Discovery Health TreatMe Qorus-NTT DATA Innovation in Insurance Awards 2025- Nominated

Introduction Cancer care has undergone remarkable advancements over the past few decades as our understanding of cancer biology improves and the number of therapeutic options increases. However, the landscape of cancer is evolving rapidly. With these advancements, there are new challenges to address, particularly the increasing incidence of cancer among younger populations, rising healthcare costs with new treatment options, and the growing need to optimise both survival rates and quality of life for patients.

With the rapid approval of new therapies/drugs, and the influence of clinical trials and real-world data in reshaping treatment paradigms, we have developed TreatMe. This tool evaluates survival outcomes and quality of life across various drug combinations, for a specific cancer diagnosis and factors in demographic features (such as age and gender). This tool will be introduced to help healthcare providers and case managers make informed decisions that balance treatment efficacy with the patient's overall well-being.

The growing incidence of cancer among younger populations Historically, cancer was largely considered a disease affecting older individuals. However, over the past several decades, there has been a significant rise in the number of cancer diagnoses among younger people. This is particularly true for certain cancer types, such as breast, colorectal, and prostate cancers, which are increasingly being diagnosed at earlier ages. Although at an absolute level these incidence rates are still much lower than for the older age groups, it is a trend to keep a close eye on. If we look at the Discovery Health Medical Scheme (DHMS) data, between 2010 and 2023, we have seen a compound average growth rate that is higher for the age group of <50 compared to the age group of >50 across all of the top 8 cancer types (Figure 1).

Figure 1: Incidence rate per 100,000 DHMS lives per age banding for the top 4 most common cancer types

The reasons for this trend are multifactorial, including lifestyle factors, genetics, and environmental exposures. While advances in early detection and screening have contributed to the higher detection rates, the rise in younger cancer patients poses unique challenges. These patients often face different prognostic outcomes and treatment tolerances compared to older adults. Additionally, their concerns are not only survival-focused but also about maintaining a good quality of life, particularly in terms of fertility, employment, and family life.

Accelerated introduction of new therapies The approval of new cancer therapies has accelerated in recent years, driven by advancements in molecular biology, immunotherapy, and personalised medicine. Over the past decade, the number of new cancer drugs introduced into the market through clinical trials has expanded significantly. This includes targeted therapies, immunotherapies, and novel combinations of existing treatments. For DHMS, from the start of 2015, we’ve seen 51 new cancer drugs funded for 7,590 patients costing us R2.3 billion. On average, this is around R310,000 per patient funded for new cancer drugs. In 2019 DHMS started funding immunotherapy drugs which caused a huge increase in cost to the scheme from that year. The 6 drugs that were released in 2019 have contributed to 28% of the R2.3 billion.

While these therapies offer hope for more effective cancer treatment, their rapid introduction into clinical practice raises several questions. How do these therapies compare in terms of survival benefit? What are the long-term side effects? And how can healthcare systems integrate them without overwhelming budgets? The continuous influx of new drugs into the cancer landscape demands robust clinical trials and real-world evidence to guide their optimal use. This not only ensures that patients receive the most effective treatments but also helps healthcare providers navigate the complexity of personalised medicine.

People are living longer, but at what cost to their quality of life? One of the greatest achievements in cancer treatment over the past few decades is the increased survival rates across many cancer types. Patients are living longer, thanks to better treatment regimens and advancements in early detection [6,7]. From our DHMS population we’ve seen that over time cancer survival rates have improved. A Cox proportional hazard model was fitted on DHMS female breast cancer patients, accounting for the following factors:  Year of diagnosis  Age at diagnosis  Chronic condition count  Metastases  Benefit group (i.e. the plan choice of the member)  Herceptin claimant  Avastin claimant From the model outputs when looking at the hazard ratios for the year factor we’ve seen that over time the risk of death decreases i.e. patients diagnosed in later years are at lower risk of death compared to the similar patients diagnosed in earlier years (Figure 2). Figure 2: DHMS breast cancer survival model hazard ratios by year of diagnosis

This extended survival often comes with a significant trade-off in terms of quality of life (QoL). Many cancer treatments, including chemotherapy and immunotherapy, are associated with severe side effects, such as fatigue, nausea, cognitive impairment, and even long-term physical disabilities [8,9]. Moreover, the psychological burden of living with cancer, particularly for patients undergoing long-term treatments, can be significant. Healthcare providers now face the dual challenge of improving survival while also minimizing the negative impact of treatment on a patient’s quality of life. This requires a paradigm shift in how we evaluate cancer treatment success—moving beyond survival alone to include QoL as a critical factor in treatment decision-making.

The rising end-of-life care costs Another concern arises which is the rising costs associated with end-of-life care. Cancer patients often require extensive treatment in the final stages of their illness, including hospitalisation (which can include intensive care unit stays as well as ventilation), palliative care, and extended chemotherapy or radiation. These costs can be a significant burden on both the healthcare system and the patients themselves. An analysis of all the DHMS patients that died in 2023 due to cancer, revealed that the amount spent in the last six months prior to death accounted for 2.3% of total scheme spend and 11.2% of total cancer spend for only 0.1% of scheme members and 5.6% of oncology members respectively. Compared to 10 years ago, this represents an 4% increase per annum in members dying of cancer and a 10% and 12% increase per annum in total spend and cancer related spend respectively in the last six months prior to death.

End-of-life care is among the most expensive segments of cancer treatment. In many cases, the focus is on prolonging life, rather than improving comfort or managing symptoms. This approach leads to rising healthcare costs without a corresponding improvement in patient satisfaction or quality of life in the final months or years of life [10]. This growing financial strain underscores the need for a re-evaluation of treatment goals, with a more balanced approach that includes cost-effectiveness, patient comfort, and quality of life in the decision-making process.

Clinical trials are broad and controlled, but not reflective of real-world dynamics While clinical trials have been pivotal in bringing new therapies to market, they are often highly controlled and may not reflect the diversity or complexity of real-world patient populations. Clinical trial participants tend to be younger, healthier, and less diverse than the broader population of cancer patients. Furthermore, trials typically focus on specific treatment regimens for specific cancer types, with little attention given to the nuanced factors that patients face in everyday life. Factors such as comorbidities, socioeconomic status, and psychosocial factors often play a critical role in patient outcomes but are not well-represented in clinical trials.

Real-world experience is increasingly being recognised as a vital source of information to guide treatment decisions. By integrating real-world experience with clinical trial data, we can gain a more holistic view of how therapies perform in diverse patient populations, ensuring that treatment options are truly aligned with real-world patient needs.

Current versus new cancer treatment models, survival versus quality of life at what cost Currently, the primary focus of cancer care is on survival rates and benefits of treatment regimes. The success of a treatment regimen is often measured by how long it extends a patient's life, regardless of the side effects or long-term impact on their quality of life. However, with the advent of new therapies and a better understanding of cancer’s impact on patients' lives, there must be a shift toward a more holistic treatment model. The Pareto effect, states that roughly 20% of cancer patients (those with the most aggressive or advanced disease) often account for 80% of healthcare costs due to intensive treatments, hospitalizations, and end-of-life care. Understanding this helps optimise resource distribution for cost-effective care. If we look at the DHMS experience in 2023 we found that 20% of cancer members accounted for 87% of cancer spend. 10% of the top 20 most expensive members in the scheme were oncology members, making up 18% of total scheme spend. Oncologists currently prioritse precision medicine to identify patients likely to benefit from certain treatments. However, patients unlikely to respond can avoid unnecessary toxicity and costs. Clinical trials often struggle because only a minority benefit, making it crucial to refine patient selection and making it more personalised. TreatMe therefore evaluates treatment success not only in terms of survival and benefits but also in terms of quality of life, side effects, costs and overall well-being.

TreatMe recognises that survival is important, but it is not the sole factor in determining treatment success. The balance between survival outcomes, the side effects of treatment, the costs of the proposed treatments and the patient's quality of life is now a cornerstone of personalised cancer care.

The role of real-world data and AI in optimising treatment The next frontier in cancer care involves integrating real-world data and experience with machine learning and artificial intelligence to guide treatment decisions. Real-world data and experience include treatment history from other similar members, comorbid conditions, and lifestyle factors, all of which play a significant role in shaping cancer treatment choices.

Machine learning algorithms can process vast amounts of real-world data and experience to identify patterns and predict the optimal treatment path for individual patients. By incorporating survival data, side effect profiles, and QoL indicators, AI can help healthcare providers and case managers make more informed decisions about which therapies to prescribe, how to manage side effects, and when to adjust treatment regimens.