Introduction Robert T. Riley

Introduction

Robert T. Riley

Economic Impacts

The true techie says, “If it can be done, it should be done!” The true bean counter says,

“Show me a detailed cost-benefit analysis—and I don’t want to see any of those phony soft benefits included!” In economic terms, today’s optimal health informatics strate- gies lie somewhere between these two extremes. Healthcare systems everywhere are facing increased economic pressures; therefore, our informatics endeavors must recognize and adapt to these pressures.

Key Historic and Organizational Points

Before looking at some basic economic approaches and tools, we need to recognize several key historic and organizational points that strongly affect the economic strug- gles occurring within many of our healthcare organizations.

Information and Health Care

Many healthcare organizations have been slow to realize just how critical information and its proper management are to the modern healthcare organization. As an example, hospitals in the United States typically spend 2 to 4 percent of their revenue on information systems, while organizations in other industries typically spend 5 to 15 percent. Even in the late 1980s, there were still a few hospitals in the United States that could not produce an itemized, computerized bill on demand—let alone a revised one.

While computerization has lagged behind other industries in the administrative areas it lags even further behind in the clinical areas. It is a general axiom of informatics that computers produce their greatest productivity benefits when they reach the hands of those on the “factory floor,” i.e., those people in the system who actually produce the organization’s goods or services.

Physicians—the historic driving force in the clinical area—have traditionally valued

most highly the information they receive by word of mouth from other physicians. For

a variety of reasons, many have been slow to utilize systems with significant potential

for providing information in written form from alternative sources, and this has slowed

the “factory floor” implementation.

Weak Accounting Systems

Quality decision making requires quality inputs. Many healthcare organizations have accounting and tracking systems that were never designed to provide sound cost and operational data. Systems have typically been designed to ensure fiduciary accounta- bility and meet mandatory billing and reporting requirements. To compound the problem, one of the biggest challenges for any complex organization is the accurate costing of diverse products and services. As a result, many healthcare organizations today cannot accurately determine whether they are making or losing money on par- ticular services.

The Pace of Change

“But we just bought the one we have two years ago!” is often the complaint.

Many decision makers have difficulty accepting the economic aspects of today’s rates of technological change—whether in informatics or in other health areas. Regardless of logic or economic analyses, these “bill payers” often have a negative visceral reaction to replacing an expensive physical unit that they still psychologically regard as new.

Historic Investment Benefits

Many noninformatics managers, especially financially oriented ones, are disillusioned by the mediocre measurable benefits from the money that has been poured into the informatics area over the years. Where are the miracles we were led to expect? Stan- ford economic historian Paul David

has explained this phenomenon. When major new technologies are introduced, they are first applied to the existing paradigms—organi- zational structures, market structures, legal frameworks, workforce skills, etc. The full benefits of a new technology are realized only as the total framework or paradigm slowly shifts to allow these benefits to take effect. At the same time, paradigm shifts might well not be possible without the new technology. The full benefits of informa- tion systems in our healthcare systems will accrue only after our healthcare systems have undergone massive transformations, which in turn cannot happen without these new information technologies. In health care, we are much closer to the beginning of these massive transformations than to the end.

Budgets As Power

A joking remark about American males is, “He who dies with the most toys wins!” For living bureaucrats, the winner is often perceived as the manager with the biggest budget or the largest staff, the library with the most books, or the hospital with the most beds (or increasingly in the United States, the hospital with the most insured patients).

Bigger is better; therefore, any technology promising that the job can be done with a smaller budget, less staff, fewer books, or fewer beds normally encounters resistance from everyone except the bean counters. For example, as the information function has become decentralized in recent years with the advent of distributed systems, many tra- ditional centralized data processing managers have stubbornly—and even bitterly—

resisted a decrease in their direct budgetary power.

Cost-Benefit Analysis

Regardless of what the process is called, virtually every informatics program or project must survive some form of cost-benefit analysis. What will this system cost? What will it do for us? Will the benefits to the entire organization exceed the cost and by how much? As with many management terms, there is no standardized definition of the term cost-benefit or of the analytical process. Twenty different organizations could perform such an analysis in twenty different ways. Despite these differences, we need to examine a few key points common to all cost-benefit analyses.

Analysis vs. Rationalization

Organizational life becomes easier when we finally internalize the following observa- tions: Most organizational decisions are made at the emotional level, and logic is sub- sequently used to justify or rationalize them. Nevertheless, decision makers defend to their dying breaths the belief that decisions were made on a completely rational basis.

As a result, cost-benefit analysis in many organizations has become cost-benefit ration- alization, “massaging” of the data until a desired outcome is obtained. We know that we need this new system, and we also know that our recommendation will be chal- lenged. Therefore, we will ensure that the cost-benefit data for the system appears very positive.

For example, one hospital chief executive fell in love with a particular informatics system he felt would give him national prominence if it were implemented. Despite the extremely questionable technology involved, the staff was charged with producing a study that would make the proposed system appear to be the answer to all their prob- lems—including some they didn’t even have! The chief executive then moved on to greener pastures, leaving the hospital with a glowing cost-benefit analysis in the files and the hospital staff saddled with a mediocre dead-end system.

This is not the way to run a quality organization, but it is all too common. Unfortu- nately, we may be forced at times by organizational pressures to “play the game.” Still we should always be clear in our own minds as to when we are analyzing and when we are rationalizing.

Hard vs. Soft Costs and Benefits

In any significant cost-benefit analysis, we face the twin problems of measurability and uncertainty. Measurability refers to the difficulty in determining exact measures of eco- nomic value for subjective variables—especially on the benefits side—such as staff morale and improved quality of services. The terms hard and soft are often used to rep- resent the ends of the measurability spectrum. In addition, there is uncertainty, which refers to the difficulty in accurately predicting the values of variables that may be quite measurable after the fact, e.g., hardware costs 3 years in the future or just how many staff we will be expected to service the hardware 2 years from now.

Early health informatics implementations tended to be in the accounting area,

paralleling the trend in other types of organizations. These early implementations

appeared to have attractive levels of measurability and certainty: The processes to be

computerized were highly structured and well known; there was typically only a small,

disciplined group of users; and the benefits were typically predicted as definite mone-

tary savings resulting from labor displacement, i.e., hard benefits. In reality, the costs

often overran estimates by huge amounts, and many of the cost savings were never realized. Still, viewed in advance, cost-benefit analyses in this era had an aura of “fiscal responsibility” about them that appealed to the bean counters.

As health informatics implementations move more into clinical areas, the traditional levels of measurability and predictability inevitably fall. How do we assess the eco- nomic value of various types of improvements in patient care? How do we predict all the direct and indirect costs that this complex implementation will ultimately incur?

Dr. Michael Bourke

also makes a parallel point for heavy investments in a technical infrastructure, e.g., a backbone network. This infrastructure investment may enable massive future progress in a wide variety of specific applications areas, but we may find it very difficult to measure and/or allocate its precise benefits. In Bourke’s terms, an investment with the lowest “directness of benefits” and “quantifiability of benefits”

might well have the highest “potential impact of benefits.”

Probably the most neglected soft issue on both the cost and benefit sides is the time and/or inconvenience costs imposed on various groups coming into contact with the system. Time costs or inconveniences imposed on employees are often rationalized by saying, “That’s what they are paid for.” Costs to users or customers have also often been ignored. A classic example is the waiting time imposed on patients by many physi- cians who use atrocious, self-serving office scheduling models.

By this time, you might logically conclude that we view cost-benefit analysis as a useless tool. Not at all. What alternative do we have? We ultimately have to make a judgment as to whether a proposed step is worth more than it costs. Otherwise, we can simply resort to tossing a coin or using a Ouija board.

Realistic Cost-Benefit Judgments

We must face the reality that our limited current financial technologies—coupled with the high uncertainties of a fast-changing world—dictate that our informatics cost- benefits analyses require managerial judgments. There are no techniques that can mechanically produce accurate numeric values and generate “automatic” decisions.

We inevitably face a mass of data of widely varying quality that we try to organize as best we can into useful information. However, at some point we must tighten our belts and say yea or nay, with our decision based on much poorer information than we would like. It will always ultimately be a judgment call, given that some will be easier than others.

It is essential that we list in a subjective sense the wide variety of cost and benefit areas that we anticipate will be involved. This is typically quite impossible. The problem is to assign reasonable economic values or ranges of values to these cost-benefit impact areas. One process that can help in producing reasonable rough estimates is the mod- ified Delphi process described by Lorenzi and Riley

in Organizational Aspects of Health Informatics. Group processes such as this can be very effective in assessing values in ill-structured, amorphous situations. Ultimately, we may have to feel or think that there will be a cost or benefit in some area but have no way to judge, estimate, or even guess its magnitude.

The true value of this overall process is often more in the reduction of subsequent

unpleasant surprises than in the specific cost or benefit estimates developed. A

Pandora’s box discovered and opened in the cost-benefit stage is far less dangerous

than one discovered only at some later project implementation stage.

A Few Basic Economic Tools

During his Pentagon days in the early 1960s, economist Alan Enthoven

commented that most of the economic analysis tools used in the Department of Defense (DOD) were taught in introductory economics courses; however, the DOD had to hire Ph.D.

economists to perform analyses because their analysts didn’t really believe in the tech- niques and acquire a vested interest in them until they had a Ph.D. in the subject. Forty years later, Enthoven’s comments are still relevant. We can perform some very pow- erful economic analyses in the health informatics area without resorting to arcane tech- niques or complex mathematics, always keeping the following basic tools and concepts in mind.

Sunk Costs

In economic analyses of what actions to take, sunk costs are irrelevant. The only rele- vant issue is a comparison of the future cost-benefit options. Unfortunately, past expen- ditures are often not irrelevant in psychological or organizational terms. Abandoning a poor prior decision may be a personal admission of failure, or we may fear that it will diminish us in the eyes of others. Influenced by these forces, we may continue to pour good money after bad.

Fixed and Variable Costs

When analyzing or forecasting costs, we need to make a distinction between which costs are relatively fixed, regardless of the volume of activities, and which costs vary with the level of activities. Part-time staff and consulting services are examples of variable costs.

An equipment lease is a fixed cost.

Average vs. Marginal Cost

A key economic cost concept is the difference between average costs and marginal costs. Assume that we have a system with fifty users that costs $1 million annually. Our average cost per user is then $20,000, a straightforward computation. The marginal cost is the cost of adding one additional user. This cost might be much less than, equal to, or much greater than $20,000, depending on the relationship between fixed and vari- able costs. In the informatics area, marginal cost is especially important whenever we encounter “modular” scenarios—or in economic terms, discontinuous cost functions.

Suppose that a particular communications “box” costs $5,000 and will support up to sixteen users, with each of the users costing an additional $200 for a “card” to be added.

The marginal cost of adding a fifteenth or a sixteenth user is relatively small—$200 in each case. The marginal cost of a seventeenth user is relatively large because it would cost $5,000 for a second box plus $200 for a card, for a total of $5,200. Of course, the marginal cost for the eighteenth user would again be only $200. On the software side, vendor pricing schemes for site licensing often create similar cost scenarios.

Diminishing Marginal Productivity

If it were not for the law of diminishing marginal productivity, we could grow food for

the whole world in a flower pot. If we have fixed amounts of any resource and keep

adding more and more of other resources, we will reach a point where the marginal

productivity of the additional resources starts to decline. The benefit we receive from additional units of input declines steadily. The marginal productivity can even turn neg- ative when the original fixed resource is overloaded enough by the new resources; e.g., putting too many additional programmers on a project actually delays its completion, or giving people too much training can actually decrease their productivity.

Marginal Analysis

Marginal analysis is a practical tool for examining the impact of the law of diminish- ing marginal returns. Suppose someone proposes a system that will cost $3 million and will process 90 percent of our transactions without human intervention. The marginal analyst says, “Wait a minute. What would 85 percent cost? 95 percent?” Table IV.1 shows a common cost pattern for this type of situation; however, the “correct” decision as to what percent age level should be selected is strictly a managerial judgment. Mar- ginal analysis merely provides a more intelligent basis for this judgment.

Case Introduction

The case presented next discusses the financial evaluation process for the implemen- tation of a clinician decision support tool (sometimes referred to as a clinical dash- board) in a primary care setting at Vanderbilt University Medical Center in Nashville, TN. This case reflects the current thrust to use information technology to address the escalating costs within today’s healthcare systems. Some of the information included also gives a feeling for the magnitude and complexity of modern healthcare informa- tion systems.

References

1. David PA. Computer and Dynamo: The Modern Productivity Paradox in a Not-Too-Distant Mirror. CEPR Pub. 172. Stanford, CA: Stanford Center for Economic Policy Research, 1989.

2. Bourke MK. Strategy and Architecture of Health Care Information Systems. New York:

Springer-Verlag, 1994;186–189.

3. Lorenzi NM, Riley RT. Organizational Aspects of Health Informatics: Managing Technological Change. New York: Springer-Verlag, 1994;132–133.

4. Enthoven AC. Defense and disarmament: economic analysis in the Department of Defense.

American Economic Review 1963; 53(2):422.

T

IV.1. Example of marginal cost analysis.

Percent of cases Marginal cost of

handled without performance

intervention Total cost ($) improvement ($)

80 2,300,000 —

85 2,500,000 200,000

90 3,000,000 500,000

95 4,000,000 1,000,000

100 6,000,000 2,000,000