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Resources And Operations Management In Health Care Essay Sample

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Resources And Operations Management In Health Care Essay Sample

Health care is the No. 1 domestic industry in the United States and one of the largest industries in the developed world. Health care systems present many complex problems that could benefit from operations research-type analysis and applications. OR professionals, however, have generally neglected the field.

Less than 2 percent of INFORMS members—about 180—belong to the Health Application Section. Eighty of those are academics and another 30 are students. Twenty-two call themselves practitioners, 10 are consultants, and only two actually work at a hospital. Many OR people have written at least one paper on health care or have been involved in at least one health care project—and then moved on to something else. Very few OR people specialise in health care.

Why don’t more OR people work in health care? The health care industry faces many of the same issues confronting other industries, but with some significant political differences. If you try working in the health care area without understanding the politics behind the issues, you are asking for some grief. At the same time, health care represents a huge segment of the economy, and it needs our help. In 1999, the United States spent $1.2 trillion on health care ($662 billion in private money; $549 billion in public money, primarily federal Medicare and Medicaid funding), according to the U.S. Health Care Financing Administration. The $1.2 trillion represents 13 percent of the U.S. gross domestic product for 1999. (The percent of GDP devoted to health care in the United States has been dropping recently due in part to strong GDP in the past eight years.) In more personal terms, the per-capita annual expense for 1999 equates to $4,358.

Typically, it takes two to three years to collect reliable information on health care spending. The system is large and not well integrated, and reporting is slow. Hence, the figures for 1999 are the most recent real numbers. However, according to HCFA projections, the United States spent $1.4 trillion in 2001, which equals 13.4 percent of GDP or $5,043 per person. The HCFA’s projection for 2008 is $2.3 trillion or 15.5 percent of the GDP.

Corresponding figures for Canada indicate that Canadian health expenditures totalled $56 billion in 1999, and accounted for 9.2 percent of the GDP or $1,828 per ca pita. The figures are from the Canadian Institute for Health Information, which estimates that 2001 expenditures rose to $64.2 billion, or 9.4 percent of the GDP and $2,066 per capita. (Note: All monetary figures are expressed in U.S. dollars and were reported as of December 2001 on the HCFA’s and CIMI’s respective Web sites. The conversion rate used: $1US = $0.62633CN.)

Most people are astounded by the per-capita expenditures. Individual health care costs are generally much lower than that, perhaps a few hundred dollars per year. In fact, the elderly consume the bulk of health care funds during the last few years of their lives. Many people live these days live beyond 90 years of age and remain in reasonably good health, but they often consume more than the average person in medications per week by the time they reach this age. This is frightening news for a baby-boomer population both in the U.K and the U.S that will be retiring during the next 20 years just as their health care demands are escalating.

Figure 1 provides a breakdown of U.S. health care funds in 1999. Public expenditures accounted for 45.3 percent of total spending; the remaining 54.7 percent came from private sources. Figure 2 shows where money spent on health care went in 1999. Other spending includes dental, other professional, non-prescription drugs, home care, research, public health and construction.

Figure 1: Source of U.S. health care funds in 1999.

Figure 2: Where the U.S. health care funds were spent in 1999.

The United States spends more than double per person on health care compared to Canada, yet several scholars claim that Canadians, on average, enjoy better health. Canadians live about three years longer on average than Americans, they have more surgical procedures per capita, but they also have to wait for non-urgent service.

Of course, averages are deceptive. There is no doubt that Americans have the best health care system in the world—for those who can afford it.  I only mention this to suggest that, perhaps, Americans are not getting full value for their investment. This is not just an American problem. The people working in the system are generally dedicated to providing the best possible service. The problem is, the workforce and, more importantly, management, do not have the training or knowledge to make the best use of the available resources. In my opinion, no private industry would survive with the level of waste and inefficiency commonly seen in health care.

What about the rest of the world? The OECD reports on health expenditures for most developed countries. Table 1 provides a few of the figures for 1998. The United States spends far more on health care as a percentage of the GDP than any other developed country. Most developed countries, with the exception of Mexico on the low end and the United States on the high end, spend between 6.7 percent and 10.6 percent of the GDP on health care.

Table 1.

OECD Health Expenditures as Percentage of GDP for 1998
United States 13.6
Germany 10.6
Switzerland 10.4
Canada 9.5
France 9.5
Australia 8.5
Sweden 8.4
Greece 8.3
Japan 7.6
UK 6.7
Mexico* 4.7
*Mexico GDP is for 1997

It is universally acknowledged that there is no good way to determine the effectiveness of any health care program or treatment, since we don’t have good tools to measure “health,” and no information systems to record a person’s “health” over time, even if we could measure it. Designing linear programming to maximise “health” is pretty difficult when you don’t know what “health” means and have no way to determine the impact that expenditures have on “health.”

In other words, we know what the constraints are, but the objective function is still a mystery.(Doug Samuelson’s 1995) There are also serious research funding problems. Health care management research is not usually viewed as a core research area by engineering, medical or social science funding agencies.
Health care is a business like no other. It has multiple decision-makers with conflicting goals and objectives. Glouberman and Mintzberg [2001] have produced a clever framework to help explain why the health care system in all countries has proven to be virtually impossible to manage effectively.

First, consider the acute care hospital. Most hospitals in the United States, and virtually all hospitals in Canada, are not-for-profit, independent corporations. Glouberman and Mintzberg identify four different management groups (called four worlds) within the hospital as illustrated in Figure 3. Doctors and nurses manage “down” into the clinical operations because of their focus on patient care. Managers and trustees manage “up”, toward those who control or fund the institution. Moreover, employees (managers and nurses) practise some management “in” the institution, while doctors and trustees manage “out” of the hospital, since they are technically not employees and are thus independent of its formal authority.

Figure 3: Four worlds of the general hospital.

Figure 4: Four worlds of society at large.

According to this scheme, the bottom left quadrant is the world of cure, which is characterised by short, intensive and (mostly) non-personal medical interventions. North American doctors typically do not work for the hospitals. They are private entrepreneurs who have admission privileges at a hospital. (Some doctors are salaried hospital employees, but the majority of doctors work on a fee-for-service basis.) To maximise their income, doctors make brief appearances when the patient needs a cure and intervention (treatment), and then they move on.

The bottom right quadrant represents the world of care. This is the world represented by nurses, the providers who work directly for the hospital on salary and typically account for the largest component of its operating budget. They work in their own internal management hierarchy and have a unique relationship with patients. They are the only providers who actually touch patients.

Managers represent control. They are employed by the hospital and are normally removed from direct involvement in clinical operations, but are responsible for its control. Since managers lack the knowledge to understand clinical operations, they control what they can, i.e., costs.

The world of community, formally represented by the trustees or Board of Directors, is often composed of community members. The Board is responsible for setting hospital policy and appointing senior managers. However, they are the people who generally know the least about health care or its delivery, since they neither work for the institution nor do they provide clinical services.

In this fractured environment, doctors and nurses form what is called the “Clinical Coalition.” They form a coalition, based on the objective of delivering patient care, usually as a common front as patient advocates against managers and trustees. The nurses and managers make up the “Insider Coalition,” since they are the ones who actually work for the hospital and have concerns about the day-to-day operation of the organisation. They form a coalition against the outsiders (doctors and trustees) to preserve their hospital and their jobs. The managers and the Board of Directors form the “Containment Coalition.” They form a coalition on the basis of strong concerns about budget constraints. Finally, the Board and the doctors make up the “Status Coalition.” They share the prestige of being independent of the institution, and yet, they are at the top of its pecking order.

Unlike any private sector business, no one is really in charge of a hospital. Managers make resource allocation decisions, but doctors decide what the hospital does with those resources. A horizontal cleavage divides the clinical workers from the containment sector, and there is little cooperation between the two. Glouberman and Mintzberg have found that both doctors and managers tend to turn to the nurses for coordination and conflict resolution. Nurses become the hospital managers. This puts them in an awkward situation, since they do not have the authority to truly manage.

The same template can be applied to the overall social health system. In this case, the acute care hospital itself represents cure. Patients go to the hospital when they are really sick, and then get quickly discharged back to the community (home care, family doctor) where they receive basic long-term care. The hospital is somewhat beyond direct public control and thus “out” of the day-to-day community. Government agencies or insurance companies provide control. They are removed from direct care, yet they are responsible for funding. Finally, politicians and advocacy groups, like trustees in the hospital model, try to influence the system without being directly involved in funding or care.

Glouberman and Mintzberg argue that these four worlds, in both models, operate independently and without much cooperation. The frequent reorganisations common in health care at both the hospital and system level usually just affect one of the worlds. Unless these worlds become integrated, costs will continue to spiral out of control.

In health care situations, we typically want to minimise cost or maximise quality or, more likely, some combination of these two. On the surface, this sounds pretty straightforward, but if we look closer, even the definition of these terms—”cost” and “quality”—is open to interpretation. Cost to whom? The hospital? The government? The patient? The doctor? Whose cost are we minimising? Do we want to minimise the cost per hospital visit (minimise care and length of stay) or do we want to minimise the overall annual cost? In the latter case, we should spend more on prevention, as more tests now may mean avoiding a much more expensive episode later. When we minimise hospital costs, we often simply transfer those costs from the hospital to the family who must provide support or hire home-care nursing. In other cases, we want government to spend money on prevention programs that save social costs later, but these costs may not translate into government savings, which makes them difficult to cost justify.

But how do we maximise quality? Quality—real patient outcomes—is hard to measure. Once you leave the hospital, you’re gone. Most hospitals have no information on what happened to you once you walk out the door, unless outcomes are so bad that you are re-admitted within a short period due to complications.

Doctors are the gatekeepers, but they don’t care about cost. They are required to be the patient’s advocate. Personally, I don’t have the expertise to know what I need as a patient. I rely on my doctor to decide if I need more tests. And if it were me, I would say do the tests, the heck with the cost, especially if I don’t have to pay for them.

Maximising quality is also quite ambiguous. Do you maximise the quality of the outcome for a particular episode of care? Or do you try to maximise the patient’s quality of life?

One particular measure receiving a lot of attention is the concept of “Quality Adjusted Life Years.” The idea behind Quality Adjusted Life Years is akin to asking yourself when faced with a fatal illness and an option of a medical intervention, “Would I rather live for two additional years in a hospital bed as compared to living normally for three months and then dying?” Sometimes surgery can have negative impact on quality of life. How can people pick between complex and risky options like these?

Tom Koch [1999, 2001] published research related to the dilemma of a national organ transplant service in the United States. The stated goals of such a system are “equality, efficiency and optimality,” goals enshrined in law. But how do we determine what is equitable? For example, hearts and lungs will survive at most four to five hours outside a living host, so one could argue that proximity is optimal. But this policy creates inequities across the country due to imbalances in supply and demand, and in the location of major transplant centres. Therefore, equality, efficiency and optimality become conflicting objectives. There are more than 50,000 U.S. citizens waiting for a suitable donor. Who gets the organ? The person who has been waiting longest or the person who is the best match? Should younger recipients have priority since they will have more years of potential value? What is fair? Should the population of Africa be guaranteed the same service as the U.S and the UK? Keep in mind that these decisions are also highly influenced by politics.

It seems that one of the major causes of inefficiency in the health care system can be referred to as “localised expertise.” People working in the health care system are very knowledgeable about their own area but have relatively little understanding of what goes on in the next department. Doctors and nurses in the Emergency Department or in operating rooms do not really understand or sympathise with the problems faced by ward staff. People in hospitals have little appreciation for issues in long-term and home care. Occasionally, there are issues about “my work is more important than yours” or “my problems are bigger than yours.” More often, it is simply too difficult for people to get a real handle on the whole “system.” This is where OR professionals can play an important role.

It is appropriate now to illustrate a few of the reasons why health care problems are unique and to demonstrate the wide variety of applications. For a more thorough review, it is useful to consider the research of Pierskalla and Brailer [1994] or Jun, Jacobson and Swisher [1999]. Obviously, one of the major issues in health care is waiting times (waiting for surgery, wait lists for transplants, location of emergency services, etc.), and most health care queueing problems are too complex to be analysed theoretically. Therefore, simulation is a popular alternative. Simulation also helps people visualise the impact of local decisions on the whole system. One problem with using simulation in health care is the difficulty of collecting data. You cannot really follow patients around with a stopwatch. The health care environment also frequently involves multi-tasking; doctors and nurses look after several patients at once, and it is challenging to determine how to model their time.

Linear Programming has been used in a number of applications including staff scheduling, budget allocation and case mix management among others. Case mix is similar to the basic product mix example problem in every introductory LP text, except that it contains a few twists. The problem lies in deciding which set of procedures a hospital should perform to meet performance targets and stay within budget. One major obstacle in this process is that the hospital administration, unlike private industry, cannot dictate the case mix. As discussed earlier in the four worlds of health care, doctors are the gatekeepers. They decide what the hospital does, and they are generally more concerned about patient care than they are about the hospital’s case mix issues.

Hospitals are usually divided into a “medical” and a “surgical” side. There is not much that we can do about the medical side; patients typically arrive with a variety of symptoms and must be treated promptly. However, the surgical side is primarily concerned with “elective” procedures. They are not elective in the sense that the patient has a choice about having them performed, but the patient and doctor will schedule them for some future date. These people are on the waiting list. Since we cannot dictate the “optimal” case mix, we simply determine whether or not a given hospital policy is feasible and point the hospital in the direction of feasibility.

There have been dozens of NHS papers published in the UK health care sector and else where in Europe. For example, Kooreman [1994] used DEA to compare the 320 nursing homes in the Netherlands. The homes were funded based on the number of beds and days of treatment. The author noted that it is hard to measure real health outcomes like “improved health status” or “improved quality of life,” so he just used the output “number of patients treated” divided into four treatment groups.

The lack of reliable outcome measures is a common problem in health care. Most hospitals do not track the patient’s health status after they leave the hospital unless they are re-admitted due to complications. Lack of data is even more pronounced in home care and nursing homes where data is typically manual and not standardised.

There have also been several papers published describing integer-programming applications, primarily for facility location and staff (nurse and physician) scheduling problems. A number of these describe locating emergency medical services, and ambulance location in particular. For example, Repede & Bernardo [1994] developed a system for locating ambulances in Louisville, Ky.

According to U.K standards, “95 percent of all (urban) ambulance calls should be served within 10 minutes.” In their model, Repede and Bernardo assumed that the fleet size and the demand pattern changes over time. They provide a decision-support tool to help EMS planners relocate ambulances to maximise the total expected demand that can be served within 10 minutes. One of the distinguishing features of this type of facility location problem is that, once an ambulance is dispatched, it is no longer available to cover calls, and it could be out of service for an hour or more. Therefore, the fleet size is constantly shifting. This is a tricky stochastic problem that requires more attention.

Another application involves the optimisation of radiation beams that travel through the body to treat cancer patients. These beams can travel through the body at a variety of directions and intensities. The objective is to maximise the radiation on the tumour and minimise the impact on healthy tissue, especially vital organs. Today, these calculations are typically done by hand. Linear and mixed integer models have been developed to improve patient treatment. (Holder, 2003). However, since these are considered medical interventions (they would actually be used on patients) the processes must be approved by the medical regulatory agencies.

Much of OR modelling in AIDS research is systems dynamics models. To quote from Kahn, Brandeau and Dunn-Mortimer [1998] in their introduction to a recent special issue of Interfaces devoted to AIDS modelling: “The AIDS epidemic is a serious, growing public health problem worldwide, but resources for treating HIV-infected patients and for combating the spread of the virus are limited. Governments, public-health agencies and health-care providers must determine how best to allocate scarce resources for HIV treatment and prevention among different programs and populations.

OR-based models have influenced—and can influence—AIDS policy decisions. Mathematical modelling has had an effect on AIDS policy in a number of areas, including estimating HIV prevalence and incidence in the U.K, understanding the pathophysiology of HIV, evaluating costs and benefits of HIV-screening programs, evaluating the effects of needle-exchange programs, and determining policies for HIV/AIDS care in U.K major cities. Further work is needed to model a range of programs using comparable methods, to model overall epidemic control strategy, and to improve the usefulness of OR-based models for policy-making.”

There has been some work on managing hospital waiting lists and allocating beds in a hospital to various services. One interesting aspect of health care waiting lists, particularly for home care and long-term care, is the dynamic nature of the problem: as the queue increases, the reneging rate increases. People on the list either look elsewhere for service, become more seriously ill and go to a hospital, or perhaps die waiting.

Quality management is popular in the U.K NHS system and has been for many years previously due to the fact that hospitals started to quality assurance using tools like statistical process control to monitor (immediate) outcomes. Manufacturing was at this stage in 1975, and other service industries were there in the 1980s. Other components of the health care industry (home care, nursing homes) are further behind. In contrast, the pharmaceutical industry was probably more in line with other manufacturing sectors.

One of the reasons for the delayed reaction in health care has been reluctance on the part of the medical community to acknowledge and report errors and problems. Physicians were often reluctant to even have their results tracked and bench marked. There is a culture of silence in health care; they do not want to admit that mistakes can happen. The Harvard Medical Practise Study (Brennan,1991) reviewed more than 30,000 hospital records in the U.S and the U.K and found injuries from care itself (“adverse events”) to occur in 3.7 percent of hospital admissions, more than half of which were preventable and 13.6 percent of which led to death.

If these figures can be extrapolated to the 33.6 million admissions to American hospitals in particular in 1997, then more than 98,000 Americans die each year as a result of preventable errors in their hospital care (Kohn et al, 2000). By comparison, 97,860 people died in 1999 due to all unintentional accidents, which would make medical error the fifth highest cause of death (NHS Department of Health. National Centre for Health Statistics, 1999). The analysis and prevention of adverse medical events has become a major focus of attention. In many situations, redesigning the processes can prevent errors.

References

  1. Brennan, T.A., Leape, L.L., Laird, N.M., Hebert, L., Localio, A.R., Lawthers, A.G., et al, “Incidence of adverse events and negligence in hospitalised patients: results of the Harvard Medical Practise Study I,” New England Journal of Medicine, 1991, Vol. 324: pp. 370-376
  2. Glouberman, S., and Mintzberg, H., “Managing the care of health and the cure of disease—part I: Differentiation,” Health Care Management Review, Winter 2001, Vol. 26, pp. 56-69.
  3. Glouberman, S., and Mintzberg, H., “Managing the care of health and the cure of disease—part II: Integration,” Health Care Management Review, Winter 2001, Vol. 26, pp. 70-84.
  4. Holder, A., “Partitioning Multiple Objective Optimal Solutions with Applications in Radiotherapy Design,” to appear in Health Care Management Science, 6, No. 1, Feb. 2003.
  5. Jun, J.B., Jacobson, S.H., and Swisher, J.R., “Applications of discrete-event simulation in health care clinics: A Survey,” Journal of the Operational Research Society, 1999, pp. 109-123.
  6. Kahn, J.G., Brandeau, M.L. and Dunn-Mortimer, J., “OR Modelling and AIDS Policy: From Theory to Practise,” Interfaces, 1998, Vol. 28, pp. 3-22.
  7. Koch, T., “The Transplant Dilemma,” OR/MS Today, 1999, Vol. 26, No. 1, pp. 22-29.
  8. Koch, T., “The Art of the Science,” OR/MS Today, 2001, Vol. 28, No. 5, pp. 28-32.
  9. Kohn, L.T., Corrigan, J.M. and Donaldson, M.S. (editors), “To err is human: building a safer health system,” Committee on Quality of Health Care in America, Institute of Medicine, National Academy Press, Washington, D.C., 2000.
  10. Kooreman, P., “Nursing Home Care in The Netherlands: a Nonparametric Efficiency Analysis,” Journal of Health Economics, 1994, Vol. 13, pp. 301-316.
  11. Pierskalla, W.P., and Brailer, D., “Applications of Operations Research in Health Care Delivery,” “Beyond the Profit Motive: Public Sector Applications and Methodology, Handbooks in OR&MS,” 1994, Vol. 6, (S. Pollock, A. Barnett and M. Rothkopf, eds.), North Holland, pp. 469-505.
  12. Repede, J.F., and Bernardo, J.J., “Developing and Validating a Decision Support System for Locating Emergency Medical Vehicles in Louisville, Kentucky,” European Journal of Operational Research, 1994, Vol. 75, pp. 567-581.
  1. Samuelson, D., “Diagnosing the Real Health Care Villain,” OR/MS Today, 1995, Vol. 22, No. 1, pp. 26-31.

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