When Will Real-World Data Fulfill Its Promise to Provide Timely Insights in Oncology?
Marc L BergerPatricia A GanzKelly H ZouSheldon GreenfieldPublished in: JCO clinical cancer informatics (2024)
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.
Keyphrases
- electronic health record
- big data
- artificial intelligence
- healthcare
- clinical trial
- clinical decision support
- machine learning
- public health
- randomized controlled trial
- adverse drug
- papillary thyroid
- mental health
- primary care
- type diabetes
- palliative care
- young adults
- deep learning
- gene expression
- emergency department
- risk assessment
- squamous cell
- systematic review
- open label
- high resolution
- squamous cell carcinoma
- high throughput
- skeletal muscle
- health insurance
- metabolic syndrome
- social media
- climate change
- glycemic control