Cancer research is one of the mostly heavily invested areas of medical research, with more than $5 billion spent on exploring and developing new therapeutics and treatment options in the U.S. annually. Given that more than 1.7 million people are diagnosed with cancer in the U.S. every year, this research is critical to prolong survival and improve quality of life for patients.
While a significant element of cancer research relies on patient data, much of the available data is limited in quantity and scope. Currently, this type of information is primarily obtained from labs or clinical trials, limiting analyses to small cohorts of patients, which significantly affects the results obtained. Additionally, these controlled environments can produce synthetic outcomes and data that may not be reflective of real-life situations.
A further point of consideration is that most research in cancer care is focused on the disease, rather than individual patients. When the primary goal is to shrink tumors and kill cancer cells, research and collected data tend to favor the clinician’s view, and certain key insights provided by the patient may be missed.
For data to be truly valuable, it must not be limited solely to a patient’s clinical experience but should also take into consideration all the facets of the patient’s life. That includes demographics and financial status as well as psychological, emotional and logistical factors. Through the aggregation of such comprehensive data, researchers can uncover new patterns in cancer prognosis, diagnosis and treatment, which can greatly contribute to our broader understanding and management of the disease.
Maximizing patient engagement through communities
With the rise of patient communities, we now have the ability to track full patient journeys among a large population. These platforms allow patients to access information specific to their condition, track their disease journey and connect with others in similar situations. While communities can collect valuable data, members tend to be more forthcoming regarding personal aspects of their journey when they can post anonymously, which leads to richer data while retaining full patient privacy.
To maximize the usability of this data, a large population needs to be available to enable a wide spectrum of demographics. To create a large userbase, high patient engagement is necessary, which is a continual challenge for such forums and apps. The best way to achieve this goal is by ensuring the platform provides true value for the user. If an app asks too much of patients, requiring them, for example, to constantly track symptoms and fill out questionnaires, patients will rarely use them, resulting in little to no usable data being created.
In order to retain members and keep them truly engaged, patient communities need to provide tools and features that will encourage engagement from the very start. This includes medically verified content, networking capabilities that connect patients, connection to medical experts and other useful tools for patients going through intense journeys. You might provide, for example, a way to track and manage symptoms and side effects and a place to store medical files are both good examples.
Practical and easy-to-use tools will encourage patients to use the networks multiple times a day. That ultimately will help with generating large amounts of rich yet anonymized data that go beyond treatment and provide insight into the complete patient journey.
Tracking specific patient input
Artificial intelligence and machine learning have enabled researchers to assess large volumes of data and generate meaningful insights more effectively and efficiently than ever before. Together, these technologies can detect subjective information offered by patients, such as their concerns and sentiments—something that electronic medical records seldom record.
Algorithms also detect data points, such as disease symptoms, drug efficacy and side effects, while also looking for other elements that may affect a patient’s personal experience. For example, are they practicing yoga, using cannabis or taking supplements? What’s the patient’s diet like and how much sleep do they generally get per night?
AI and ML essentially connect the dots between observed data points to discover connections or patterns. By observing a chain of events, or causality, we can gain deep insights into many neglected aspects of cancer care. These chains of events are, in fact, patient journeys, and important discoveries can be made through observing the journeys of hundreds of thousands of patients. For example, if a certain symptom becomes apparent in many patients taking a specific new drug, a previously undetected side effect may be discovered.
Research suggests that cancers are not always or only caused by a genetic or familial defect, but may also result from the accumulation of different triggers and environmental changes. The ability to identify these factors and track health determinants through patient communities provides an incredible preventive set of tools to anticipate the risk of developing a cancer and the measures that need to be taken to minimize that risk.
Additionally, due to the large number of different malignant diseases, stages and subgroups present in oncology, there is much room for observation, including that of 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 patient communities with the help of AI and ML is ripe with opportunity for researchers. The abundance of information available, highlighting so many aspects of the unique patient journey, provides new, real-world insight that has largely remained hidden until now.
Through the analysis of this data, researchers can gain new perspectives on a variety of important facets. That includes the cost of illness, length of treatment plans, benefits of alternative treatments in conjunction with a medical regimen, management of side effects and much more.
Such insights have already provided the backbone for studies on the effect of Covid-19 on cancer patients. This kind of data has also informed research on how a breast cancer diagnosis affects the sex life of patients and their partners. In sum, these insights are infusing the industry with new understandings that can significantly improve care and patient quality of life.
Of course, with so much data, we can anticipate that many additional studies are in the pipeline. Additionally, through these insights, oncology researchers may also be able to foresee the possibility of a cancer recurrence, which remains one of the most important concerns among patients and clinicians alike. Researchers may also be able to better assess the measurement of survival for specific patients.
It is clear that patient communities are an integral tool for elevating the traditional approach to cancer care and research. By going beyond treatment to consider individual patient journeys, we uncover hidden patterns. These insights can improve quality of life for individual patients and populations and, ultimately, help shape a more positive future for global cancer care.
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