Elsevier

Health Policy

Volume 88, Issues 2–3, December 2008, Pages 236-242
Health Policy

Economic barriers to implementation of innovations in health care: Is the long run–short run efficiency discrepancy a paradox?

https://doi.org/10.1016/j.healthpol.2008.03.014Get rights and content

Abstract

Favourable cost-effectiveness of innovative technologies is more and more a necessary condition for implementation in clinical practice. But proven cost-effectiveness itself does not guarantee successful implementation. The reason for this is a potential discrepancy between long run efficiency, on which cost-effectiveness is based, and short run efficiency. Long run and short run efficiency is dependent upon economies of scale. This paper addresses the potential discrepancy between long run and short run efficiency of innovative technologies in healthcare, explores diseconomies of scale in Dutch hospitals and suggests what strategies might help to overcome hurdles to implement innovations due to that discrepancy.

Introduction

Cost-effectiveness analysis as part of the evaluation of medical innovations has become mainstream in several European countries as in the US. If the evidence on therapeutic value and cost-effectiveness of medical innovations is convincing, implementation in clinical practice is warranted. However, a characteristic of the healthcare sector is the somewhat fuzzy priority setting about implementation and the numerous potential conflicts between the stakeholders in the health system. Also, behavioral factors in individual health professionals, such as clinical inertia and persistent routine behaviors, may inhibit change. Therefore, implementation of evidence-based innovations and guidelines does not follow automatically, as there might be barriers for change at different levels that need to be solved. Specific strategies targeting increasing speed and level of adoption of innovations can be launched, such as ‘tailored’ strategies directed at the medical profession and patients (for example, providing information, education, training, communication, etc.). However, in a situation with convincing cost-effectiveness evidence and a high willingness to implement by the medical profession, implementation of a technology or guideline into clinical practice might stagnate because there are negative consequences for specific stakeholders. This paper argues that implementation of technologies, driven by cost-effective evidence, might be hampered due to specific economic barriers related to disincentives to implement by management of care providers.

Key in the argumentation about successful implementation directed to technological change is the state of equilibrium in production: long run versus short run. A necessary condition for achieving technical efficiency (an assumption underlying long-term cost-effectiveness) is that all inputs can be set at their cost-minimizing levels [16]. However, inputs such as capital but also personnel with fixed labor contracts are difficult to adjust quickly to respond to changing output (levels) and will therefore not be set at their cost-minimizing level, given the output produced. This might have consequences for the management of health care providers who are often accountable for short run results, for example, a balanced yearly budget or is restricted by tight financial frameworks. In health care systems where budgetary exceeds are sanctioned by a discount in the budget for next year (for example in the Netherlands) achieving budget becomes so important that those who perceive great pressure to meet budget, may be less inclined to partake of any activity that may cause temporarily inefficiencies in their environment [1].

The aim of this paper is to come up with implementation strategies that are able to create a short run equilibrium that equals the long run equilibrium, making the often anticipated discrepancy between long and short run equilibrium to a paradox. This paper is structured as follows: first, a case in clinical practice of a cost-effective innovation that is illustrative for the long run–short run discrepancy in efficiency is presented and put in a theoretical economic perspective. Second, (dis)economies of scale, being an important source in explaining the long run–short run discrepancy, are discussed. Further, to get a quantitative impression whether diseconomies of scale are present in health care the prevalence of variable returns to scale is investigated in a sample of 33 Dutch hospitals. Third, using a case in diagnostics, implementation strategies that are able to overcome short run diseconomies are explained.

A study about a new combined outpatient and home-treatment of psoriasis technology showed that 89% of the anticipated savings, based on the outcomes of an earlier performed cost-effectiveness analysis, could not be achieved in the short run when implementing this technology due to inflexibility of production factors labor and infrastructure [2], [12]. In absolute figures this meant that only €694 of the anticipated €6058 savings per patient could be freed and consequently over €5000 per patient could not be re-invested in the outpatient and home-treatment in the short run. This was a serious obstacle for health care management to implement the technology. On the other hand, the government and the medical profession strongly advocated the new technology as it was found convincingly cost-effective [12]. This resulted in the situation that the inpatient alternative (usual care) existed next to the more efficient combined outpatient and home-treatment of psoriasis. It was argued that the opportunity costs of using the inpatient treatment modality on the short run was very low. Hospital beds and personnel on the dermatology ward were fixed factors of production in the short run with little alternative use. Ultimately both psoriasis treatment modalities co-existed in the same organization but were used at less than optimal capacity. So in this particular case successful implementation of the innovation failed.

Obviously information about an innovation's cost-effectiveness provides not all economic information necessary for decision making about successful implementation. The evaluation of costs and benefits of new technologies and implementation of technologies is generally discussed in the context of Welfare Economics where welfare losses on the short run are considered ‘sunk’ [9], [11], [15]. This might be true for stakeholders that decide on a long enough planning horizon where all production factors are flexible. In fact this convexity assumption (mathematically: a set in Euclidean space R is convex if it contains all the line segments connecting any pair of its points) is traditionally invoked in economics. However, not without criticism. Farrel (1959) points to indivisibilities and economies of scale as sources of non-convexities [10]. Allais (1977) confirms Farrel's arguments where he rejects global convexity but favors local convexity [3]. More recently, for example, Briec et al. (2004) and Tone and Sahoo (2003) point to non-convexity behavior of inputs [6], [17]. In a global convexity context, technologies implemented by health care providers are assumed to be infinitely divisible and production factors are supposed to be operating in a constant returns to scale region. Local convexity constraints this reasoning to a specific range. Global convexity is not always the case on the short run [2]. Some factors of production are costly to adjust in the short run, and this induces short run decreasing returns to scale and an upward sloping short run marginal cost curve. Short run inefficiencies might cause disincentives for health care management to implement innovations. Implementation strategies directed to solve the short run–long run discrepancy can be exogenic and endogenic. Exogenic means the solution lays outside the care provider, for example, changing the health system (and budgetary system) in a way that is consistent with decision making based on long run efficiency. This paper focuses on endogenic implementation strategies meaning strategies that can be employed by the care provider itself. Therefore to optimally implement innovations that in the long run are found cost-effective, implementation strategies need to be directed at minimizing welfare losses, or inefficiencies, on the short run. To build effective implementation strategies information about potential short run diseconomies is necessary.

The relationship between short run and long run efficiency is in microeconomic theory associated with economies of scale and scope [5], [15]. Returns to scale is a long run concept that reflects the degree to which a proportional increase in all inputs increases output. For example, one can consider scale economies to the degree that costs change in relation to changes in number of diagnostic performances. Cost-effectiveness analysis assumes constant returns to scale [1], [9], [11]. This occurs when a proportional increase in all inputs results in the same proportional increase in output. This assumes that all production factors are functioning at optimal capacity. The case of the outpatient treatment of psoriasis innovation clearly shows that, at least on the short run, both treatment of psoriasis modalities do not function at optimal capacity and suffer from inefficiencies. These inefficiencies are due to diseconomies of scale. Diseconomies of scale refer to the relationship of average costs with volume of production. Diseconomies of scale arise when marginal costs of production get, with increasing volume of production, higher than average cost. This may be the result of a variety of factors: returns to scale, behavior of overheads, indivisibility of factors of production, nature of contracts between different stakeholders and organizational governance [1], [17].

Diseconomies of scope refer to the multipurpose use of capital investments. Diseconomies of scope are conceptually similar to diseconomies of scale. Where diseconomies of scale refer to changes in the output of a single technology, diseconomies of scope refer to changes in the number of different types of technologies [1]. Being such a major source in explaining the potential long run–short run efficiency discrepancy raises the question whether (dis)economies of scale are prevalent in health care. To investigate returns to scale in health care a sample of 33 Dutch hospitals was researched. The data came from annual reports of Dutch hospitals over the year 2003 [14]. To create insight in whether these hospitals are working in the area of constant returns to scale, increasing returns to scale or decreasing returns to scale, data envelopment analysis (DEA) was applied. DEA is a non-parametric approach originally developed for evaluating the performance of a set of peer entities using linear programming techniques [7]. According to Coelli et al. (1998) in the non-profit service sector, where random influences are less of an issue, multiple-output production is important, prices are difficult to define and behavioral assumptions, such as cost minimization or profit maximization, are difficult to justify, the DEA approach may often be the optimal choice [8].

The inputs of the hospital production function were: personnel costs, feeding and hotel costs, general costs, patient related costs and maintenance and energy costs. The outputs were: number of day-care, number of hospital days, number of first outpatient visits. This production function is not complete, however the inputs and outputs are well acceptable and generally used in this context for a hospital environment. Inputs and outputs were on a yearly basis. The basic constant returns to scale DEA model is represented as follows:Minθs.t.j=1nλjxijθxi0i=1,2,,m(inputs)j=1nλjyrjyr0r=1,2,...,s(outputs)λj0where λj is the weight given to hospital j in its efforts to dominate hospital 0 and θ is the efficiency of hospital 0. Therefore, λ and θ are the variables to solve from the model.

Fig. 1 displays two production frontiers, one under constant returns to scale and one under variable returns to scale. Scale efficiencies are found by comparing efficiency on the variable returns to scale frontier to efficiency on the constant returns to scale frontier [8]. The variable returns to scale model adds the convexity constraint j=1nλj=1 to the constant returns to scale DEA model. This approach forms a convex hull of intersecting planes which envelope the data points more tightly than the constant returns to scale frontier. The convexity constraint ensures that an inefficient hospital is only benchmarked against hospitals of a similar size. To explore returns to scale both DEA models were run using an input-orientation.

Solving the VRS model (Table 1) shows that variable returns to scale are prevalent in Dutch hospitals. Results show that 55% of the hospitals were operating under decreasing returns to scale, 33% under constant returns to scale and 12% under increasing returns to scale. Based on these results it seems appropriate to assume that (dis)economies of scale are prevalent in the Dutch hospital environment and consequently a potential concern for implementation of innovations.

Recently it was found that in patients with suspicion for prostate cancer diagnosis using a MRI device (with a specific contrast liquid) is cost-effective (the point estimate showed dominance) compared to diagnosis with a CT device [13]. From a long run perspective it is therefore clinically and economically sound to substitute CT for MRI in this particular patient group. Now let us assume that prior to substitution both modalities CT and MRI were functioning at long run equilibrium, ie., optimal capacity (at constant returns to scale). To deal with the extra demand for MRI it is necessary to increase capacity by investing in an extra MRI device. On the short run output (number of MRIs) will increase less than proportionate with the input (production factors) (see Fig. 2). This results in diseconomies of scale on the short run, assuming that on the long run constant returns to scale is restored. Implementation of the MRI modality has also consequences for the CT modality. To successfully implement MRI there is a need to establish substitution between both modalities. So an increase in demand for MRI comes with a decrease in demand for CT in the short run. This demand shift causes to reduce the multipurpose function of the CT modality and causes, on the short run, diseconomies of scope (see Fig. 3).

How do these phenomenon translate into barriers of implementation? This can be illustrated with the following numerical example. Table 2 shows the hypothetical production process on a yearly basis of the CT and MRI modality for a particular organization. Let us assume that fixed costs of production constitute of depreciation costs of the CT and MRI device, respectively. Variable costs constitute of personnel and material cost like contrast liquids. In the short run the health care provider has an incentive (marginal costs decrease) to meet the extra demand by investing in the variable input. However, in Table 2 the assumption is made that the health care provider is functioning at full capacity and in order to meet the extra demand for MRI needs to invest in an extra MRI device. Also assume that the health care provider negotiated earlier an internal transfer price or with the insurance companies a tariff of €75 for a CT image and €165 for an MRI image. Initially profitable performances need now to be compensated elsewhere in the health care organization to meet a balanced budget at the end of the year. The management which is accountable for a balanced budget at the end of the year has, under these circumstances, a disincentive to implement the MRI modality. This disincentive is due to inflexibility of the production factor MRI and CT capacity. In general innovations cause shifts in the demand for services. Implementation strategies consequently should be directed at increasing flexibility of production factors.

The first step in the development of a framework for implementation strategies directed at increasing flexibility in production factors is the identification of fixed factors of production. Adang et al. (2005) developed a simple checklist specifically for the health care context to detect the proportion of fixed costs in the total production costs [2]. Next, the fixed factors of production need to be disentangled into specific factors, for example, housing, capacity and personnel. Next a strategy directed at increasing flexibility of the specific production factor(s) should be developed.

To illustrate this approach it will be applied to the diagnostic modality case. Employing the checklist to the diagnostic case shows that diagnostic capacity (both MRI and CT) is the major inflexible factor. Now a tailored strategy directed at making diagnostic capacity more flexible needs to be developed. To make capacity more flexible urges for capacity planning. Depending on whether the production process is operating under decreasing returns to scale or increasing returns to scale a capacity planning strategy needs to be developed. Under increasing returns to scale capacity planning should be directed to increase production with the same inputs. Under decreasing returns to scale outsourcing production to meet extra demand can be a way of capacity planning without changing the size of operations in the organization. This means that the organization facing these inefficiencies needs to search for organizations that have excess MRI capacity available and are in need for extra capacity CT. Consequently this organization faces search costs that result in an increase in marginal cost. A regional market for diagnostic capacity could limit search costs and increase the adoption of innovations by limiting or even eliminating short run diseconomies. Such a market is not viable if all organizations in that particular market are operating at optimal capacity (i.e., constant returns to scale, meaning the most productive scale size). This means that some organizations need to operate under increasing returns to scale whereas others operate under decreasing returns to scale. These latter organizations produce above the optimal scale of operations and would improve their efficiency by downsizing or outsourcing, whereas for the organizations operating at increasing returns to scale it is efficient to increase production.

A minor inflexible factor of production (semi-fixed) was personnel. An MRI image takes more time than a CT image. An implementation strategy directed at personnel, being the inflexible production factor, could be for example teleradiology. According to Wachter the technical and logistic hurdles of remote teleradiology have been overcome [18]. This implementation strategy follows the same principles as in the capacity example. Other implementation strategies aiming at increasing flexibility of the labor force are ‘learning’ and ‘making labor contracts more flexible’ [4].

Section snippets

Conclusions

Implementation of cost-effective technologies (technological devices, practice guidelines, etc.) in clinical practice is a crucial process in healthcare, as in all industries. Health technology assessment has focused on long-term efficiency at societal level, ignoring short term inefficiencies for a specific hospital or other stakeholder. Research on implementation processes in healthcare has focused on perceived barriers for behavioral change and on educational interventions targeted at health

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