Written by: Rachana Kolluru, Product Manager, Veradigm Solutions Management and Cheryl Reifsnyder, PhD
Globally, cardiovascular death is the leading cause of death, taking the lives of 18 million people each year.1 The global cardiovascular drug market size is around $55.32 B in 2022 and is projected to reach $78.791 B in 2030.2 Patients with a history of cardiovascular disease (CVD) experience various physical and emotional symptoms that limit their physical and social activities, leading to poor quality of life. Hospitalization and mortality rates are also high among CVD patients.
There have been more than 500 clinical trials studying CVD since 2018.3 However, there have also been increasing concerns about the current state of cardiovascular clinical research, concerns such as:
These factors, plus increasing research costs, have undoubtedly contributed to recent declines in investment in cardiovascular research.
Many are hopeful that electronic health records (EHRs) will help counter some of these concerns by providing data—data already being collected—for use in clinical research. This blog examines the benefits and challenges of using EHR data for clinical research and explores how the Veradigm Therapy Focused Solutions product addresses many of these potential challenges.
The widespread adoption of EHRs has transformed the healthcare landscape in recent years—and, in the process, EHRs have come to offer an ever-increasing wealth of data for research and analysis.
EHR data has numerous potential research applications. Its simplest and generally accepted use is to help researchers with tasks such as assessing study feasibility, facilitating patient recruitment, and streamlining data collection. For instance, EHRs can facilitate pre-screening patients according to designated eligibility criteria such as age, gender, and/or diagnosis.
The second and more complex use of EHR data is when researchers take information previously collected in routine clinical care and reuse that information as source data for research. For instance, modern EHR systems support a broad range of epidemiological research, enabling researchers to evaluate topics such as the natural history of a disease or drug safety and utilization using data already collected.
In fact, EHR data is already being used for research purposes. Analysis of EHR data is an increasingly common approach for studying real-world patient data—for instance, EHR data is used to survey population health, develop prediction models for clinical decision support, and elucidate optimal treatment policies. EHR data has also been used to support observational studies, either using standalone data or by linking to external data sets. In the U.S., the FDA has used EHR data to support post-marketing safety investigations; in Europe, EHR data is used to conduct post-marketing risk assessments.
The use of routinely collected EHR data for research has numerous potential benefits.
Traditional randomized controlled trials (RCTs) are often costly, but using EHR data that has already been captured can significantly reduce administrative costs. One driver of expense in clinical trials is frequently the administrative burden associated with data collection. However, a review of trials utilizing EHR data has found a trend of substantial cost savings in such trials due to the use of fully automated EHR-driven data.
EHR data can also reduce costs by facilitating patient recruitment. Recruiting patients through the EHR allows researchers to prescreen for eligibility before approaching potential trial participants, reducing the effort involved in identifying and enrolling an appropriate patient sample. Poor recruitment is the most frequent reason trials are discontinued before enrolling the required patient sample; using EHR data to facilitate recruitment can generate tremendous cost savings.
RCTs often use strict inclusion and exclusion criteria and take place in standardized settings, which may mean that the results they generate cannot be generalized to all patient populations. However, using EHRs to randomize patient samples allows researchers to conduct large-scale RCTs within the routine care setting—generating results that are definitionally far more generalizable.
EHR data also enable trials to be conducted with larger sample sizes than otherwise possible. These larger samples tend to be more representative of the target population. They also tend to be less subject to inclusion bias than RCTs because EHR data originate from all patients interacting with the healthcare system.
Results from EHR data-powered research can often be obtained more quickly—even in real time—because it utilizes data already being collected. EHR-sourced data also allows researchers to assess outcomes more easily, without having to specifically measure or collect trial-related information in a dedicated data collection system. When research involves outcomes such as stroke, hospital admission, or serious adverse events, that information is typically recorded in the EHR, making it readily available for research purposes.
Together, these benefits mean that RCTs conducted using EHR data sets can help researchers overcome many of the limitations of traditional RCTs; unfortunately, successfully using EHR data sets also poses numerous challenges.
The sheer volume and richness of EHR data, when extracted, often present a formidable challenge for researchers attempting to navigate an entire population’s worth of information. Some of the challenges researchers face when trying to work with EHR data include:
Research has also shown that many of the issues that arise during clinical care, such as the fact that people from minority groups tend to seek health care less frequently, tend to be reflected in EHR data, thus introducing bias into EHR studies.
In addition, much of the untapped potential of EHR data lies in its complex and unstructured nature. EHRs often contain semi-structured and unstructured data, particularly in free-text physician notes. These data can be a goldmine of valuable information, but extracting meaningful insights from these free-text fields is often difficult. Without data from the free-text sections, though, EHR datasets often lack key patient information.
Recognizing these impediments, Veradigm designed the Therapy Focused Solutions to offer a transformative approach to EHR-based research: the introduction of sample cuts per therapeutic area. This product enables a targeted focus on a small set of patient populations associated with specific diseases or conditions. Strategically narrowing the data’s focus significantly increases efficiency, enabling researchers to extract targeted and relevant information without being bogged down by the enormity of the entire dataset. With Veradigm Therapy Focused Solutions, researchers can focus on patient populations specific to their area of interest instead of the entire EHR patient population.
Veradigm Therapy Focused Solutions provide another key advantage as well: Using the power of Natural Language Processing (NLP), this solution unlocks the potential of semi-structured and unstructured data, converting it into structured and actionable information.
Veradigm Therapy Focused Solutions starts with multiple EHR data sources to provide an extensive, nationally distributed patient population. Because Veradigm has direct access to the Veradigm Network of EHRs, we are able to access and analyze unstructured notes that are not routinely offered by other deidentified real-world data providers—on a massive scale. Using NLP, we enrich the EHR data to collect information from semi-structured fields and unstructured physician notes. The process utilizes a statistically certified strategy for deidentification of patient data. The resulting data are integrated into an easy-to-use, industry-standardized format.
With the Cardiovascular Therapy Focused Solution, researchers can analyze data focused on a cohort of patients with CVD—a continually growing dataset of more than 34 million patients from the past 5 years alone. This comprehensive dataset covers a diverse spectrum of diseases, including:
The Cardiovascular Focused Solution product slices the data based on cardiovascular conditions and leverages NLP to extract CVD-related elements. The resulting dataset can help organizations develop novel CVD therapies. It can help improve understanding of current therapies for cardiovascular patients, helping to facilitate improved performance and optimize patient outcomes. The data can also be used to evaluate CVD risk factors in key patient subgroups and develop disease prediction models for daily care.
With Veradigm Therapy Focused Solutions, you can spend less time managing your data and more time on the analysis required to obtain the results you need. Veradigm now offers several Therapy Focused Solutions: cardiovascular-focused, metabolic-focused, immunology-focused, and central nervous system-focused sub-sections of Veradigm Network EHR Data.
Contact us today to learn more about Veradigm Therapy Focused Solutions and how they can help you reach your research goals!
Download the Veradigm Insights Report: Cardiovascular Conditions in 2024
References:
Somberg J. The Importance of Cardiology Research. Cardiol Res. 2020;11(6):355. doi:10.14740/cr1173
insights10 (September 27, 2023). “Global Cardiovascular Drugs Market Executive Summary.” Retrieved February 14, 2024, from https://www.insights10.com/report/global-cardiovascular-drugs-market-analysis/.
U.S. National Library of Medicine. “ClinicalTrials.gov.” Retrieved February 14, 2024, from https://clinicaltrials.gov/.