Abstract
With continuing advancement in the understanding of diseases and expanding medical technologies, doctors are more equipped than ever to treat illness. However, as medical knowledge grows, the medical decision-making process becomes increasingly complicated. With greater understanding of the pathology of diseases, more personalized treatment options are available for certain diseases, depending on patient factors such as demographics or the stage of disease progression. However, humans are limited to processing only a few factors at a time when making decisions, and so it is difficult for doctors to thoroughly assess and discuss treatment options with their patients without assistance. Additionally, doctors must take into account the preferences of their patients, as many expect to be involved in the decision-making process rather than just taking suggestions from their physicians. This all adds up to a complex decision-making process that can become extremely difficult to navigate, even with the years of training that doctors receive.
For the technical project, the aim was to address the limited decision-making capacity of humans relating to the increasing complexity of radiation therapy planning for cancer. Because radiation treatment planning depends on many factors such as the size of the tumor and proximity to organs at risk (OARs), it is difficult for radiation oncologists to accurately compare and determine tradeoffs when choosing from multiple treatment plans for their patients. To assist with this process, we have contributed to the development a web-based decision support system that will be used by radiation oncologists to easily generate and compare treatment plans. The decision support system generates multiple treatment plans using artificial intelligence algorithms developed by another team, and we have developed visualization tools to allow for oncologists to easily interpret and compare those plans. The first tool that was developed was a DICOM viewer that will allow physicians to overlay dose structures for various treatment plans onto images of the target tissue. The second tool developed was a dynamic radar chart that displays the change in the volume of radiation received by OARs resulting from changes to the treatment plan. These contributions will greatly improve the ability of oncologists to compare, adjust, and determine tradeoffs of various treatment plans to ensure that they are able to create the treatment plans with the highest chances of success possible for their patients.
The aim of the STS research project portion was to investigate the relationship between technology and patient autonomy. The evolution of doctor-patient relationships from a paternalistic to a mutual participation dynamic has previously been studied and the societal factors that shaped it have been mapped out. However, it was not previously understood how technology played a role in that evolution. In order to study this relationship, advances in medical imaging and health information technologies over time were mapped out and analyzed using a framework of autonomy enhancing and inhibiting features. Through these methods, several of these technologies were found to have a positive impact on patient autonomy, especially those incorporating computers or the internet. These technologies exhibited a majority enhancing features, such as clarity of information and choices available, compared to older technologies like mammography and other static imaging modalities. While it is difficult to determine whether there is a causality between changes in technology and patient autonomy, the parallel of rising rates of reported autonomy with these technologies suggest that there may be some sort of interaction between the two. Thus, this research provided a set of criteria and historical examples for engineers to abide by when developing new medical devices to ensure that patient autonomy is not lost.
Overall, both of these projects have been successful in what they set out to accomplish, despite some adjustments to specific aims along the way. The contributions of the technical project will be used in a software that will hopefully be very helpful in improving the accuracy, efficiency, and effectiveness of radiation therapy. The results of the STS project show a correlation between changes in medical technology and patient autonomy, and provides insight into how engineers going forward can enhance doctor-patient relationships through their technological contributions. Both of these projects have exciting prospects for future directions. The software resulting from the technical portion can be updated to improve the efficiency and accuracy underlying algorithms or enhance the usability of the various tools. Meanwhile, the STS research can be expanded upon by looking at the many other categories of medical technology that were not analyzed, or by applying the same framework to technologies that will be released in the future to determine if doctor-patient relationships will continue to improve.
