What is Real-world Data?

Real-world data (RWD), also referred to as real-world evidence, refers to data collected from various sources outside traditional clinical trial settings. Such data primarily originates from electronic health records (EHRs), claims and billing activity, product and disease registries, ID cards, and mobile applications, among other sources. RWD provides complementary insights to randomized controlled trials (RCTs) by reflecting outcomes in real-world clinical practice settings across larger and more diverse patient populations.

Sources of Real-world Data
Some major sources that generate real-world data include:

Electronic Health Records (EHRs)

EHRs contain a wealth of patient-level data pertaining to demographics, medical history, diagnoses, procedures, prescription drug information, lab results, and other clinical information. When aggregated across many providers and patients, EHR data can offer insights into patterns of care, treatment effectiveness, safety outcomes, and more at a population level.

Claims and Billing Data

Real-world Data (RWD)  as Insurance claims contain details about medical services and procedures received by patients as well as related diagnoses and prescription medications. This type of administrative claims data provides a view into treatment patterns across health plans and patient subgroups. It enables analysis of healthcare utilization, expenditures, and adherence to guidelines.

Product and Disease Registries
Registries systematically collect standardized data on patients with specific medical conditions or those receiving particular medical devices or therapies. They allow monitoring of real-world outcomes, adverse events, effectiveness, and quality of care for specific rare diseases or new interventions.

Mobile Apps and Wearables
The rise of digital health technologies has led to the proliferation of data from technologies such as activity trackers, medication reminders, symptom checkers, and other mobile health apps. Such data reflects patients' actual behaviors, contexts, and sentiments in everyday life settings.

Benefits of Using RWD
Real-world data offers several key advantages over traditional clinical trial data:

Larger and More Diverse Patient Population - RWD reflects "real patients" seen in routine clinical practice and includes individuals often excluded from clinical trials such as elderly patients, pregnant women, pediatric patients, and those with comorbid conditions.

Real-world Effectiveness - It provides insights into treatment effectiveness and safety outcomes under real-world conditions of clinical practice versus restrictive trial protocols. This includes assessing how clinical effectiveness may differ in subgroups.

Post-market Monitoring - RWD enables ongoing monitoring of benefits and risks even after drugs and devices receive regulatory approval and are used by much larger populations outside trials. It allows detecting rare or long-term adverse events not observed in pre-market studies.

Healthcare Economics - Analyses using claims and administrative databases deliver insights into the comparative effectiveness, cost, and cost-effectiveness of different treatment strategies and healthcare programs across a large population.

Speed - RWD insights on safety issues, usage patterns, adherence rates, and more can be generated more rapidly than traditional trials to support timely decision-making.

Areas of Application for Real-world Data

By reflecting outcomes beyond randomized controlled settings, RWD augments clinical evidence across several therapeutic areas:

Oncology - RWD sheds light on comparative effectiveness of anticancer therapies, impact of genetic factors and biomarkers, management of side effects, palliative care patterns in community settings, etc. This helps shape optimal care pathways.

Cardiovascular disease - It aids understanding treatment patterns, safety and effectiveness of devices like stents and valves, management of risk factors, and outcomes in real-world subgroups including the elderly.

Neurology - Conditions like Alzheimer's, Parkinson's, epilepsy are increasingly monitored using RWD to track disease progression and quality of life outside controlled trials.

Rare diseases - Registries are critical for rare and orphan conditions due to small patient populations who can benefit from therapies. RWD expedites drug development and access.

Maternal and child health - Analyses of maternal/newborn claims and EHRs deliver insights into safety, efficacy patterns of vaccines, and impact of new screening/therapeutic guidelines on population health.

Real-world evidence also supports care delivery transformation goals like value-based care, precision medicine through improved disease subtyping and matching of therapies to biomarker-defined patient segments observed outside traditional trials. RWD plays a key role across the drug and medical technology lifecycle from discovery and development to market access and post-marketing surveillance activities.

Challenges of Analyzing Real-world Data

While RWD holds immense promise, its analysis also presents certain challenges:

Data Quality - RWD sources were not primarily designed for research and may have missing values, documentation errors, variability in collection practices across sites/regions.

Selection Bias - Patients included in registries or EHR systems may not represent the true target population. Lack of randomization affects ability to control for confounders.

Heterogeneity - Variability in protocols, definitions, standards limits pooling data across multiple sources for large-scale analyses.

Privacy and Security - Accessing patient-level RWD while protecting individuals' privacy and complying with regulations requires robust techniques.

Analytical Rigor - Observational study designs are more prone to biases than RCTs. Robust methodologies mitigate such biases to support causal inferences.

Lack of Standardization - Terminologies, data formats, variables definitions vary greatly across RWD systems limiting interoperability and reuse for novel research questions.

Despite existing challenges, real-world data analytics has become a key research paradigm complementing evidence from clinical trials. Rigorous methodologies, open formats, rigorous auditing and oversight are enabling RWD to fulfill its tremendous potential for improving patient outcomes and healthcare delivery worldwide.

About Author:

Ravina Pandya, Content Writer, has a strong foothold in the market research industry. She specializes in writing well-researched articles from different industries, including food and beverages, information and technology, healthcare, chemical and materials, etc. (https://www.linkedin.com/in/ravina-pandya-1a3984191)

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