The need for quality-of-life data among cancer patients: Addressing disparities and leveraging technology

Cancer treatment has come a long way in recent decades, but as survival rates improve,[1] the need to focus on the quality of life (QoL)[2] of patients has become more urgent. While survival rates and clinical outcomes are important markers, they do not tell the full story of a cancer patient’s experience. The impact of cancer and its treatments on day-to-day life can be profound and diverse, affecting physical, emotional, and psychological[3] well-being. The need for comprehensive QoL data among cancer patients is critical for guiding effective care and improving overall outcomes. Currently, there is a significant gap in the collection and use of QoL data. This gap becomes even more evident when examining the disparities between different demographic groups. Research has shown that women, particularly those diagnosed with breast cancer, may face unique physical and emotional challenges[4] compared to men with cancer. Women often experience side effects like hair loss, fertility issues, and hormonal changes, which can have a significant impact on their quality of life. Furthermore, societal expectations and caregiving responsibilities often place additional stress on women, compounding the challenges they face during and after treatment. Understanding these gender-specific issues is key to improving care for female cancer patients and addressing their unique needs. In addition to gender disparities, race and ethnicity play a crucial role in shaping the cancer experience. Black and Hispanic cancer patients, for instance, face a disproportionate burden of cancer-related disparities when compared to their White and non-Hispanic counterparts. Black patients may have up to a 78% higher incidence of prostate cancer compared to non-Hispanic whites.[5] These disparities are not only linked to access to care but are also influenced by socioeconomic factors, cultural differences, and systemic inequalities in the healthcare system. Studies show that Black and Hispanic cancer patients are more likely to receive treatment late (Black/60-day OR 1.78 and Hispanic/60-day OR 1.5 compared to white patients),[6] which can worsen their prognosis. Furthermore, these groups may experience higher levels of pain and distress due to delayed diagnosis, lower access to palliative care, and a lack of culturally tailored interventions. Gathering QoL data across diverse populations is essential for identifying these disparities and ensuring equitable care for all cancer patients. Building real-world data (RWD) from both clinical settings and patient-reported outcomes[7] is key to understanding the full scope of the cancer journey. Advances in artificial intelligence (AI), predictive modeling and large language models[8] hold the potential to revolutionize the way we collect, analyze, and apply QoL data in research and clinical practice. By integrating data from electronic health records (EHRs), wearable devices, and patient surveys, AI models can provide valuable insights into how cancer treatment impacts patients in real-time. These insights can help doctors personalize treatment plans, predict complications, and offer tailored interventions that address both physical and mental health needs. With the use of advanced computing cancer patient digital twins (CPDTs) the model training could be enhanced tremendously.[9] Furthermore, AI models can help in identifying underserved populations and suggest precise strategies for addressing their specific QoL concerns. RWD can also serve as a valuable tool for improving clinical trials. By incorporating a broader range of patient experiences, particularly from underrepresented groups, researchers can gain a deeper understanding of how different treatments work across diverse populations. The integration of QoL metrics into clinical trial design allows for a more holistic approach to evaluating cancer therapies, moving beyond just survival rates and tumor shrinkage to include the patient’s overall well-being. This will lead to more inclusive and equitable[10] high quality cancer care, ensuring that new treatments benefit everyone, not just a select few. In conclusion, while clinical outcomes remain important, understanding and addressing the quality of life of cancer patients should be a priority. The disparities in QoL among women, Black, and Hispanic cancer patients compared to their male and White counterparts highlight the need for targeted interventions and personalized care. By leveraging real-world data and AI models, we can bridge gaps in healthcare, provide a more accurate reflection of patient experiences, and improve overall treatment plans. CALL-TO-ACTION To improve cancer care, it is imperative that we prioritize the collection and analysis of quality-of-life data. This will not only help identify and address disparities among different demographic groups but also foster personalized treatments. By embracing advances in technology, such as real-world data and predictive AI models, healthcare providers can offer more equitable, patient-centered care. It’s time for a shift in focus: from simply surviving cancer to thriving after cancer. Policymakers, researchers, and healthcare providers must collaborate to make this vision a reality. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.