Cancer research is one of the most heavily invested areas in medical research, with the United States spending more than $5 billion annually to discover and develop new therapies and treatment options. Given that more than 1.7 million people in the United States are diagnosed with cancer each year, this research is critical for prolonging patient survival and improving patients’ quality of life.
While an important part of cancer research relies on patient data, much of the available data is limited in quantity and scope. Currently, such information is mainly obtained from laboratory or clinical trials, limiting analysis to small groups of patients, which greatly affects the results obtained. Additionally, these controlled environments may produce aggregated results and data that may not reflect real-life situations.
Another consideration is that most cancer treatment research focuses on the disease, not individual patients. When the primary goal is to shrink tumors and kill cancer cells, research and data collected often favor the clinician’s perspective, while certain key insights provided by patients may be missed.
For data to be truly valuable, it cannot be limited to just the patient’s clinical experience, but should also take into account every aspect of a patient’s life. This includes demographic and financial status as well as psychological, emotional and logistical factors. By integrating this comprehensive data, researchers can uncover new patterns in cancer prognosis, diagnosis, and treatment, which will greatly contribute to our broader understanding and management of the disease.
Maximize patient engagement through the community
With the rise of patient communities, we now have the ability to track a patient’s complete journey across a large population. These platforms allow patients to access information specific to their condition, track their disease course and connect with others in similar situations. While communities can collect valuable data, when members can post anonymously, they tend to be more open about the personal aspects of their journeys, which leads to richer data while preserving complete patient privacy.
To maximize the availability of these data, large populations are needed to support a broad range of demographics. To create a large user base, a high level of patient participation is necessary, which is an ongoing challenge for such forums and applications. The best way to achieve this is to ensure that the platform provides real value to users. If an app demands too much from patients, such as asking them to constantly track symptoms and fill out questionnaires, patients will rarely use them, resulting in the creation of little to no usable data.
To retain members and keep them engaged, patient communities need to provide tools and capabilities that encourage engagement from the start. This includes medically verified content, networking capabilities to connect patients, connections to medical experts, and other tools useful for patients going through stressful journeys. For example, you can provide a way to track and manage symptoms and side effects and a place to store medical files are good examples.
Practical and easy-to-use tools will encourage patients to use the web multiple times a day. This will ultimately help generate a wealth of rich but anonymized data that extends beyond treatment and provides insight into the complete patient journey.
Track specific patient input
Artificial intelligence and machine learning allow researchers to evaluate large amounts of data and generate meaningful insights more efficiently than ever before. Taken together, these techniques can detect subjective information provided by patients, such as their concerns and emotions—information that is rarely recorded in electronic medical records.
Algorithms also examine data points, such as disease symptoms, drug efficacy, and side effects, while also looking for other factors that may affect a patient’s personal experience. For example, are they practicing yoga, using cannabis, or taking supplements? What is the patient’s eating habits? How much sleep do they usually get each night?
AI and ML essentially connect the dots between observed data points to discover connections or patterns. By looking at a series of events or causal relationships, we can gain insight into many overlooked aspects of cancer care. These chains of events are actually patient journeys, and important discoveries can be made by observing the journeys of hundreds of thousands of patients. For example, if certain symptoms become apparent in many patients taking a particular new drug, previously undetected side effects may be discovered.
Research shows that cancer is not always or only caused by genetic or familial defects, but can also be caused by the accumulation of different triggers and environmental changes. The ability to identify these factors and track health determinants through the patient community provides an incredible set of preventive tools that can predict cancer risk and what needs to be done to minimize that risk.
Furthermore, due to the large number of different malignancies, stages, and subgroups in oncology, there is a large scope for observation, including inherent changes such as mutations and many other unknown factors.
Shaping the future of cancer care
Data that is de-identified, aggregated, and analyzed from the patient community with the help of AI and ML has matured to present opportunities for researchers. The wealth of available information highlights many aspects of the unique patient journey, providing new, real-world insights that have been largely hidden until now.
Through the analysis of this data, researchers can gain new perspectives on various important aspects. This includes cost of illness, length of treatment plan, benefits of combining alternative therapies with medical regimens, management of side effects, and more.
These insights already provide the basis for studying the impact of Covid-19 on cancer patients. Such data also inform research into how a breast cancer diagnosis affects the sex lives of patients and their partners. Taken together, these insights are infusing the industry with new understanding that can significantly improve care and patient quality of life.
Of course, with so much data, we can expect many additional studies to be done. In addition, through these insights, oncology researchers can also foresee the possibility of cancer recurrence, which remains one of the top concerns for patients and clinicians. Researchers can also better assess survival in specific patients.
It is clear that the patient community is an indispensable tool to advance traditional cancer care and research methods. By considering the individual patient’s journey beyond treatment, we uncover hidden patterns. These insights can improve the quality of life for individual patients and populations, and ultimately help shape a more positive future for cancer care globally.
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