Monday, July 15, 2024

Real-World Data: Enhancing Cancer Treatment Insights for Diverse Populations

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The intricacies of modern cancer treatment demand data that goes beyond the confines of traditional randomized trials. For populations such as the elderly, those with comorbid conditions, and rural residents, real-world data (RWD) offers a promising avenue to capture the nuanced realities of their healthcare experiences. This shift is crucial for informing both clinical practice and healthcare policy, ensuring treatments are effective across diverse patient groups.

Challenges in Oncology Data Collection

Randomized trials, while providing high-quality and consistent data, often fail to encompass the broader aging population, who are predominantly diagnosed with cancer and typically have other health conditions. This gap underscores the urgent need for relevant, high-quality data that can be obtained through the collection and analysis of real-world data. RWD can reveal critical insights into patient outcomes during and post-treatment for subgroups like the elderly, children, and those in rural areas.

Sources and Trustworthiness of Real-World Evidence

To effectively inform clinical practice and policy, real-world evidence must originate from reliable and comprehensive RWD sources. These include pragmatic clinical trials, registries, prospective observational studies, electronic health records (EHRs), administrative claims, and digital technologies. However, oncology faces unique challenges as essential parameters such as cancer stage, biomarker status, and quality of life are often unrecorded, isolated in inaccessible documents, or available only as unstructured data in EHRs.

Advancements in analytics, particularly artificial intelligence, hold promise for extracting more detailed information from EHRs and supporting integrated diagnostics. However, these technologies require validation tailored to specific purposes to ensure accuracy and reliability. Standardizing data collection across various sources and building infrastructures capable of producing fit-for-purpose RWD are recommended to achieve timely and effective treatment insights.

Implications for Market Access

For market access stakeholders, understanding the real-world effectiveness of cancer treatments is critical. The ability to analyze comprehensive RWD can:

  • Enhance the evidence base for drug approvals and reimbursement decisions.
  • Support the development of tailored treatments that address the specific needs of diverse patient populations.
  • Facilitate more equitable healthcare access by providing data on underrepresented groups.

In conclusion, the collection and analysis of real-world data are vital for advancing cancer treatment and ensuring that it is effective across diverse populations. The commitment to standardizing data and building robust infrastructures will enable timely and relevant insights, ultimately improving patient outcomes and informing healthcare policies.

Original Article:

JCO Clin Cancer Inform. 2024 Jun;8:e2400039. doi: 10.1200/CCI.24.00039.

ABSTRACT

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Randomized trials provide high-quality, internally consistent data on selected clinical questions, but lack generalizability for the aging population who are most often diagnosed with cancer and have comorbid conditions that may affect the interpretation of treatment benefit. The need for high-quality, relevant, and timely data is greater than ever. Promising solutions lie in the collection and analysis of real-world data (RWD), which can potentially provide timely insights about the patient’s course during and after initial treatment and the outcomes of important subgroups such as the elderly, rural populations, children, and patients with greater social health needs. However, to inform practice and policy, real-world evidence must be created from trustworthy and comprehensive sources of RWD; these may include pragmatic clinical trials, registries, prospective observational studies, electronic health records (EHRs), administrative claims, and digital technologies. There are unique challenges in oncology since key parameters (eg, cancer stage, biomarker status, genomic assays, imaging response, side effects, quality of life) are not recorded, siloed in inaccessible documents, or available only as free text or unstructured reports in the EHR. Advances in analytics, such as artificial intelligence, may greatly enhance the ability to obtain more granular information from EHRs and support integrated diagnostics; however, they will need to be validated purpose by purpose. We recommend a commitment to standardizing data across sources and building infrastructures that can produce fit-for-purpose RWD that will provide timely understanding of the effectiveness of individual interventions.

PMID:38950323 | DOI:10.1200/CCI.24.00039

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