Facts and figures. Let's start with some to frame why real world data (RWD) and the real world evidence (RWE) it delivers continue to grow in significance. RWD and RWE bring increasing value to pharmaceutical and medical technology (MedTech) companies to accelerate study timelines, shorten time to market, demonstrate safety and efficacy, and even maximize commercial success.   

• In a published report based on a 2020 industry survey, more than 80% of responding companies indicated they are entering into strategic partnerships to access new sources of RWD.¹
• Almost all companies expect to increase investments in talent, technology, and external partnerships to strengthen their RWE capabilities.²
• 90% of respondents reported they have either already established or are currently investing in building RWE capabilities for use across the entire product lifecycle, but only 45% have mature enough capabilities to do so.³
• FDA has published an analysis of 90 examples using RWE to support regulatory decision making in MedTech.⁴

While RWD and RWE are by no means new, what is new is the increased focus on their prevalence and the value that such data and evidence will continue to provide to pharmaceutical and MedTech companies throughout a product's lifecycle.

The time to embrace RWD and RWE is now.

RWD forms the basis for RWE and can be extracted from a broad range of sources, but not all RWD is created equally.

The extent to which the data can provide valuable evidence, depends on the appropriate use of the most relevant source(s) of data, as well as dependency on the appropriate use of expertise in analyzing and reporting study findings. High-quality research can be achieved by early-onset collaboration with the right partner from design to delivery. 

Getting Ready: How to Select Real World Data

Relevancy and reliability of data are critical to ensure that RWD and the evidence generated can be used appropriately to support decision making.

Before diving in headfirst, researchers should define a clear clinical research question and work with their relevant stakeholders to develop a study design most likely to result in clinically robust answer(s). There is no one-size-fits-all approach to selecting RWD.

Some factors to address when selecting RWD include⁵:

• The data elements to be collected
• Data element definitions
• Methods for data aggregation
• Relevant timeframes for data element collection

While those factors can guide RWD source(s) selection criteria, the real word dataset(s) need to be evaluated to determine if it is fit-for-use. Here are three key considerations to help do just that:

• Availability: What data are available? Can the information be ethically and efficiently sourced?
• Relevance: How relevant are the data sources? Are the data capable of answering the desired research question(s)?
• Reliability: How reliable are the data? How likely is the dataset to provide a sufficiently-robust answer?⁶

How to source Real World Data to develop high-quality Real World Evidence

The next step is sourcing the right real world data for your unique needs. What does this process look like? What are some of the common sources of RWD in healthcare?  

While a wide array of RWD sources exist, each has its strengths and limitations. But when you factor in the ability to link multiple U.S. data sources, the value that RWD provides increases exponentially.

Here's a snapshot of six of IQVIA's RWD sources that can be leveraged and linked together as well as linked with other external data sources (e.g., patient registries, client-supplied data) to successfully drive results.⁷

• Health plan claims database: IQVIA's PharMetrics® Plus provides a longitudinal database of adjudicated medical and pharmacy claims, with data reaching back to 2006.
• Electronic medical records (EMR): This includes IQVIA's ambulatory EMR (AEMR) as well as specialty EMRs such as its Oncology EMR.
• Charge data master (CDM): This data source features over a decade worth of data from short-term, general non-federal hospitals. It tracks patients from all pay types, provides a greater level of detail about what happens during hospital visits, and extends beyond standard hospital claims data.
• Longitudinal prescription claims (LRx): This data source includes longitudinal patient prescription data assets comprised of retail, mail, and long-term care facility pharmacy data.
• Medical Claims (Dx): Featuring 1.1B annual claims, IQVIA's Dx data is collected from services performed in physicians' offices across the U.S. The data includes patient-level diagnosis, procedures, and in-office treatments for visits made primarily to U.S. office-based professionals across medical practices, hospitals, ambulatory surgical centers, and other healthcare practices.
• Laboratory data offerings: The laboratory data provides results of in-depth laboratory, genomic, and biomarker data. IQVIA collects this data through a vast network of national independent lab chains, lab data aggregators, and specialized labs. Thousands of different lab results are available in this data offering for different market segments.

Equipped with the vast array of data sources listed, combined with the ability to link them, what is the next step? How can RWD be leveraged in a real world study? The following case study provides a demonstration.

Case Study: Leveraging Real World Data to measure cost associated with severity in multiple sclerosis (MS) patients⁸

There are many metrics of a disease that are important indicators for patients, payers, and providers, but are difficult to measure using claims data alone. Consider multiple sclerosis (MS). Severity in this disease, as defined by level of disability, has a direct impact on disease management and patient outcomes. The level of MS disability progression is most commonly measured using Kurtzke's Expanded Disability Status Scale (EDSS).

EDSS scores are typically not available in readily-available RWD sources, including EMR and administrative healthcare claims databases. However, IQVIA researchers recently demonstrated that it is possible to define disability level in MS patients by using the individual elements of the EDSS to translate and analyze EMR data, and then corroborating those results through an analysis of health plan claims data.

The process

First, all MS patients in IQVIA's AEMR Database were identified. The database features data from 76 million patients across the U.S. dating back to 2006, and is one of the largest linkable EMR databases in the industry. Elements of the EDSS were identified in the diagnostic and problem list tables.

Working with a panel of MS experts, IQVIA researchers assigned a severity level for each EDSS-related symptom and used an assignment to map records to billing codes, attaching a measure of disability to each patient. The algorithm was specifically designed to track changing levels of symptom severity over time, and to note when patients had more than one symptom or condition -- one mild but another (others) severe. For example, in the pyramidal system responsible for motor movement, peripheral weakness might be seen as a mild symptom of disability, whereas paraplegia would be associated with a much more severe form of disability.

Then, all patients with MS in the EMR were linked to IQVIA's PharMetrics® Plus Claims database. In total, 45,687 patients were identified in the EMR database, and 1,599 were linked to the claims database.

Using the disability algorithm, symptoms were tracked to assess how they evolved among mild, moderate, and severe patients, and to quantify the related financial implications of these trends.

The findings

The study found that the adjusted healthcare costs were 15% higher in patients with moderate disability than in patients with mild disability, and 20% higher in patients with severe disability compared to those with mild disability. It also shows that disease modifying therapy (DMT) costs accounted for 89%, 82%, and 78% of outpatient pharmacy costs in patients with mild, moderate, and severe disability, respectively.

This study, which was recently published in the Journal of Medical Economics, is the first of its kind to show how a claims-based algorithm can be used to estimate MS disability utilizing data from EMRs.

It proves that complex disease data can be drawn from these RWD resources, advancing the opportunity to examine outcomes in the absence of standard markers of disease progression.

Conclusion: RWD will continue to add value  

Without question, RWD adds tremendous value to the research process. However, finding the right combination of unparalleled data, the right methodology for your study, and the right partner, can be complicated.

In today's highly competitive life sciences marketplace, connected data must be part of the evidence ecosystem to support smarter healthcare decisions that improve patient outcomes. IQVIA's diverse datasets deliver that value in a single, linked data ecosystem, meeting the unique RWD needs of organizations like yours. Find out more by visiting IQVIA's website, and talk to an expert today.

1. Deloitte. RWE focus is shifting to R&D, early investments begin to pay off. Retrieved March 2021 at https://www2.deloitte.com/us/en/insights/industry/health-care/real-world-evidence-study.html
2. Deloitte. RWE focus is shifting to R&D, early investments begin to pay off. Retrieved March 2021 at https://www2.deloitte.com/us/en/insights/industry/health-care/real-world-evidence-study.html
3. Deloitte: Mission Critical: Biopharma companies are accelerating real-world evidence adoption, investment, and application. Retrieved March 2021 at https://www2.deloitte.com/content/dam/insights/us/articles/4354_Real-World-Evidence/DI_Real-World-Evidence.pdf
4. MedTech Dive. FDA touts real-world evidence use by Abbot, Medtronic in analysis of regulatory decisions. Retrieved March 2021 from https://www.medtechdive.com/news/fda-real-world-evidence-use-abbott-medtronic/596846/ 5. IQVIA. Selecting Real World Data. Retrieved March 2021 at https://www.iqvia.com/library/white-papers/selecting-real-world-data
6. IQVIA. Selecting Real World Data. Retrieved March 2021 at https://www.iqvia.com/library/white-papers/selecting-real-world-data
7. IQVIA. Real World Data and Life Sciences Research. Retrieved March 2021 at https://www.iqvia.com/locations/united-states/library/white-papers/real-world-data-and-life-sciences-research
8. IQVIA. Leveraging Real World Data to Measure Disease Severity. Retrieved March 2021 at https://www.iqvia.com/locations/united-states/blogs/2021/02/leveraging-real-world-data-to-measure-disease-severity