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Stewart Anderson

Health Care

Measuring and Optimizing Healthcare Quality

Patients value ‘noncontractible’ quality, although they find it difficult to measure

Published: Tuesday, October 1, 2013 - 12:17

A recent news story here in Ontario detailed how health officials were reviewing the results of 3,500 CT scans and mammograms at two Toronto-area hospitals because of potential errors caused by a radiologist’s “performance issue.” Although the results of that review are still pending, the story once again brought to the fore long-standing concerns with the quality of healthcare services.

Managing and improving healthcare quality is a complex issue because quality, in a healthcare setting, is a multidimensional concept. In healthcare, quality is determined not only by the ability of physicians who make diagnoses and provide treatment, but also by other attributes of service delivery such as attentiveness, care, and diligence.

Certain aspects of healthcare quality are observable and measurable, and thus contractible, by patients, who can make informed choices about the levels of quality they wish to receive. For example, patients may prefer to receive treatment in a certain hospital because the rooms are bigger, the food better, or the nurse-to-patient ratio more favorable. On the other hand, certain aspects of healthcare quality are not directly observable by patients and are therefore noncontractible. These aspects of quality might include such things as the accuracy with which CT scans are reviewed and interpreted or the attentiveness of nurses.

Why the distinction? Simply because noncontractible quality is of value to the patient, but the patient is not well-positioned to measure it. Thus, healthcare organizations cannot usually use noncontractible quality as a basis for payment because it is difficult, if not impossible, to incorporate these quality attributes into a payment system. Like contractible quality, noncontractible quality is costly for healthcare providers to produce. Most important, there is an inherent information asymmetry present with noncontractible quality where one party to a transaction (a healthcare provider) may have information that another party (the patient) does not possess. Due to this information asymmetry, patients may be unable to really know whether noncontractible quality has been provided. As a result, there may be an incentive for providers to skimp on noncontractible quality by providing a socially suboptimal level of quality, which in turn could lead to market failure.

How, then, should healthcare quality be measured? In 1966, Avedis Donabedian, a physician and health services researcher at the University of Michigan, produced a model for assessing the quality of care in clinical practice that remains relevant and useful today (see diagram below).

In Donabedian’s model, there are three dimensions to measuring healthcare quality. The structure dimension refers mainly to input use. Consider, for example, that a hospital’s production function, in Cobb-Douglas form, is given by:

Q = ALβKα

where Q is output, L is the quantity of labor, K is the quantity of physical capital, A is a productivity measure, and β and α are the output elasticities of labor and capital, respectively. (The Cobb-Douglas form is not the only form such a function may take, nor is it necessarily the best form for expressing a hospital's production function.) Output elasticity measures the responsiveness of output to a change in levels of either labor or capital used in production, if everything else is held constant. For example if α = 0.30, a 1 percent increase in capital usage would lead to approximately a 0.30 percent increase in output.

Under Donabedian’s model, structure measures are contextual measures that describe the uses to which the factor inputs (e.g., capital and labor) of the production function are put by the hospital. These measures may include such things as the range of services provided, the number of full-time equivalent nurses per bed, and staff absentee levels.

Process measures in Donabedian’s model refer to how care is delivered. Process measures cover all the actions that encompass healthcare delivery, including technical and interpersonal processes. This category of measures would embrace such things as wait times in the ER, the frequency of appropriate prescriptions for certain conditions, and whether the right diagnostic tests were ordered.

Outcome measures relate to the effects and results of the care delivered. This would include measuring such things as changes to health status as a result of treatment, the frequency of adverse effects or outcomes from treatment, and the prevalence of hospital-acquired infections.

Although outcome measures are often used to assess a healthcare organization’s quality, this category of measures is not without its drawbacks. Foremost among these is the fact that negative (or positive) outcomes often occur for reasons other than the quality of medical care. Second, it may be difficult to establish causal connections between process and outcomes unless sufficiently large data samples are used, and adjustments made to allow for the effects of intervening events that may have occurred to patients following the cessation of treatment.

While a strength of Donabedian’s model is that it can be applied to a variety of quality problems of either a broad or narrow scope, the model also highlights the fact that it may be impractical to measure every relevant aspect of healthcare quality. Healthcare practitioners must therefore carefully select those measures that will lead to a better understanding of their system and that help establish causal chains that can be used to design appropriate improvement interventions. Most important, the use and communication of suitable process and outcome measures may help to address noncontractible quality issues and reduce the information asymmetry associated with noncontractible quality.

A key question with respect to healthcare quality is the degree to which productivity influences the supply of quality. Productivity differences may be related to differences in quality. For example, a hospital with higher productivity may also have a lower marginal cost of quality, in turn incentivizing the hospital to supply higher quality to the marketplace.

As a rule, hospital costs increase with output and wages, and may also increase with quality. Productivity affects costs, with more-productive hospitals having lower fixed costs, lower marginal costs, or both. A major reason why this is so is that it is quite difficult to substitute capital for labor in a healthcare setting. For example, a hospital may improve its physical capital by acquiring the latest equipment, yet it may still require the same amount of labor to operate the new equipment as it did the old. (This partly explains why healthcare productivity remains relatively low and why costs continue to rise, despite advances in medical technology and equipment.) How effectively a hospital uses its factor inputs—capital and labor—will determine its productivity and hence the level of quality it can produce relative to its output.

The word “productivity” can be used to mean several different things. The most common measure of productivity is the ratio of output to labor input, which is called the average product of labor. Total-factor productivity, or TFP, which is the letter A in the Cobb-Douglas production function cited above, measures an organization’s overall efficiency in transforming inputs into outputs. With TFP, the idea is that, holding A constant, both the average and marginal products of labor increase when the ratio of capital to labor is increased. Why? Because labor can be more productive if it has more or better capital (e.g., equipment, technology) to work with. However, as previously noted, it is difficult to substitute capital for labor in healthcare settings. Therefore, finding ways to more effectively combine capital with labor becomes a key to improving productivity and lowering costs.

Whether a healthcare organization, such as a hospital, is for-profit or not-for-profit will determine how it goes about choosing the optimal level of quality to produce. For both types of organization, the issue is essentially a constraint maximization problem.

The problem for a for-profit hospital is to set its output quantity, quality, and price, to maximize profit. Price will tend to rise with increases in quality and tend to fall with increases in quantity. Total cost will be directly related to the output and quality levels chosen. In this case, finding the optimal levels of output and quality that maximize profit involves setting the output quantity at the level where marginal revenue equals marginal cost, and setting quality at the level where the marginal revenue resulting from a change in quality equals the marginal cost of changing quality. Thus, the hospital finds the quantity-quality combination that yields the largest difference between revenue and cost, thereby maximizing profit.

For a hospital that is a not-for-profit organization (about two-thirds of U.S. hospitals fall into this category), the hospital will attempt to maximize utility, instead of profit, subject to a constraint. This will involve determining the hospital’s quantity-quality frontier (i.e., production possibility curve), which shows all feasible combinations of quality and cost, given the hospital’s demand and average cost curves. From its utility function, the hospital then finds the point where utility will be maximized at the point of tangency of the slope of the utility function and the production possibility curve.

In both cases, higher productivity can lead to lower costs, allowing the hospital to provide a higher level of quality than would otherwise be possible. Productivity improvement can therefore be a pathway to offering a higher level of quality with the same factor inputs, or offering the same quality with less factor inputs.


About The Author

Stewart Anderson’s picture

Stewart Anderson

Stewart Anderson is a partner with Anderson Lyall Consulting Group, a Toronto-based consulting and advisory firm that helps firms develop their competitive advantage. Anderson’s background and expertise includes competitive strategy and value chain engineering. He has advised companies in the manufacturing, service, and contract manufacturing industries. Anderson is completing his bachelor of arts in economics and he is a certified trainer in lean manufacturing principles and techniques.