Looking back on this semester, I can safely say that this class has pleasantly surprised me. To be honest, I was not looking forward to taking a sociology class because of the daunting amount of reading and writing, something I thought I’d be done with after freshman year writing classes. It’s hard for me to think abstractly about themes and ideas and I would much rather write a scientific article than an essay. However, the topics discussed in this class have been so fascinating to me that I actually enjoy reading and writing about them.
This class especially has given me a critical eye on medical school. I have always thought of hospitals as such magnificent places where some of the smartest people in the world work. Medical school has always seemed like the place to be, and it has been my dream to attend medical school and then one day work in a hospital. Because of the competitiveness and high standards of medical school applications, I have always had the utmost respect for medical students. However, after learning about the hidden curriculum and the socialized dominance of medical students and professionals, I see them all in a new light. I wonder if being a doctor will really allow me to help individuals as much as other professions do. I have definitely reconsidered my decision to attend medical school. There are so many other medical professions and settings that would be just as rewarding as being a hospital doctor. I have a friend who is a nursing major at Northeastern and she is only a sophomore but has already taken classes that are directly applicable to the hospital setting. If we were both thrown into a medical situation she would know exactly what to do in the moment, and the most I could offer would be some basic biology facts. It really is interesting to compare different health professions, especially how they are socialized and taught in school. This class has guided me to rethink my career choices by taking a critical approach to the medical world.
In the article “Fighting to Honor a Father’s Last Wish”, the author paints nursing homes in a negative light. The nursing homes discussed in the article seemed to be charging excess money to Medicare and using treatments that were unnecessary or harmful. Also, they increase the risk of infection. If nursing homes are really this terrible, why are so many patients discharged from hospitals into nursing homes? Especially because nursing homes go against many people’s wishes for how they’d like to spend their last living years. The patient in the article claimed to have been forced into nursing homes after his hospital stays even though his daughter was trying to arrange 24-hour care. Why weren’t they successful in avoiding another nursing home? Why are they so supported by hospitals if they really are so dangerous and expensive?
In the article “What if your self-driving car decides one death is better than two—and that one is you?” author Sarah Kaplan discusses the ethical dilemma that faces self-driving car manufactures. It is becoming an increasing reality that self-driving cars will hit the roads in the near future because of the research suggesting that self-driving cars will increase road safety. A recent study aimed to answer the ethical questions surrounding self-driving cars, such as what the car should be programmed do if it has to choose between crashing into a crowd of people and saving the driver, or swerving to avoid the crowd and kill the passenger instead. Participants took a survey and results supported the idea of utilitarianism—reducing the amount of lives lost to as little as possible. However, these same study participants said they would not buy a self-driving car that would potentially sacrifice its passengers, even if it meant saving a crowd of people.
The ethical dilemma of death in self-driving cars can be used as a metaphor for the ethical dilemma of death in medicine. The cars can be equated with doctors because the power to save people or let them die lies in their hands. Doctors can be “programmed” to make a decision a certain way by the desires of the patient or the patient’s family like a self-driving car can be programmed by car manufacturers to save certain people over others. In a medical setting for terminally ill patients, the decision to stop treatment rather than continue on with treatment can be one of the hardest decisions to make. For example, giving up on a brain-dead patient means the loss of one person, but his or her organs can be donated to patients in need of transplants and therefore save the lives of many. This decision is similar to the self-driving car decision of whether or not to sacrifice one life in order to save many. The study discussed in the article showed that people were more in favor of a self-driving car that had a programmed algorithm favoring utilitarianism, but would not buy the car knowing that there’s a chance their life could be sacrificed. In the medical setting, it’s easy to be objective and look past one death if it means saving the lives of many, but once a patient and their family is actually in that situation it becomes more difficult to be objective and everything is done to hold on to that one life.
A significant difference between doctors and self-driving cars is that doctors have the ability to make personal decisions about a patient’s treatment, even if they are technically just supposed to follow the wishes of the patient and the patient’s family. Doctors have certain strategies that allow them to increase their decision-making power. In the case of self-driving cars, the majority of people support the act of sacrificing a passenger in order to save 10 pedestrians. But what if that passenger was the president of the United States and the pedestrians were civilians with low social statuses? Can the car’s algorithm be changed in order for certain people to be given a higher value of life? According to Timmermans, in the medical setting, minorities, old people, and people with a low social status, are given less worth in terms of life or death. Doctors will work harder to save young people with a higher SES because they believe that their life at that quality is something worth saving. In the case of self-driving car manufacturing, it is not being talked about yet whether there needs to be an adjusted algorithm for people of higher importance.
The bioethics movement has different impacts in different settings, such as in the medical setting or in the self-driving car manufacturing setting, but there are many parallels. In the medical setting, doctors usually make life-or-death decisions objectively, but patients are subjective. This same pattern is seen by self-driving car purchasers. People can understand objectively the consequences of an algorithm that does not favor utilitarianism, but once they picture themselves as the passenger in the car, their opinion changes when the decision becomes subjective. Ethical dilemmas about death rarely have a clear solution.
Earlier this week on Monday, the World Health Organization released a statement that consuming red or processed meat, such as bacon or hot dogs, increases the risk of colorectal cancer. In the article “California considers adding meat to cancer-alert list”, author Tom Polansek discusses California’s potential decision to put red and processed meats on a cancer-alert list, called the Proposition 65 list in California. According to Polansek, California usually leads initiatives that reflect the opinions of the consumer. The meat industry is the main opposition to including red and processed meats on the Proposition 65 list, because consumption of red meat would presumably decline if it is added to the cancer-alert list. The article mentions the idea of putting a warning label on processed meat packages, similar to labels on tobacco products, to increase the prominence of the information for consumers. However, these actions are drastic and preemptive because they are an example of the radical social reactions that occur when the word “cancer” is mentioned. Continue reading “The Power of Cancer’s Stigma”
In the Frontline video “The Medicated Child”, children are starting to be diagnosed with mental disorders at increasingly younger ages. Previously, bipolar disorder was considered only an adult disorder. There has been so much research on bipolar, but only on adults. Drugs that work in treating bipolar disorder for adults might have a harmful effect on children, especially because these are drugs that target the brain and children’s brains aren’t fully developed until age 25. This reminded me of the topic of gender we discussed when Professor Rieker taught the class. She said that until very recently, only men were represented in clinical research. Just because a drug works for men, doesn’t mean it’s going to work on women. The same principle is applicable for adults versus children. Can mental disorders in children even be diagnosed if there has been no research on how mental disorders manifest in children? How are doctors sure that a child has bipolar disorder if they have only seen and treated bipolar adults?
In the article “Understanding Gender and Health”, the author compares the health of men and women. Women on average live longer than men, but they also have different social roles and community actions than men. Rieker looks at the differing social factors between men and women to try and explain the difference in health, without just considering biological reasons. In lecture we have been discussing health differences for people of different races and ethnicities. There is a shocking difference in life expectancy, infant mortality rate, and disease between genders and races. We learned in our last unit that health insurance plans are different for men and women for both cost and coverage. However, health insurance is not tailored to a certain race like it is to a certain gender. Looking at the statistical differences between African Americans and Caucasians for example, African Americans seem to have a higher likelihood of developing a serious disease than Caucasians do because of social factors. How do we create a health care system that is applicable to everyone and not just the statistical averages across all races and genders? Also, I’m curious if private insurance companies (before the ACA prohibited cherrypicking) were able to deny people from purchasing insurance if they were of a certain socioeconomic status or race. Knowing how selective insurance companies have been, it seems as if they might have justified not covering a certain person because of his or her race or socioeconomic status based on the statistical evidence.
In the article “Social Conditions as Fundamental Causes of Health Inequalities: Theory, Evidence, and Policy Implications” by Phelan et al., the authors insist that the connection between socioeconomic resources and preventative health care needs to be broken. The article outlines ways to fix this inequality between health and mortality of people with different socioeconomic statuses. However, this inequality does not seem like a shocking piece of information to me. We live in a capitalist society, which means that those with the money can buy themselves the best and most expensive health care and those less fortunate are unable to receive all the benefits a person with a higher SES might receive. The author tries to fix this issue, however, with our strongly capitalist society, is this inequality in health and mortality even possible to fix? It makes sense that people with a low SES have the most dangerous jobs, such as meat processing jobs discussed in Weitz chapter 3. This pattern of high mortality rates for people with a low SES seems perfectly logical in the way our society is organized. I agree with the authors of this article that in a perfect world it would be great to fix this inequality so everyone is at an equal risk of death and illness, however, is this an issue worth pursuing? The author even states that this pattern persisted when radical changes in the disease and risk factors were implemented. Will this association between low SES and high disease and mortality rates ever be eradicated?
A few weeks ago when this class first began, I was overwhelmed by all the topics mentioned on the first day. It was just an introduction to the course, but I was frantically taking down notes of all the terms and phrases I had never heard before. I remember looking back through my notes after that class and having no idea what even the definitions to these terms and phrases meant. Concerned that I was behind already because I didn’t come into this course with any previous knowledge of health insurance and healthcare systems, I read ahead and tried to wrap my brain around what this course was even about. Continue reading “Process Reflection 1”
In the article “Costs derail Vermont’s dream of a single-payer health plan”, I was disappointed to learn that there is no hope in Vermont for a single-payer system. When first hearing that Vermont had enacted a bill to create the nation’s first single-payer system, I was hopeful that if this was successful, then it would be a gateway into a whole nation of single-payer systems. The main reason the article gave for why it was unsuccessful was that Vermont recovered from the last recession slower than expected and tax revenues had not grown. Would another state without preexisting economic problems similar to Vermont’s be successful at creating their own single-payer system? Or does private insurance have too much of a grip on health insurance? Is there no hope for the US in establishing a European-style single-payer system? Or will it just take baby steps? For example the Affordable Care Act expanded Medicaid and shifted the hybrid of private insurance and government programs more towards government programs. Was this a step forward towards the ultimate goal in making US health insurance a single-payer system? Or was it a step back because the ACA mandated everyone uninsured to purchase insurance from private insurance? Is our health insurance system too far gone, or do we have some hope for a better system based on the ACA and Vermont’s Green Mountain Care idea?
In the article “Poverty may increase odds of repeat hospitalizations” published in September 2015, author Lisa Rapaport brings up the issue of high patient readmission rates to hospitals. The Medicare system penalizes hospitals with higher than expected readmission rates by decreasing inpatient payments to hospitals by 3%. Continue reading “Justification of Penalties for High Hospital Readmission Rates”