Discussion questions:
Who finances most long-term care in Uniter States? Ask your colleagues and determine what others know about finance, and did their responses surprise you?
Discuss how QALY analysis benefits the young over the old? What is your opinion of comparative effectiveness research?
Responses: (Samantha)
1. “Medicaid provides coverage for 60% of all long-term care residents, making Medicaid the major program paying for long-term care,” (Milstead, 2022). Medicaid was created as a counter part to Medicare in 1965, (Milstead, 2022). Eligibility for Medicaid includes a low-income household, 65 years of age or older and blind or disabled, pregnancy or postpartum, or younger than 18 years of age, (Milstead, 2022). In my personal life, I have little experience with Medicaid, but I am aware that my aunts and uncles were not necessarily considered low-income but could not afford nursing home or long-term care. One of my aunts did have to give up her home to be able to be eligible for Medicaid and long-term care.
I discussed finance and health care with fellow co-workers they were able to identify how Medicare sets the standard for reimbursement. Some of them had utilized Medicaid in the past during pregnancy and for their children before they had their own health insurance. They voiced how they felt they had more options for their children when they were under Medicaid but had much longer wait times to get in to see a doctor. They did not understand how finance worked with commercial insurance other than that they paid into their own health insurance with every paycheck. I am not surprised with the knowledge my co-workers had regarding Medicare as we work with many Medicare patients, and we make it our business to understand what is covered and to establish Medicare standards in order to maximize reimbursement. I am also not surprised with the lack of knowledge of commercial insurance. I also did not give it much thought to how it worked as long as I was able to utilize my insurance. As future APRN’s, it is crucial for us to better understand the financial aspects of healthcare. As leaders and providers of care we need to understand what patients are eligible for and how to maximize reimbursement. Being an APRN, clinical knowledge is not where it begins and ends, we need to understand the financial and business aspects of healthcare to be successful.
2. Quality-adjusted life-years (QALYs) is used to analyze age and health conditions and cost-effectiveness for public insurance such as Medicare and Medicaid, (Milstead, 2022). Economists have created a catalog called EQ-5D Index to rely on for cost-effectiveness for each condition a person may have, (Milstead 2022). The goal of the QALY is to identify the quantity and the quality of life. “The basic idea underlying the QALY is simple: it assumes that a year of life lived in perfect health is worth 1 QALY and that a year of life lived in a state of less than this perfect health is worth less than 1,” (Prieto & Sacristan, 2003). QALY analysis benefits the young because part of the measurement is to identify the years at a specific state of health, which is more likely to increase with age which will affect whether something is covered based on cost-effectiveness.
“Comparative effectiveness research (CER) compares overall benefits of one therapeutic approach with those of another for the majority of patients,” (Milstead, 2022). CER is effective in research and deciding what the cost-effectiveness of a particular intervention for different patient populations. The issue with CER is that it does not take social determinants of health into consideration: such as patient’s financial situations. Milstead (2022) discusses the costs associated with a particular treatment for Hepatitis C. This treatment is more effective but also more expensive and would cost the state of California $6.3 billion for all patients suffering with liver disease whereas the cheaper and less effective option would cost a fraction of the cost at $25,000. Milstead (2022) continues to break down the cost for the average American household, “the average gross household income in the U.S. is about $52,00 per year.” The out-of-pocket expenses for treatment could be between $25,000 to $30,000 which is more than half of their annual household income, (Milstead, 2022).
Response: (Joanna)
1. Medicaid is by far the largest financer of long-term care services in the United States. It is a collective federal and state program for low-income people that covers the costs of medical care and some types of long-term care for people who have limited income and meet other eligibility requirements (NIH, n.d.). However, long-term care services can also be financed by private, public, and mixed out-of-pocket insurances. Furthermore, some life insurance policies can help pay for long-term care if they offer a policy that includes both life insurance and long-term care. Interestingly, reverse mortgages have also been used to free up cash to cover for long-term care expenses (NIH, n.d.).
When speaking to colleagues regarding finance, I found it surprising that most of them did not know much about this topic. Many just merely knew the fact that they have medical insurance through their employer but were unaware of their plan and what it actually covered. When I asked some colleagues who finances most long-term care services in the United States? One stated: “my current insurance will cover it.” Another stated: “their disability insurance will pay for it.” I even received some responses from colleagues that Medicare or social security would cover the expenses of long-term care services. It’s evident that there is a lack of healthcare insurance literacy. Truthfully, I too just acknowledged that our employer, through our union, provided us insurance. But, after engaging in this discussion I realize how easily we are confused about basic insurance terms and how little we know about our coverage. I think its important to increase literacy about health insurance, so we know how its protecting us.
2. The quality adjusted life year (QALY) analysis is a tool that measures the value of health outcomes by measuring how well medical treatments improve or lengthen patient lives. QALY is used in comparative effectiveness research (CER) to evaluate the cost-effectiveness of these medical treatment interventions. According to Milstead (2022), QALY is used in conjunction with EQ-5D, which is a age and health condition index. The main benefit of QALY is that it can be calculated for all types of intervention, treatment or procedure and has a high comparative potential when used to compare health benefits (Kocot, Kotarba, & Dubas-Jakobczyk, 2021). The QALY analysis “creates differences based on effectiveness of treatment” (Kocot, Kotarba, & Dubas-Jakobczyk, 2021). QALY can be varied for different ages where one QALY for when someone is young, is not the same as one QALY when one is old. For example, a successful intervention in older patients can result in smaller QALY gains than for younger people since older people with comorbidities do not “fully” improve to the level of ideal health, thus, they may respond less to the treatment intervention or have a greater risk of developing complications (Kocot, Kotarba, & Dubas-Jakobczyk, 2021). It seems QALY favors the young over the old because younger patients have better health statuses and longer life expectancy.
It is arguable that CER can give a better understanding to policymakers on healthcare interventions that work and those that don’t. I think CER can be used effectively to identify active interventions with consideration to cost-effectiveness and patient outcomes as it reduces wasteful spending by eliminating expensive and ineffective treatments. However, there are challenges. For example, since some treatment options are complex with no or little information on comparative effectiveness, physicians and policymakers are left in the dark as to how to compare treatments to another. Additionally, findings might not be generalizable to different geographic areas. Another, concern is that there may be theories that fit the same data.
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