Date: April 18th
Location: Wasserman Cinematheque
Join the class of Critical Perspectives on Health for the screening and presentation of the film, The Life Equation which discusses the topic of global health in the era of big data. The event will include a viewing of the film, and a Q and A session with the Emmy-award winning director Rob Tinworth. Stop by this great event to learn more about this amazing film.
And here’s a question for people who saw the film: what do think is the greatest potential benefit of big data for global health? What is the greatest danger of using big data to allocate resources?
2 Replies to “The Life Equation Film Screening”
“The Life Equation” really re-shapes my opinion concerning the global health. The documentary focuses on the story of one individual, Crecencia, and illustrates the idea of utilizing big data to allocate the resources effectively. One ongoing debate is the choice between maximize the impact and put the health of individual in the first place. From my perspective, the greatest potential benefit of big data is that it can possibly offer “the opportunity to serve more people”. The health organization can decide where the dollars should go and how to spend the dollars more efficiently based on the big data. In shorts, the algorithm can help us look at the “big picture” of global health. However, it’s almost impossible for the algorithm to take the health of each individual into consideration, which leads to the potential danger of big data: overlook the different situations of different people. It may cause devastating consequence because one person could possibly die due to the shortage of, say, 5k dollars. In the documentary, Crecencia gained four years with the donation she received, 8140 dollars. However, as the documentary notes: “for the cost of Crencencia’s radiotherapy you could protect 1,000 people from malaria for three years”. Nobody can make the decision easily because no one has the right to say that we should sacrifice one’s life to protect other 1,000 people. There is no way that big data can make such decision for us. After all, there is a limit on how much money we have. It’s hard to meet the needs of all people in the world. Algorithm can offer us the most reasonable way to spend the money, but it may also threaten others’ lives.
This film shows the life of a woman named Crecencia and how she seeks treatment for her cervical cancer. It also showed the life of two doctors who migrated to a rural area in Nepal, and their goal was to provide better healthcare access to people in that community. From this film, we see the challenges that physicians and healthcare workers face when balancing work and their personal lives. The greatest potential of big data for global health is that it allows healthcare practitioners, non-governmental organizations, donors, philanthropists and others to successfully and efficiently assess the quality of healthcare, interventions, treatments, and the allocation of time, energy, and resources. The greatest danger of using big data to allocate resources is that leaves little room to incorporate morality and ethics into the discourse on healthcare treatment. When we allow data sets and trends to dictate how we allocate health resources, then do we sacrifice or ignore those who are seen as outliers on a dataset? Who will attend to the diseases and populations that may come off as statistically insignificant? It may be difficult to determine where our money goes and what causes we would like to support because a person may need a treatment that could cost thousands of dollars and that same amount of money could help many more. Thus, we must ask ourselves, who deserves the funds? Public health focuses on treating the general population, while medicine focuses on treating an individual. Some illnesses are out of our control due to genetic, behavioral, and environmental factors. Some illnesses are rare and only affect seemingly isolated groups of people and populations. If these factors and determinants are not accounted for in the allocation of resources and we only consider big data health, this question must be answered: Do we save money or save lives?