Introduction: The $350 Billion Healthcare Data Revolution You Can't Afford to Ignore
The healthcare industry is sitting on a goldmine of untapped data worth $350 billion annually. While traditional clinical trials have dominated medical research for decades, a seismic shift is happening right now. Real-World Data (RWD) and Real-World Evidence (RWE) are revolutionizing how we understand patient outcomes, accelerate drug approvals, and deliver personalized healthcare.
Here's the shocking reality: 90% of healthcare data goes unused, yet the organizations harnessing RWD are seeing 60% faster drug approvals and 40% reduction in clinical trial costs. Whether you're a pharmaceutical executive, healthcare provider, or research professional, understanding RWD and RWE isn't just an advantage—it's essential for survival in today's data-driven healthcare landscape.
Real-World Data Overview: The Foundation of Modern Healthcare Intelligence
Both RWD and RWE have many valuable uses in healthcare, including patient recruitment for clinical trials, comparing drug efficacy, and monitoring drug safety. However, it is important to understand the key differences between these two concepts.
What Is Real-World Data? The Game-Changer Beyond Clinical Trials
Clinical trials seek to answer specific questions in a controlled environment to earn regulatory approval of an investigational drug or device. Typically, the information gathered from these trials lacks data from a real-world environment. Therefore, post-market studies are often critical to understanding patient adherence and clinical efficacy in the real world, outside of a controlled clinical study.
The pharmaceutical industry has traditionally used randomized controlled trials when seeking approval of therapies, but the U.S. Food and Drug Administration (FDA) has been developing a framework and issuing guidance to support the use of real-world evidence—enabled by RWD—in regulatory decision-making. Additionally, technological innovations that protect patient privacy have expanded the possible sources of real-world data available to researchers immensely.
RWD comes from a variety of sources outside of traditional clinical trials. Researchers routinely collect patient data from sources such as:
- Claims and billing activity
- Electronic health records (EHRs)
- Patient-reported outcomes
- Disease or product registries
- Biometric monitoring sources such as pedometers and smartwatches
Health data from these sources can provide a more comprehensive picture of the patient journey and experience, and can even provide an overview of population health. Although RWD can enable clinical evidence, it's essential that the data is fit-for-purpose (i.e., relevant, valid, and reliable), which is determined based on the research question.
How Does Real-World Data Differ from Real-World Evidence?
Stop Confusing These Two Critical Concepts!
Real-world data and real-world evidence are often used interchangeably, but they are two different concepts. RWE derives from the analysis of RWD and can provide valuable information about the risks, benefits, and use of a therapy. Real-world evidence helps accelerate the approval of new therapies, especially in oncology.
What Is the Value of Real-World Data and Real-World Evidence?
The ROI is Staggering: Here's Why Smart Organizations Are Investing Heavily
By using sources of patient health data, researchers can evaluate therapies in a larger population, in real-world conditions, and at a lower cost than with typical clinical trials.
RWD has the potential to provide information about a more diverse population than the typical clinical trial participants. Therefore, researchers can get valuable efficacy and safety information on a more representative population than they can from a randomized clinical trial.
RWE provides a more comprehensive view of how a therapy will work in a real-world setting. Researchers can evaluate the therapy while factoring in other variables such as comorbidities, demographic groups, and age groups, among other parameters. Most importantly, RWE helps researchers develop a better understanding of the long-term use of the therapy beyond the clinical trial period.
🎯 Example Use Cases of RWD and RWE That Are Changing Healthcare Today
Some ideal use cases for RWD and RWE include regulatory requirements and deciding on a treatment plan for patients.
RWE can help support regulatory requirements to expand on a therapy's indication without performing a full additional clinical trial. For example, if a product is often prescribed for off-label conditions, companies may use RWD to study patient outcomes and therapy safety and then submit this information to regulators for market authorization.
Healthcare providers can use RWD and RWE to better inform a patient's treatment plan, procedures, tests, and prescriptions, and these data may help develop practice guidelines. For instance, during the beginning of the COVID-19 pandemic, public health officials needed to rapidly evaluate and share information on the prevention and treatment of COVID-19. Much of the information gathered during this time leveraged RWD.
The Complete Real-World Data Ecosystem: Your Roadmap to Success
Numerous types of patient data gathered from multiple sources can be useful for generating RWE. To develop a deeper understanding of how RWD can be used—and how it can add value in healthcare—examining key data types and sources can be instructive.
Real-World Data Types: The Building Blocks of Healthcare Intelligence
Health data can be pulled from numerous sources to provide valuable insights into the patient journey.
💰 Claims Data: The Financial Fingerprint of Healthcare
Claims data results from processing a healthcare claim. Two types of claims data include open and closed. Open claims datasets come from claims clearinghouses or providers' revenue cycle management systems. They cover a large scale of patient lives, but may not represent complete claims coverage for a given patient.
Closed claims come from health insurance plans or self-insured employer groups. They tend to cover a smaller scale of patient lives, but represent complete claims coverage for a given patient during the time that patient was on the insurance plan or worked at the employer. Claims data is longitudinal in nature and captures a long period of the patient journey, but it does not have as much depth of clinical detail about a particular medical encounter as other data types.
Because closed claims datasets are very comprehensive, they prove ideal for health economics and outcomes research (HEOR) that considers a patient's journey, resource utilization, and the economic burden of their condition. Open claims datasets prove less useful for HEOR, due to their incompleteness for a given patient. Given the large scale of patients covered as well as lower data latency, open claims datasets can prove useful for marketing use cases.
Claims data is even more powerful when used in tandem with clinical metrics such as lab data, EHR data, or patient-reported outcomes. The combination of these data sets can provide deeper insight into symptoms, disease progression, and clinical outcomes.
🧬 Laboratory and Genomics Data: The Molecular Map to Precision Medicine
Lab testing data proves valuable for a variety of use cases in healthcare analytics, from market sizing to monitoring disease progression to finding biomarker signals of patients eligible for certain therapeutics. Lab data can provide a deep point-in-time clinical and biochemistry profile of a patient, but isn't as longitudinal as claims data.
Genomics data is a specialty area of lab testing currently growing in popularity within healthcare analytics given the increase in biomarker-targeted therapeutics. Genomics data proves useful for both clinical development use cases in which scientists may employ genomics data to inform biomarker selection, or in commercial use cases in which genomic results may provide input towards building a predictive model to find patients eligible for a biomarker-targeted therapy.
💊 Pharmacy Data: Tracking the Therapy Journey
Pharmacy data provides information about which therapies patients have been prescribed and filled at a pharmacy. It can give insight into how therapies change over time. Pharmacy data proves extremely useful for specialty drugs, which now account for approximately 75 percent of prescription drugs in development. A network of specialty pharmacies, contracted by pharmaceutical manufacturers, typically distributes specialty drugs. The manufacturer will then aggregate real-world data from specialty pharmacies to understand real-world prescribing, dispensing, and medication adherence patterns.
📋 Electronic Health Records (EHR): The Digital Patient Story
As patients move throughout the health system, valuable real-world data is collected as part of their electronic health records (EHRs). EHR data contains richer clinical detail than claims data, but a patient may visit many providers across different care settings, using different EHR systems. This makes finding a single EHR real-world data source very unlikely. EHRs contain data on appointments, medical history, diagnoses, symptoms, medications prescribed, labs, and chart notes. These data are important for gaining a more granular understanding of clinical patient outcomes.
Even though EHR data is valuable, it requires significant curation and cleaning because much of the valuable information may reside in unstructured physician notes.
Generally, the information recorded as part of a patient's EHR—whether they're in an inpatient setting, outpatient setting, or a specific therapeutic area—includes:
- Procedures performed
- Diagnosis
- Vital Signs
- Laboratory results
- Medication orders
- Medications administered
- Patient surveys or questionnaires
- Surgical care information
- Symptoms
- Immunizations
- Social history, such as smoking status
Note that electronic health records on their own may not contain all of the necessary RWD, so researchers may be required to seek additional sources of data.
🎗️ Oncology Data: Fighting Cancer with Real-World Intelligence
RWD plays an important role in answering a variety of research questions surrounding cancer. One of the top priorities in research is generating accurate evidence on the efficacy of cancer prevention, diagnosis, and treatment in a real-world setting.
Researchers often study cancer treatments in a select population in a clinical trial setting. However, researchers can also collect and analyze real-world oncology data to provide RWE on the efficacy and tolerability of new treatment methods in the real world. The main sources of real-world oncology data include:
- Registries
- Claims
- EHRs
- Specialty data providers and networks
Each state legally mandates central cancer registries, thus providing a census of all the patients who have cancer within a defined geographic area. Because of this, as well as the capture of detailed exposure information such as diet or physical activity and patient-reported outcomes, these registries provide unique information because data comes from a non-random group of people.
Limitations of cancer registry data include a lack of information on outcomes other than survival as well as long-term treatment. Addressing these limitations requires new initiatives such as linking registry data with data from other organizations. The new initiatives, as well as real-time access to pathology reports, provide opportunities to supplement the understanding of therapeutic advances and impact outside of clinical trials.
👥 Consumer Data: Understanding the Person Behind the Patient
Recently, researchers have increased the demand for consumer data. This information can provide additional context about a patient population, such as:
- Employment
- Socioeconomic status
- Interests
- Health
- Race
- Ethnicity
- Languages spoken
These data come from consumer data companies and have traditionally been used for targeted marketing. Note that consumer data companies only have data on adult consumers.
🏘️ Social Determinants of Health (SDOH): The Hidden Healthcare Influencers
Social determinants of health (SDOH) are the conditions in which people are born, work, live, play, age, and worship. These have a large impact on peoples' health, quality of life, and functioning. Some examples of social determinants of health include:
- Poverty
- Education
- Racism
- Polluted water and air
- Access to healthy, nutritious foods
- Physical activity opportunities
Social determinants of health contribute to health inequities and disparities. For example, those who don't have access to grocery stores that carry healthy foods may not have good nutrition, which can lead to obesity, diabetes, and heart disease. Data on SDOH can provide insights to address health disparities and health equity.
Real-World Data Sources: Choosing Your Data Partner Wisely
Different types of data providers are relevant for different situations. Three types of data provider categories exist:
🖥️ Data Platforms: The Plug-and-Play Solution
Data platforms provide a technology platform that has intuitive user interfaces (UI) for analyzing data within the platform. These companies have data science and data engineering teams that clean and standardize continuous streams of data coming into the platform, and combined with the UI layer, can be considered user-ready.
In many cases, the platform provides limited ability to export data for use. Working with a data platform is best for companies without data analytics or data engineering capabilities.
📊 Data Aggregators: The Middle Ground
Data aggregators offer cleaned and standardized data that have been aggregated from many underlying sources. Typically, a technology platform or user interface overlay doesn't exist, and the data are available to license as a one-time or continuous data feed.
These data are analytics-ready. Companies working with a data aggregator need to have data analytics or business intelligence analysts who can manipulate the data into analysis, but they do not need to have sophisticated data engineering to clean and standardize the data.
🔧 Data Originators: The Raw Power Source
Data originators are closest to the source. They have the most granular and detailed data, but they do not clean it. These data requires the application of sophisticated data engineering capabilities before it can be analytics-ready.
Real-World Data Solutions Providers: The Innovation Ecosystem
The RWD ecosystem includes both real-world sources, as described above, as well as solutions providers that have built analytic and workflow solutions on top of real-world data. Many platform companies are also solutions companies, having built specific data views and analytic tools that provide solutions for specific use cases.
💼 Commercial Solutions That Are Dominating the Market
Specialty pharmacy aggregation: These companies aggregate specialty pharmacy data on behalf of pharmaceutical manufacturers to monitor therapy launches. Specialty drug data is proprietary data of pharma companies that may link to other real-world data such as claims for a longitudinal view of the patient journey.
Outcomes and patient journey: These companies enable outcome studies and patient journey research. Many of these companies build their solution on top of aggregated and linked claims data to enable a comprehensive view of patients as they move through the healthcare system.
Commercial triggers: These companies provide triggers to commercial teams at pharmaceutical companies to alert them when a patient eligible for a specific therapy sees their provider, so sales teams can be deployed to the provider's office for education on the relevant disease or therapy. Use of this solution is especially common in rare diseases since providers are often unaware of the rare disease and patients can be hard to diagnose.
Digital marketing: These companies identify relevant patients and providers and then serve up digital advertising to educate them on a disease or therapy.
Commercial analytics and insights: These companies provide aggregated data, usually claims or EHR, to help commercial teams with strategy and insights before and post-launch.
🧪 Clinical Solutions: Revolutionizing Drug Development
Trial recruitment: These companies use aggregated data—typically EHR, lab, and claims data—to identify the ideal clinical trial sites that have sizable populations of patients who would meet inclusion/exclusion criteria for a trial.
Synthetic control arms: Synthetic control arms, also known as external control arms, are studies in which real-world data is utilized as the control arm rather than enrolling actual patients into a control arm where a placebo or standard of care (SOC) is utilized.
This is popular in disease states where patient populations are increasingly sub-stratified by biomarker status (e.g., oncology, rare disease), given the challenges of recruiting enough patients as well as the ethical considerations of placing patients on placebo or standard of care (SOC). Companies that provide these solutions often have deep and highly curated clinical and genomic data to conduct synthetic control arms. Synthetic control arms lower trial costs, increase efficiency, and increase the speed of therapies to market.
Decentralized clinical trials: These companies provide technology infrastructure to collect data and support decentralized clinical trials (DCTs). DCTs are trials where patient communication and data collection has been decentralized away from a traditional clinical trial site. Instead, remote and digital technologies communicate with study participants and collect their data.
Conclusion: Your RWD Success Blueprint - Don't Get Left Behind in the Data Revolution
The real-world data revolution isn't coming—it's here. Organizations that master RWD and RWE today will dominate tomorrow's healthcare landscape. With the global RWD market projected to reach $84.8 billion by 2030, early adopters are positioning themselves for unprecedented growth and competitive advantage.
Here's your action plan for success:
Week 1-2: Audit your current data capabilities and identify gaps in your RWD strategy Week 3-4: Evaluate potential data partners based on your specific needs (platforms vs. aggregators vs. originators) Month 2: Launch a pilot RWD project in one therapeutic area or use case Month 3-6: Scale successful initiatives and build internal RWD expertise
The stakes couldn't be higher. While you're reading this, your competitors are already leveraging RWD to accelerate drug approvals, reduce trial costs, and deliver better patient outcomes. The companies that acted decisively on RWD adoption are now seeing 3x faster time-to-market and 50% better patient recruitment rates.
Remember: In today's data-driven healthcare ecosystem, RWD isn't just about collecting information—it's about transforming that data into actionable insights that save lives, reduce costs, and drive innovation. The question isn't whether you should invest in RWD capabilities, but how quickly you can build them before your competition leaves you behind.
Start your RWD transformation today. Your patients, stakeholders, and bottom line depend on it.
Ready to unlock the power of real-world data? The organizations making strategic RWD investments now will be the healthcare leaders of tomorrow.