With advancements in data analytics and availability of real-world data sources, real world evidence (RWE) is gaining increasing importance in pharmaceutical and life sciences decision making. RWE involves using data from electronic health records (EHRs), claims and billing activity data, product and disease registries, integrated delivery networks, and other sources to generate evidence for driving research, development, and patient outcomes.
While randomized clinical trials remain the gold standard, RWE can help answer questions that cannot feasibly be addressed through traditional clinical trials alone. RWE offers the potential to generate evidence using much larger and more diverse patient populations in real-world clinical settings over extended periods. This enables understanding treatment effectiveness, safety, and optimal use beyond the controlled environment of clinical trials.
Key Data Sources And Technologies For RWE Generation
EHRs provide a rich source of patient-level longitudinal data reflecting the real-world patient journey. When linked with other data sources like pharmaceutical administrative claims, product registries and patient-reported outcomes, EHRs enable generation of comprehensive RWE. Advanced technologies like machine learning and artificial intelligence are now routinely applied to analyze large and diverse real-world datasets.
Claims and administrative databases maintained by health insurers and government programs capture detailed reimbursement activity data reflecting treatments received in real-world settings and across large patient populations over long periods. Linking claims with clinical data sources like EHRs enhances the insights that can be generated from claims data.
Patient registries collect standardized information from subject matter experts and are being increasingly leveraged for RWE. Disease registries in particular offer the potential to follow patients over the long term, allowing assessment of treatment effectiveness and safety outcomes in certain disease areas.
Mobile health technologies and wearables now also contribute new types of real-world data through continuous collection of physiology, behaviors and lifestyle metrics. Novel data types from sources like social media and website searches are also finding RWE applications. Overall, the expanded availability of diverse data sources is fueling innovative RWE uses.
Applications Of Pharmaceutical And Life Sciences Real World Evidence In Drug Development And Patient Care
One key application of RWE is to generate evidence for new indications of already approved drugs. By leveraging rich real-world datasets, RWE studies allow evaluating drugs for new patient populations or conditions beyond initial trial scope in a more timely and cost-effective manner than traditional trials.
RWE is also increasingly used in drug safety monitoring to complement mandatory reporting of adverse events from randomized trials. Large-scale analysis of real-world data enables detection of even rare safety signals with greater statistical power than trials alone. Proactive safety monitoring helps characterize long-term risks more comprehensively.
In clinical decision support and personalized medicine, RWE helps optimize treatment selection and dosing for individual patients based on real-world effectiveness in patient subgroups. It also aids understanding of effectiveness variations due to differing comorbidities, concomitant medication use and other real-world factors not captured in trials.
RWE finds applications in health technology assessment and reimbursement decisions as well. Generating real-world evidence on comparative effectiveness, quality of life impacts and economic outcomes assists payers and formulary decision making.
However, challenges remain around data quality, completeness and bias that could impact RWE credibility versus randomized trials. Standards and methodologies are still evolving for optimal RWE generation and evaluation.
Pharmaceutical And Life Sciences Real World Evidence Enablement Requires Technology Investments And Partnerships
Pharmaceutical organizations are making significant investments in data and analytics infrastructure to enable systematic RWE generation at scale. Advanced technologies like cloud computing, artificial intelligence and machine learning are playing an increasing role in processing disparate real-world data sources and generating actionable evidence.
Partnerships are also key given that most real-world data resides outside manufacturer systems. Collaborations between life sciences companies, healthcare providers, payers, clinicians and patient groups are leveraging shared data assets and domain expertise for RWE applications. Initiatives like patient registries also rely on partnerships across stakeholders.
Overall, as RWE methodologies mature along with supporting technologies, real-world evidence has tremendous potential to complement clinical trials and support continuous learning about approved medicines. With proactive investments and strategic partnerships, life sciences companies are positioning themselves to extract maximum insights from real-world patient experiences.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it