"Dynamic policies that adjust dialysis based on the kind and number of patients a center can handle have the potential to improve life expectancy, in some cases by as much as 20%," says Stefanos A. Zenios of Stanford University.
Another way of applying these models would allow centers to reduce the financial burden of operation by as much as 10% while maintaining a constant level of care.
The study, "Managing the Delivery of Dialysis Therapy: A Multiclass Fluid Model Analysis," is by Professor Zenios, Graduate School of Business, Stanford University, and Prashant C. Fuloria, Closed Loop Solutions of Mountain View, California. It appears in the recently issued Vol. 46, No. 10 of Management Science, an INFORMS publication.
Rising Health Care Costs
The annual death rate for American patients with End Stage Renal Disease (ESRD) is 20 - 25%, double the rate in European countries. Experts suggest two explanations for the higher mortality rate: one, the larger number of critically ill patients accepted for treatment in the US compared to Europe; and, two, an American dialysis reimbursement rate that is 50% lower.
The reimbursement rate reflects the high cost of dialysis therapy. In the U.S., Medicare’s ESRD program covers 90% of ESRD-related expenses. The program, which began in 1973 covering 10,000 patients, now covers more than 240,000 patients and has an annual budget of over $9 billion. There are pressures to contain the rising expense.
The authors’ model addresses the need of two groups: physicians who prescribe dialysis for patients and kidney dialysis centers, which are strongly affected by budget pressures.
The model is a decision support tool that supplements physicians’ current resources by helping them zero in on the precise treatment for a patient by quantifying needs of categories of patients, including high risk patients like diabetics. Currently, explains Prof. Zenios, physicians are often forced to determine dosages based on experience and patient history.
The model also helps a center cope with two constraints: the limited number of dialysis units in a facility, which forces it to curtail patient time on a kidney dialysis machine or turn away new patients; and high costs, particularly the cost of a component known as a dialyzer.
During dialysis, the blood of a patient is withdrawn and passed through a dialyzer - a device with a semi-permeable membrane - allowing a significant reduction in the concentration of blood toxins. On average, say the authors, Medicare reimburses centers $126 per dialysis session. Because a dialyzer costs between $30 and $70, a center typically retains a patient’s dialyzer until that person’s next dialysis session. Dialyzers are typically reused as many as 20 times.
The authors note, however, that while a patient’s treatment time on a dialysis machine remains fixed, reuse of the patient’s dialyzer reduces its efficiency, thus gradually cutting the dosage every visit until the dialyzer is replaced.
Their model helps centers appropriately adjust patients’ time on dialysis machines and the number of times that dialyzers are reused, thus producing optimal use of a center’s scarce resources while improving life expectancy.
Accounting for Patient’s Varying Needs
To determine the optimal dose for different patients, the authors divide those receiving dialysis into three classes of diabetics, and three of non-diabetics. The authors also factor overall for the relative efficiencies of low-, medium-, and high capacity dialysis centers.
The study looks at several alternate policies that employ an operations research technique known as optimization. The first, called a dynamic flexible policy, uses patient data to deliver the dialysis dose that maximizes patient life expectancy without restriction on allocation of dose, time, and budget. Two related optional policies reflect regulatory and ethical constraints.
The researchers found that in moderate or low capacity dialysis centers, the three optimized policies simultaneously increase the Urea Reduction Rate (URR) – a key indicator of dialysis efficacy – and decrease the amount of time needed on dialysis in comparison with the model currently employed. The relative improvement over the current policy is greatest at high capacity dialysis centers, followed by low and then medium capacity centers.
The authors found that system capacity has a significant impact on the overall system performance. In high capacity dialysis centers, the three optimized policies achieve an average increase in overall life expectancy of 4.5% relative to a medium capacity center and 20.6% relative to a low capacity center.
The researchers employed data from the United States Renal Data System and the Health Care Financing Administration to design and test their operations research models.
The Institute for Operations Research and the Management Sciences (INFORMS®) is an international scientific society with over 10,000 members, including Nobel Prize laureates, dedicated to applying scientific methods to help improve decision-making, management, and operations. Members of INFORMS work in business, government, and academia. They are represented in fields as diverse as airlines, health care, law enforcement, the military, the stock market, and telecommunications. The INFORMS website is at http://www.informs.org.