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Leveraging Real World Data to Measure Disease Severity
Real world evidence can uncover hidden details about the severity and costs of the disease journey
Feb 03, 2021
The deluge of healthcare data and developments in advanced analytics is driving an evolution in the use of real world data (RWD) across the healthcare continuum. The industry can now apply techniques that tease out a new level of detail about disease severity, helping developers find patients, evaluate treatments, and assess the comparative effectiveness of their treatments to payers, providers, and regulators.
These data have the potential to improve patient outcomes, lower healthcare costs, and optimize product use. To achieve these benefits, developers need access the most robust data, analytics technology, and expertise.
Linking the data
Medical and prescription claims data have long been the go-to secondary data sources to conduct real world research. These databases offer a broad view of the disease landscape, and insights into healthcare utilization and costs.
While claims data can tell you how many people see a physician or fill a prescription, there are limitations when it comes to providing detailed clinical information required to measure certain outcomes of interest, especially those focused on disease morbidity or severity.
However, when researchers combine claims data with electronic medical records (EMR), hospital data, and laboratory results, they can gain new and more detailed insights into disease severity, progression, morbidity, and mortality. Each of these datasets offers a unique perspective to flesh out the patient experience.- Electronic medical records (EMRs) provide details about the patient’s disease journey, including descriptions of symptoms, comorbidities, treatment guidance, and other information deemed relevant by their treating physician.
- Laboratory results provide information about tests conducted, results, and the impact of treatments used, which can answer questions about disease severity.
- Hospital data provides additional details about in-patient experiences from admission to discharge. It offers line item details about drug dosages, tests conducted, diagnoses made, and any treatment outcomes, including bleeding episodes, new infections, or temperature changes.
When these data sources are linked at the patient level (while following HIPPA guidelines), researchers can better predict outcomes and adverse events, and conduct deeper patient-level analysis into disease progression, severity, and treatment efficacy.
Developers across the globe are rapidly adopting this diverse approach to RWD analysis, by selecting and combining the right RWD sources to better-understand the healthcare experience.
Case Study: Measuring cost associated with severity in multiple sclerosis (MS) patients
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 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.
First, all MS patients in IQVIA’s Ambulatory EMR 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 study found that the adjusted healthcare costs were 15 percent higher in patients with moderate disability than in patients with mild disability, and 20 percent higher in patients with severe disability compared to those with mild disability. It also shows that disease modifying therapy (DMT) costs accounted for 89 percent, 82 percent, and 78 percent 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.
Looking Ahead
IQVIA is now exploring the use of algorithms to predict when MS progression will occur, and to understand how these insights could be used to enhance treatment effectiveness for MS patients. Researchers are also exploring other healthcare categories that could benefit from similar analyses.
This is just one example of how developers can use real world evidence to answer important research questions. We have only just begun to see how RWD and AI-driven analytics can change the way we think about diseases, and we are excited to see what the future holds.
On-Demand Webinar: Reaching beyond claims for better insights
In this recorded webinar, IQVIA experts discuss novel approaches to conduct research using secondary data sources and methods.