTA as a process (Wilsdon & Serota, 2011) and HTA reports do not typically define what they are hoping to achieve over and above the assessment of a given technology (Garrido, 2008). In March 2010, a new European project – the European Consortium in Healthcare Outcomes and Cost-benefit Research – was established to compare the health system organizations of the 27 Member States and to study the robustness of health outcomes used by HTA authorities in Europe. The final report suggested that HTAs expressed as the number of QALYs or cost per QALY were inconsistent and that European HTA agencies should use other methods. Instead of using the QALY, the report recommended that cost–effectiveness analyses should be expressed as a cost per relevant clinical outcome (Beresniak et al., 2013). However, the findings were not accepted by NICE, with its Chief Executive suggesting that there needed to be a measure which could be applied across all diseases and conditions to ensure that the costs to the NHS were justified by improvements in quality of length of life (NICE, 2013).
All health care systems include some element of user charges and copayment for some services as part of the system of financing health care. Across the OECD in 2011, the proportion of health care expenditure funded by out of pocket (OOP) payments was around 20%. Among middle- and high-income countries, the reliance on OOP payments is lower than in low-income economies but it still varies considerably, with countries such as France, New Zealand and the United Kingdom below 10% yet Australia, Portugal, Spain and Switzerland at, or above, 20% (OECD, 2013). The Commonwealth Fund survey examined the performance of the health care system in 11 high-income countries. As Table 8.2 shows, in many high-income countries, significant minorities of patients still report cost issues as a barrier to accessing or completing medical care (Davis et al., 2014).
Cost-related access problems in 11 health care systems (percentage of surveyed patients/physicians reporting problems).
The rationale for user charges in both insurance and tax-based health care systems is fundamentally the same: first, there is the financial objective of reducing insurance premiums and tax. The second rationale is to improve AE – tackling the incentive to overconsume. User charges are designed to reduce the problem of moral hazard 20 and the potential overconsumption of health care. Reducing consumption can improve efficiency if it reduces patients’ use of clinically ineffective services but, if it reduces the use of cost-effective services, particularly prevention, it may be inefficient (Schokkaert & Van de Voorde, 2011). Some countries have been reforming their systems of copayment with the goal of improving AE. A prominent example is the series of innovations introduced in the USA under the banner of value-based insurance design (VBID) (Robinson, 2010).
The empirical evidence suggests that user charges reduce the consumption of health care services in middle- and high-income countries, with primary care services having higher elasticity (more sensitive to price). Policymakers often rely on user charges to slow the growth in health spending (Mladovsky et al., 2012). However, the potential for significant cost savings or enhanced efficiency from extending copayments and user charges is generally considered to be limited. User charges increase the financial burden on households (Wagstaff et al., 1992) and studies show that, in general, they do not differentiate effectively between cost-effective and low-value care. Moreover, they are particularly likely to reduce use among lower-income individuals, higher-need older people and chronically ill patients, even when the level of user charges is low (Gemmill, Thomson & Mossialos, 2008; Newhouse, 1993).
Increasing user charges in primary or ambulatory care may worsen health outcomes. Although frequently motivated by a desire to increase revenue or reduce cost, in some cases increasing user charges may have the opposite effect through increased spending in more expensive acute, emergency care. As a result, restricting user charges to low-value services and ensuring there are exemptions or caps for poorer households or regular users of care is more likely to enhance efficiency. However, it may not always be possible to identify low-value care and the transaction costs involved may be significant (Bach, 2008; Braithwaite & Rosen, 2007; Goldman, Joyce & Zheng, 2007; Thomson, Foubister & Mossialos, 2009; Trivedi, Rakowski & Ayanian, 2008).
Since the economic crisis in 2008, many countries faced with large fiscal deficits have increased user charges to reduce pressures on public funds, and private and social insurers have increased copayments to reduce the pressure on premiums. Across Europe, countries have increased or introduced user charges for a range of health services including ambulatory care, emergency department visits, pharmaceuticals and for specific services such as in vitro fertilization (IVF), physiotherapy, some mental health services and some vaccines (Mladovsky et al., 2012).
The 2008 economic crisis has seen countries prioritizing revenue raising as a response to burgeoning fiscal deficits. A recent review of international responses to austerity looked at the experience of five European countries following the recession of 2008 (Ellin et al., 2014). It found that in each country there had been a combination of changes to user charges, copayments and deductibles, and, in some countries, restrictions to the health basket. Table 8.3 shows the principal changes introduced in each country.
Changes to user charges, copayments, deductibles and the health basket in five European countries following the 2008 recession.
Beyond Europe, other countries have plans to increase copayments, principally for fiscal objectives. Faced with health care costs rising to 5.3% in 2012–2013, the Australian government proposed the introduction of a new copayment for visits to a GP, out-of-hospital pathology tests and imaging, from July 2015. The Federal Budget set out proposals to extend copayments from April 2015 under the Medicare program, with an A$7 fee for GP copayments which would be applicable to the poorest in society. However, there are plans to introduce a safety net with a cap introduced following the first 10 visits for key groups including pensioners, those on a low income and children under 16 (Johnson, 2014). Much of the political debate following the announcement has focused on the potential system impact of increased use of alternative services and the concern that there are other policies to support the financial sustainability of the health system which may be more effective (Consumers Health Forum of Australia, 2014). This highlights the need for policy to focus not just on the net efficiency impact of a specific policy but on its comparative efficiency and the potential opportunity cost of pursuing one option over another.
In Germany, the €10 fee per quarter for visits to a GP was abolished in 2012, 8 years after it was introduced, following a number of studies which suggested that that it was not effective in reducing demand for health care (Schreyögg & Grabka, 2010). Although the failure to achieve these objectives was cited as a reason for abolishing the copayment, there was no formal efficiency metric put in place when the payment was introduced to support a robust evaluation of the reform’s impact.
There is evidence to suggest that decisions on copayments and user charges are also influenced by concerns about AE. Switzerland is reforming its coinsurance rates to provide incentives for people to switch from traditional insurance plans to models of insurance with a managed care approach, which are considered to promote more efficient use of health resources. Under these reforms, coinsurance rates for those who opt for traditional insurance plans will rise from 10 to 15%, encouraging people to opt for managed care plans which will retain the lower coinsurance rate.
Perhaps the biggest development in the attempt to use copayment reform to support AE is the VBID concept, which was introduced in the USA over a decade ago. It describes a series of reforms to a number of health insurance plans in the USA that reduce copayments with the aim of encouraging patients to comply with recommended medication or treatment, to improve the proactive management of patients’ conditions and minimize the costliest medical interventions. VBID focuses on conditions and treatments with well-established clinical evidence, particularly chronic conditions such as hypertension, asthma and diabetes. Over the past decade, the VBID concept has expanded to include incentives for other types of evidence-based services.
Following encouraging reports from early adopters of VBID, and the endorsement of the concept by a number of influential bodies in the USA, the 2010 Affordable Care Act adopted the VBID concept in its requirement that health insurance plans cover preventive services rated A or B by the 13 US Preventive Services Task Forces without a copayment option for the patient. The required preventive services include blood pressure screening, colorectal cancer screening and screening for sexually transmitted infections. The Act also allows the Secretary of the US Department of Health and Human Services to establish guidelines to permit a health insurance plan to use VBID (NCSL, 2016). Although the imperative to improve the AE of health spending was a key motivation behind the expansion of VBID in US health plans, there is no evidence of the development or use of systematic, consistent metrics of AE either by health plans or government bodies.
Across health care systems over the last 20 years there has been a reduction in the number of curative hospital beds and a consolidation of hospitals, with an increasing number of hospitals merging into fewer, larger providers (Dash, Meredith & White, 2012; OECD, 2014). Over the last two decades, the average number of curative beds per 1000 population fell from 4.7 to 3.3.
The rationale for merger is often linked to concerns about the efficiency of the hospital sector. The hypothesized efficiency benefits are concentrated on different types of economies of scale and scope, either in terms of cost or quality. Table 8.4 outlines the common efficiency-related benefits claimed for hospital mergers.
Efficiency-based objectives frequently cited in support of hospital mergers.
However, mergers may also be motivated by the desire to increase the market power of the hospital. The benefit to the hospital of increased market power is traditionally defined in terms of higher profits arising from higher prices for health services. In many countries though, health care prices are fixed so that competition between hospitals is based on quality. In these health systems, mergers which increase market concentration pose a potential risk to quality incentives within the system (Gaynor, Laudecella & Propper, 2012).
In some cases, mergers arise from financial or clinical failure – a hospital is in deficit or struggling to deliver an acceptable quality of care and merger with another hospital is the failure regime in a sector where exit is very difficult. In 2009, faced with financial problems, the GasthuisZusters Antwerpen Hospital Group was formed from three hospitals (with 1100 beds) and four elderly care centres (with 300 residents) in the greater Antwerp region of Belgium. In Germany, an increasing number of public hospitals have been sold to private hospital chains, a trend driven mainly by budget deficits in regional and municipal governments (Schulten, 2006).
Systematic reviews of the relationship between outcomes and volumes suggest that for some services at least (for example, complex surgery), there is a relationship between the frequency with which the surgeon performs a procedure and quality (Halm, Lee & Chassin, 2002). There is a strong clinical consensus that higher volumes lead to better patient outcomes but, in some cases, there is limited evidence to support this consensus and there remains little evidence on specific volume thresholds (Glanville et al., 2010).
Although the objective of mergers is frequently to improve the efficiency of care (either in terms of quality or cost) the evidence of the impact of mergers on cost and quality is mixed, dated and limited. In terms of cost, a systematic review conducted in 1997 concluded that there was some evidence for economies of scale up to around 200 beds but also evidence of diseconomies of scale above 600 beds (Posnett, 1999). In some countries there are relatively few hospitals with fewer than 200 beds (Hong Kong, New Zealand and the United Kingdom). Other countries still have a significant number of small hospitals and merger activity does seem to be focused on consolidation among small providers (American Hospital Association, 2013).
Research on the impact of hospital volume on outcome shows that the volume/outcome relationship depends on the procedure/condition studied and often disappears above a small threshold. Gaynor (2004, 2006) provides a good summary of that literature. Overall, over time, the disparity in outcomes between low- and high-volume hospitals has narrowed, and outcomes have improved significantly for all hospitals. Given these improvements, lower minimum volume standards may be advisable in less populated areas (Ho, 2000).
Much of the empirical research is focused on the US health system. Studies of mergers in the USA suggest that hospital mergers generally increase prices and have no effect on quality (Vogt & Town, 2006; Weil, 2010). There is limited evidence on the impact of mergers in other countries and, critically, in systems with regulated prices. One exception is a recent study that looked at the effects of hospital mergers in the NHS in England. Using a number of measures including financial performance, productivity, waiting times and clinical quality, researchers found little evidence that mergers achieved the anticipated gains (Gaynor, Laudecella & Propper, 2012).
Hospital mergers can either be initiated by the organizations themselves or, in many cases, result from system-level planning exercises. A number of countries (including Germany, the Netherlands, the United Kingdom and the USA) approach hospital consolidation from the perspective of their general merger control regimes. In the Netherlands, the Competition Authority has responsibility for deciding merger cases but receives advice from the specific health regulator, the Health Authority, to identify whether there is an efficiency/public interest case for the merger based on the effects on patients in terms of affordability, quality and accessibility (Canoy & Sauter, 2009). For countries which take a market regulation approach to mergers, there is a significant evidence challenge in defining the relevant market for health services (both the geography and the relevant range of services) and then in assessing the potential loss of efficiency (in terms of cost or quality) from a merger proposal.
In some cases, governments have initiated major structural and institutional changes through national or local planning initiatives. The Ontario province of Canada established a programme of hospital reconfiguration motivated, in part, by financial and efficiency concerns. In 1994–1995, the government of Ontario had an operating deficit of CAD10.2 billion on revenues of CAD46 billion, or 22% of its budget. It set up a statutory body – the Health Services Restructuring Commission – with a legislative mandate to make binding decisions on the restructuring of hospitals across the province. It led to the amalgamation of 44 hospitals into 14 new organizations, the takeover of four hospitals by other hospital corporations, and the directed closure of 27 public hospitals (Rochon, 2010).
Denmark has undertaken an ambitious programme of hospital reconfiguration. In 2007, as part of a wider programme of structural reform of the Danish government, the Danish Health and Medicines Authority (DHMA) saw its role expanded from being a health sector regulator to a body with responsibilities for planning specialist functions across Denmark’s hospitals. The DHMA issued guidance on standards for specialization to the five Danish regions and required them to submit plans to meet these standards, and to bid for capital resources from a 10-year DKK40 billion national investment fund for hospitals (OECD, 2013). This programme of reform is expected to see the number of acute hospitals in Denmark fall from 40 in 2006 to between 20 and 25 in 2015 (Olejaz et al., 2012).
Decisions about hospital mergers and reconfigurations are often motivated by the desire to implement standards or guidelines developed by medical associations. A recent study in the United Kingdom reviewed a large number of guidance documents produced by Royal Colleges and other medical associations on the configuration of A&E and supporting services (Goudie & Goddard, 2011). They found that while there was a broad consensus about the need for a set of core services to be co-located with emergency services, there was less information on the minimum scale of provision. The authors comment that:
The evidence to support the guidance does not appear to draw upon economic evaluation. There is a high degree of circularity of argument as many documents cite other similar documents rather than primary sources. Expert opinion is a prevalent theme within the types of guidance cited and very often it is deemed to be ‘self-evident’ that a particular organisation of services is required. Whilst this evidence may well be valid, it is not usually based on economic analysis.
(Goudie & Goddard, 2011)
The Danish reforms to stimulate consolidation of specialist services referred to earlier also relied heavily on expert opinion. Clinical expert groups were used to determine which services should be considered specialist, and the appropriate volumes and co-location of services (OECD, 2013). The use of expert opinion is, in part, a response to limitations in the evidence base on economies of scale and scope in health care but it is also used to build support for change. Hospitals are arguably the most visible organizations within health care systems; they account for a significant proportion of health spending, their clinicians provide much of the professional leadership, and they have a significant impact on the overall provision of health care. As a result, decisions about hospital services are highly politically sensitive in most countries, regardless of the mix between public or private ownership and funding (McKee & Healy, 2002).
In many countries, clinical leadership and support for change is often seen as crucial to implementing otherwise highly contentious reforms. Many countries, including France and Germany, have introduced volume (compulsory) thresholds in the past decade, mainly with the goal of improving quality of care. In Germany, legislation in 2002 allowed the contracting parties in the German health system to determine minimum volume standards for planned care. In 2003 the compulsory health insurance funds proposed a list of 10 minimum volumes and, in 2004, these were introduced for five procedures by the G-BA (which includes the National Association of Doctors and Dentists, the German Hospital Federation and the health insurance funds). Annual minimum volume standards were implemented for five surgical procedures: kidney, liver and stem cell transplantation, and complex oesophageal and pancreatic interventions. In 2006, a minimum volume standard of 50 procedures per year was introduced for total knee replacement operations (de Cruppé et al., 2007).
To mitigate the potential negative effects of mergers, regulatory authorities have tried to impose behavioural remedies on hospitals seeking to merge; however, this is often difficult to enforce. Determining whether merged hospitals have, in practice, exercised market power is difficult in health systems where hospitals compete for patients on the basis of quality rather than price. In hospital systems with competition based on quality rather than price, post-merger attempts to enforce behavioural remedies may therefore not be an effective means of responding to a loss of efficiency. More fundamentally, unwinding a hospital merger is likely to be deeply problematic. Seeking to undo a merger may be an even less effective remedy as it introduces new risks of additional costs and quality failures.
Provider payment can be a powerful tool to promote efficient health care provision. Ideally, payment systems should encourage good quality of care while at the same time promoting the efficient use of resources at the health system level. In practice, different payment mechanisms (block, capitation, cost per case or fee for item of service) provide different incentives for providers, some of which may be conflicting with the goal of greater efficiency. Within each type of payment method there are variants that may create a different set of incentives, and several payment methods may be used in combination to mitigate unintended consequences that may be generated by each method individually. It is important to be aware of typical reactions triggered by each payment scheme and evaluate/measure the impact in terms of efficiency, quality and equity of access. This section looks at two popular payment mechanisms widely used in industrialized countries – DRG-based payment and P4P – to examine what evidence is used to justify or establish their efficiency and what metrics are used to monitor their impact on efficiency following implementation.
Activity-based payment, where hospital funding is linked to activity defined by DRGs, is widely considered a potential solution for improving efficiency in the hospital sector (O’Reilly et al., 2012). As a payment mechanism, it provides incentives to increase the number of patients that hospitals treat (compared to global budgets), and reduce inputs per case and/or improve the efficiency of the input mix. Like any other forms of payment, it can also generate perverse effects that have been largely described in the literature (Cots et al., 2011; Ellis & McGuire, 1996). Patient selection, specialization towards standardized care procedures, multiplication of high-intensity (better remunerated) procedures and upcoding are among the examples most often reported. Furthermore, the efficiency sought at the individual provider level may not always be compatible with the system-wide objective of assuring best allocation of resources in the health system, across different services, to achieve the best possible outcomes (AE). Hospitals can overprovide certain treatments/tests, modify the composition of services or abandon (when they can) certain activities considered unprofitable. This can also create problems in access to some services.
Empirical evidence on the impact of DRG payment on efficiency is surprisingly scarce. The majority of the earlier academic studies focused on technical efficiency (TE) (or productivity) looking at the relationship between hospital outputs and inputs, using empirical techniques called frontier modelling to identify the best output–input relationship to establish how much the efficiency levels of given hospitals deviate from the frontier values (Kautter, 2011). DEA is the most common metric used since it allows for flexible specification of hospital production (see Chapter 5). All these studies measure efficiency from the hospital perspective, and use either the number of discharges within each DRG or an aggregated discharge measure, adjusting for the hospital case mix, for defining outputs (Street, O’Reilly & Ward, 2011).
Despite the obvious trade-off between care quality and efficiency, quality is rarely and only partially taken into account in the analyses. In a few studies where outcomes (beyond outputs) are considered, they are always measured by inpatient mortality rates. Hospital inputs are often measured by partial indicators such as labour/physician FTEs or, less commonly, by running costs and medical expenses. Results from these earlier studies are rather mixed. The link between TE gains and DRG payment has not been demonstrated in many countries, including the Austria and the USA. However, in others, such as Norway, Portugal and Sweden, DRG payment was associated with greater TE in hospitals, although studies from Sweden showed that initial efficiency improvements were subsequently negated when activity ceilings were imposed on hospitals (Street, O’Reilly & Ward, 2011). Impact on cost efficiency, studied to a much lesser extent, appears to be mostly insignificant, except in Sweden (Gerdtham et al., 1999; Street, O’Reilly & Ward, 2011).
There is limited evidence on the impact of DRG payment on overall cost efficiency at the hospital sector level; however, DRGs are associated with higher hospital expenditure, including higher administration costs and hospital volumes (O’Reilly et al., 2012). Moreno-Serra & Wagstaff (2010) showed that the introduction of DRG payment (over global budgets) was associated with higher spending in central and eastern Europe and in Asia, with higher hospital volume but also lower amenable mortality, in particular for cerebrovascular diseases. It is difficult to attribute changes in outcomes to payment reform because of simultaneous changes in the health care contexts. In England, Farrar et al. (2009) showed that there was little measurable change in quality of care in terms of in-hospital mortality, 30-day post-surgical mortality and emergency readmissions after treatment for hip fracture, while average length of stay and unit costs decreased significantly in areas where DRG payment was introduced.
Despite the weaknesses of the evidence base on efficiency and numerous studies pointing the perverse effects of DRG payment (Cots et al., 2011), countries that introduced DRG payment relatively recently often lack thorough monitoring and evaluation of its impact.
In most countries, payers and purchasers use partial efficiency metrics with average length of stay and hospital volume (number of cases) the most common indicators. For example, in France, the official monitoring and evaluation of the efficiency of the payment reform consisted of measuring the number of hospital cases in major categories and the average length of stay (for all stays). The rise in the number of hospital stays (not-weighted), given the overall hospital sector budget, is taken as a sign of higher efficiency; however, recent research suggested that DRG creep (substantial upcoding of activity) and induced demand may be a real problem for sector-wide efficiency (Or et al., 2013). Patient outcomes and quality metrics, such as 30-day readmission rates and complication/mortality after surgery, are not monitored regularly in France.
In all countries, average length of stay is used as a key indicator of efficiency. The significant reduction in average length of stay after the introduction of the DRG payment in many countries is seen as a sign of greater efficiency. However, providers can also discharge patients to be readmitted again or transfer patients prematurely to other institutions or home. Early analysis of the readmission rates in the USA suggested some evidence that hospitals modified their coding practices under DRG payment to readmit patients into higher-priced diagnoses (Cutler, 2006). This, in turn, impacted Medicare’s payment policy, which adjusted payments to discourage inappropriate discharges. Several European countries have been inspired by Medicare in monitoring readmission rates and not paying for readmissions within 30 days of discharge. But, the definition of avoidable readmission, how readmissions are counted and the non-payment policy vary widely across countries.
Activity-based payments encourage hospitals to optimize the use of their resources based on the theoretical model of so-called yardstick competition. 21 Economics literature has largely shown that yardstick competition is efficient only if prices are set correctly. This requires that managers and regulators know each hospital’s cost function, that is, they can compare cost components of different hospitals (per case mix-adjusted stay) and determine benchmarks. However, in many European countries, such as France, Germany and Italy, unit cost data are not available for all hospitals and/or not used for benchmarking, partly because of the difficulties in measuring and standardizing hospital costs across providers (see Chapter 4). In the USA, the Medicare Payment Advisory Commission established efficiency rankings based on outcome measures (such as mortality, readmission, and so on) and inpatient costs, adjusting for factors beyond a hospital’s control, which do not reflect efficiency (MedPAC, 2009). In contrast, in France, the policy of price convergence between public and private hospitals without using the same costing methodology, nor adjusting for factors beyond hospitals’ control, has created distrust and distorted the discussion on efficiency differences between providers.
In most countries, DRG prices are set using average cost data from a sample of hospitals. Some argue that, for improving system-wide efficiency, a better option is to adjust prices for encouraging medical practice considered high quality and efficient, moving away from pricing based on observed costs per case. Evaluations of episodes of care incorporating pre- and post-hospital services (radiology examinations, physical therapy) can also be beneficial for establishing efficiency margins for given conditions/patient groups (see Chapter 3). In England, best practice tariffs have recently been introduced for selected areas (including cholecystectomy, hip fractures, cataracts and stroke) where significant unexplained variation in clinical practice is observed and clear evidence of what constitutes best practice is available (DoH, 2014). For example, best practice tariffs are set to incentivize day case activity for cholecystectomy while, for cataracts, the price covers the entire pathway, so that commissioners encourage best practice pathways where patients are treated in a joined-up and efficient manner.
Traditionally, payment systems are based on the quantity and intensity of services provided. While this may be appropriate in most situations, it is problematic where low-intensity care can provide better outcomes than high-intensity care and when service content can vary widely between providers. P4P rewards providers for achieving specified valued outcomes. Most approaches adjust payments to physicians and hospitals on the basis of a number of different quality measures but some schemes also consider efficiency of service provision. Ultimately, P4P programmes aim to increase the provision of quality care by containing or reducing health care costs over the long-term.
Payments may be made at the individual, group or institutional level. Performance may be measured using benchmarks or relative comparisons (Kautter, 2011). The results of any P4P scheme, in terms of efficiency, depend on the performance measures used (the definition of quality and outcomes) and the reimbursement rules for providers, as well as the governance arrangements ensuring the system is functioning as intended without creating perverse effects.
There is some evidence that P4P programmes can improve quality and facilitate cost savings, although unintended or negative effects have also been reported. The variety of programmes and lack of proper evaluation makes it difficult to establish firm conclusions on the efficiency benefits from P4P programmes.
Several reviews concluded that the evidence is mixed with regard to P4P effectiveness, often finding a lack of impact on provider behaviour or inconsistent effects, albeit with a few exceptions (Houle et al., 2012; Van Herck et al., 2010). For example, Curtin et al. (2006) suggested a 2.5-fold return on investment for each US dollar spent on a P4P programme for diabetes in a US health maintenance organization. An evaluation from China’s Ningxia Province (a predominantly rural area in the north-west of the country) suggested that capitation with P4P can improve drug prescribing practices by reducing overprescribing and inappropriate prescribing (Yip et al., 2014). The authors carried out a matched-pair, cluster-randomized experiment between 2009 and 2012 to evaluate the effects of P4P on antibiotic prescribing practices, health spending, outpatient visit volume and patient satisfaction. They found that the intervention led to a reduction of approximately 15% in antibiotic prescriptions and a small reduction in total spending per visit without any effect on other outcomes.
On P4P schemes for hospitals in the USA, a report on the Premier Project by Kahn et al. (2006) found that the cost (bonus expenses) of the programme was higher than financial penalties recovered from the hospitals. In England, the evaluation of the P4P for hospitals, which is broadly similar to the US scheme, showed that the P4P did not significantly reduce mortality in targeted conditions and that there is a statistically significant increase in mortality for non-incentivised conditions (Kreif et al. 2015).
P4P has been increasingly seen as the solution to problems in health service delivery, not only in high-income settings but also in low- and middle-income countries. Most P4P schemes target GPs with the objective of improving health promotion and prevention rates, as well as the organization of medical practice. The evaluation of most of the schemes, however, has been limited. Often there is no control group and it is just presumed that one can compare before and after to capture the effects of P4P.
For example, New Zealand started its Performance Based Management (PBM) programme in 2006 within its Primary Health Organizations (PHOs), which are non-profit organizations that provide primary health care services (Buteow, 2008). In 2007 over 98% of New Zealanders enrolled in the PBM programme. The P4P scheme was one component of the health sector overall quality framework, and aligned with other initiatives to improve health outcomes and reduce inequalities. Financial incentives, relatively small, aimed to provide some additional resources to enhance primary care. While all performance indicators (mostly process indicators such as vaccination and screening rates) showed modest progress (Cashin, 2011), there has been no rigorous evaluation of the impact of the PBM and it is hard to demonstrate the link between the payments and the progress made (value for money).
In France, in an attempt to improve the quality and efficiency of primary care, the National Health Insurance Fund (HIF) introduced a P4P scheme in 2009 – contracts for improved individual practice (CAPI). These contracts for GPs were initially signed on a voluntary basis without altering the existing FFS scheme. The contract aimed to encourage prevention (vaccination for older patients, breast cancer screening), adherence to guidelines (diabetes management) and reduce inappropriate prescribing, in particular reducing the prescription of vasodilators (overprescribed despite being proven ineffective) and benzodiazepines (potentially dangerous and addictive) for older people. There was also a specific objective to improve efficiency by increasing generic prescribing rates. The first contracts could provide up to €7000 annually if 100% of the targets were achieved.
Analysis of the results by the HIF after one year of implementation showed modest improvement along performance indicators in all domains. However, data also showed that the results for prevention and diabetes have been improving for all GPs, and the difference between those who signed CAPI and others was not significant. No cost–effectiveness analysis was performed and the overall cost of the programme is not known. Nevertheless, the HIF decided to generalize the P4P scheme to all GPs in 2011 and broadened the objectives (related to organization of office practice, computer use in prescription and electronic data). Since 2012, with the payment for public health objectives, all physicians, including specialists, are covered by the P4P.
The role of information as a tool to influence both provider and patient behaviour has been increasingly recognized as having great potential to contribute to health system efficiency. While initiatives are bourgeoning, this is an area that is somewhat untested (Smith, 2012).
A central pillar of the health information agenda has been the drive to improve the transparency of performance data at an organizational and service level. For example, the Tallinn Charter, signed by Member States of the WHO European Region, committed members to the promotion of transparency and to be accountable for health system performance to achieve measureable results (WHO, 2008). The growth in performance measurement and reporting can, in part, be attributed to pressure to contain costs and the parallel drive to empower patients, and the improvements in technology which allow more sophisticated approaches to data collection (Smith et al., 2008). However, this drive towards transparency is not universally popular. Health systems have had to balance the potential benefits of data transparency with the needs of those professionals working within the system.
Public reporting of such data might have a number of different purposes:
to identify and prevent failure in care quality; act as a lever to drive up quality; facilitate patient choice; provide public reassurance; and provide accountability to system payers and customers (Nuffield Trust et al., 2013).Efficiency measures will play an important role in all five objectives, although the methods for incorporating quality into efficiency measurement are still developing and different actors (such as providers, insurers and consumers) will each have different perspectives on what constitutes efficient service delivery (McGlynn et al., 2008).
The construction and presentation of performance information will depend on its purpose. Policymakers, in choosing to present new data, should be mindful of their audience and the potential trade-off between high-level summative measures and granular data (Pearse & Mazevska, 2010). For example, data aimed at improving service-level performance will need to be sufficiently granular to enable clinicians to make comparisons with their peers, but this might be inappropriately complex for members of the public who might benefit from high-level, easily digestible information.
Many middle- and high-income countries have developed sophisticated registries or data sets which allow comparisons across providers with a degree of public transparency. For example, in Germany, all hospitals approved to provide care to SHI members must provide data on approximately 300 quality measures to the AQUA Institute for Applied Quality Improvement and Research in Health Care. (The G-BA has commissioned the AQUA Institute with nationwide cross-sectoral health care quality assurance.) These data returns are a key part of the Sektorübergreifende Qualitätssicherung im Gesundheitswesen (Cross-sectoral Quality in Health Care) programme which aims to provide formative feedback to health care providers to stimulate performance improvement; citizens are also the intended users (Szecsenyi et al., 2012). Performance results are then fed back to hospitals, allowing for peer comparison. From 2011, a structured quality dialogue has been initiated in circumstances where a hospital’s performance suggests a quality deficiency (Institute for Applied Quality Improvement and Research in Health Care, 2012).
The use of public reporting as a means to encourage quality improvement is based on the principle that publicly reported performance metrics motivate providers to improve based on a range of factors including, among others, professional reputation and market forces. Krumholz et al. (2008) suggested that publicly reported efficiency measures should integrate quality and cost data but warned that an emphasis on restraining costs without thorough consideration of the consequences could undermine health outcomes, thereby leading to higher costs in the future. In addition, the drive to publish publicly available information on a specific topic area or intervention may not always be underpinned by robust evidence. For example, hospital trusts in England now have to publish their staffing ratios but NICE did not find evidence to suggest that there should be a single nursing staff-to-patient ratio (NICE, 2014) (see Box 8.3).
England: safer staffing ratios.
There is evidence to suggest the public reporting of performance does have an impact on the performance of providers in health (Hibbard, Stockard & Tulser, 2003; Shekelle et al., 2008) although evidence is not fully conclusive (Ketelaar et al., 2011). Box 8.4 demonstrates how policymakers can use performance metrics in practice to inform decision-making. However, distinguishing the impact of public reporting of efficiency measures from other dimensions of performance is challenging and the use of efficiency measurement lags far behind quality measurement in health care (Hussey et al., 2009).
There is also evidence to suggest that consumers have been slow to use the increasingly comprehensive information that is available to them (Hibbard, 2008), which may diminish the effectiveness of public reporting. Presenting information on efficiency to the public can be challenging. For example, some consumers might equate high cost with high quality and low cost with low quality (Hibbard et al., 2012). This is echoed by learning from Aligning Forces for Quality, a quality improvement programme in the USA funded by the Robert Wood Johnson Foundation, which highlights the practical challenges of presenting cost and efficiency measures to the public (Aligning Forces for Quality, 2011). Focus groups were conducted as part of the programme (N=8 × 2) and found that consumers find it difficult to access and understand information on the cost of care and that consumer interest in applying cost information to decision-making depends on a range of factors including exposure to OOP costs, the severity and urgency of their condition and preconditions about provider quality (Aligning Forces for Quality, 2012).
Finland: the PERFECT (PERFormance, Effectiveness, and Costs of Treatment) project.
Across the range of major health policies reviewed for this chapter, we find that questions of efficiency are very often a major part of the rationale for intervention. Sometimes this is implicit, but more often it is explicit. Despite the focus on efficiency in the rationale and objectives for policies across all areas, we find:
little evidence that formal efficiency metrics are used in a systematic way in the development of policy; and
little evidence that appropriate efficiency metrics are monitored in the evaluation of policy.Although policies were often motivated by efficiency objectives, we find little evidence of systematic monitoring or evaluation of policies after implementation to establish whether the policy has delivered its objectives. As a result, where evidence suggests policies may not be improving efficiency, there is often policy stasis with continued reliance on policy tools that have been shown to have a limited or no effect.
A focus on efficiency and more formalized methods of considering these questions seems to be greatest in areas of policy where there is either a legal framework to policy implementation (merger control in some countries) or bodies independent of political processes, with a clear remit and framework to make decisions (HTA in some countries with formal HTA organizations).
Evidence on AE is much weaker than on TE although, for many middle- and high-income countries, questions of AE are a high priority given the pressures on health care systems from changing and increasing demands in the face of constrained resources.
In part this reflects a somewhat piecemeal approach to policy. We find relatively few examples of a systematic examination of the policy options to improve system efficiency and, as a result, much of the focus on efficiency is narrow and does not take account of the comparative effectiveness of different policies to improve efficiency.
Cross-national bodies have often led work on tools to improve policymaking in this area (see Chapter 7). For example, the OECD has a range of programmes of comparative analysis and cross-country learning on health system efficiency and fiscal sustainability. Its work on system efficiency attempted to compare countries using output-oriented DEA, which examined one output – life expectancy at birth – with two measures of input, health care spending and a composite measure of socioeconomic and lifestyle characteristics (Jourmand, André & Nicq, 2010). However, as the OECD work shows, developing overall metrics for system-level efficiency is difficult given the multiple objectives of health care systems and domains of quality, and also given that the validity of quantitative measures of relative efficiency may be challenged.
At the international level, in some specific policy areas, there have been targeted efforts to improve policymaking. There has been a growing focus on building capacity in policymakers to produce better regulation. For example, in 2012, the OECD Regulatory Policy Committee made a number of recommendations aimed at strengthening its members’ capability for regulatory reform. The OECD’s recommendations were consistent with the ROAMEF cycle and emphasized the importance of integrating regulatory impact assessment into the early stages of policy design, carrying out programme review (which would include further cost–benefit analysis) and publishing reports on the performance/effectiveness of interventions (OECD, 2012). However, as the case studies in Boxes 8.2–8.4 show, there is somewhat limited monitoring and use of efficiency metrics across the ROAMEF cycle.
Policy questions relating to single, discrete issues or issues where effective distribution of a finite budget is an overt aim, appear to lend themselves more easily to the use of efficiency metrics in decision-making. However, it is clear that policymakers do not make decisions based on efficiency metrics alone. For example, the case study in Box 8.2, which focused on the introduction of ipilimumab in Ireland, demonstrates that public opinion is a significant factor in decision-making. The role of cost-utility metrics, such as the QALY in HTA and reimbursement policy, polarizes opinion. While economists can demonstrate the relative value of an intervention on a cost-per-QALY basis, it is ultimately for policymakers to decide whether treatment for certain conditions has a higher social value and whether it is socially acceptable to impose a threshold on treatment costs. For a process so closely tied to evidence-based decision-making, the variation in approach is significant, as are the moral and ethical considerations of limiting the availability of medicine or technologies on the grounds of cost. As rising demand continues to place pressure on health care resources, it may be increasingly difficult for policymakers (particularly those representing public payers) to avoid valued-based comparisons across therapeutic areas.
Decisions on complex and controversial policy proposals will be based on a wide range of factors including societal and sector considerations (see Figure 8.3).
Factors that can influence decision-making among policymakers.
This decision-making process is equally challenging when considering wide-scale structural reform, particularly where a decision might be highly controversial or political in nature. It is in these cases in particular where the discontent between policy and research interests can be at its starkest. Policymakers need timely, concise and context-specific input, whereas research approaches often require time to produce evidence that is relevant and robust (Garrido, 2008). In such cases, decisions can sometimes be taken with limited reference to efficiency metrics. As an example, one assessment focusing on the introduction of a provider ratings regime made no attempt to quantify the expected benefits despite introducing a policy that would potentially impact over 21 500 providers of health and long-term care (DoH, 2014).
A key issue is the availability and accessibility of underlying data to construct meaningful efficiency metrics. Often policymakers use what is available as metrics rather than investing in new, specific data and monitoring. For example, both DRG-based payment systems and P4P have a conceptual appeal. It seems logical that payment should be related to demonstrated performance on the objectives established by payers. However, these general schemes for payment need to be carefully adapted to pursue specific policy objectives and ensure their efficiency, and only a small number of partial efficiency measures are used for evaluating such payment schemes. In many European countries where activity-based payment has been introduced, hospital costs and care quality are not tracked sufficiently. Obtaining access to itemized hospital cost data is a lengthy process for researchers in many countries. The evaluation of most of the P4P schemes concentrate on monitoring the process variables that are part of the payment scheme without properly assessing the associated costs. Potential perverse effects (unintended consequences) of these schemes (for example, patient selection, induced demand) are rarely studied since this often requires data beyond those collected routinely within these schemes.
A second issue is the mutualization of the knowledge and information on well-established efficiency metrics to evaluate policy. Without robust measures it is not possible to provide sound analysis or to find the right policy direction to take. For example, despite the bulk of evidence on their costs, France does not regularly monitor hospital adverse events as a quality measure. This means that hospitals having adverse events are better remunerated, since these are coded as CCs which receive a significantly higher tariff.
The other key issue is that the evidence on efficiency is often not clear-cut and the policy implications are open to different interpretations. For example, in relation to payment reform, all payments models have pros and cons that should be identified, monitored and compensated for. The payment reforms aiming to reinforce efficiency of specific providers often ignore results at the system level for AE, and efficiency is not the sole objective of the payment system. Many countries have objectives that relate to transparency, accountability and equitable funding (O’Reilly et al., 2012).
There will always be circumstances where decision-makers want to try something new – perhaps an intervention with a limited evidence base – or need to respond quickly to a scandal or pressing policy issue. However, it is critical that sufficient attention is placed on the monitoring, evaluation and feedback stage of the ROAMEF cycle and that proper consideration is given to the use of efficiency metrics to support these stages.
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The Cancer Drugs Fund was established in 2011 and is due to continue until 2016. It provides additional funding for cancer drugs in England which have not been approved by NICE.
The G-BA determines which services and technologies are to be reimbursed by the SHI funds and comprises physicians, dentists, hospitals and health insurance funds in Germany.
Moral hazard is a situation in which people or organizations may increase their risk-taking or consumption above allocatively efficient levels because all or part of the costs will be borne by others.
Yardstick competition is the system of using comparative information about organizations with local or sector-based monopolies to set prices or performance standards.
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