October 22, 2021 - Parul Saini, Webmedy Team
The adoption of electronic health records (EHRs) in a healthcare environment is increasing rapidly around the world, yet the use of EHR data in clinical research has lagged.
Fast-forward to the present day, when countless digital technological advances, data warehouses, and hardware and software programs have made our jobs more repeatable, less erroneous, and faster. Yet, we still have not made significant progress on interoperability between disparate electronic systems such as routine, seamless, and secure data transfers from electronic health records (EHRs) to electronic data capture (EDC) systems.
According to a study, EHR optimization to support data interoperability could improve the value of pragmatic clinical research.
Clinical Research, carried out on humans (healthy or sick people), concentrates on improving knowledge of diagnosis, sickness, new treatments, and medical treatments to deliver quality care.
There are two types of Clinical Research:
They improve the knowledge of the disease and its evolution over time. They are carried out within the framework of the follow-up of the patients in the reference centers. They also contain natural history studies.
They provide scientific evidence of the efficacy and safety of a new drug, a new care device, or new management in the context of an illness. This is the necessary step for a new chemical to become a drug or a new medical device to be marketed.
Clinical research is essential to test the safety and efficacy of new treatments in any population but there are few challenges in conducting clinical research. Some of these challenges are:
We know how tightly regulated the healthcare and pharma industry is. Meeting compliance obligations is unsurprisingly among the top challenges getting in the way of timely and cost-effective clinical trial completion. According to a survey by ICON and pharma intelligence, about 43% of respondents named regulatory approval delays as the most common challenge.
Especially as trials move globally, they become increasingly constrained by their complexity. The need to coordinate between multiple sites, partners, and vendors is becoming exceedingly challenging. Even steps as fundamental as version control on consent form documentation can turn into major deviations from protocol - a regulatory disaster - if data isn't correctly stored and organized.
Recruiting and retaining enough participants to complete a trial is among the biggest sources of delays and trial failures. Site selection is a critical first step to patient recruitment, and some of the most important parameters for site selection are patient access, infrastructure, and suitability for the given treatment type.
Clinical trials have been growing increasingly complex for years. As the complexity, geographic diversity, and rate of change in trials increases, it becomes more difficult to make decisions and identify potential issues in real-time. Roles and responsibilities among staff members evolve quickly, and lack of visibility into data, as well as dealing with disparate data sources, makes it tough to respond quickly.
When trials are underway, ensuring that sites are well-monitored, and data is being accurately captured, is one of the most important priorities of trial management. Ensuring patient perseverance and completion of the clinical trial protocol requires active data analysis and monitoring to track compliance, including monitoring of endpoint data, deviations, and any adverse events.
Doing this manually, as many pharma companies still are, leads to difficulty updating information quickly and aggregating data from multiple sources and across different systems platforms. Lab results, imaging, and health records are difficult to integrate quickly, especially with site directors relying on manual spreadsheet methods. These trials should continue only if the potential benefit in continuing the research exceeds the public health risks (including that of coming to the hospital or research site, and its potential for exposure, if necessary), and if there is sufficient staff to conduct the trials without sacrificing the quality of clinical care.
While changes to study conduct may be necessary to enable the continuation or introduction of clinical trials, certain concerns with these potential changes must be addressed. By leveraging EHR data, the industry can transform how it conducts clinical research and delivers health care in the future.
Interest in using EHRs as an electronic data source (eSource) for traditional regulated randomized clinical trials has been ongoing for more than two decades. eSource is a research-based term that refers to source data in electronic format and includes the reuse of EHR data and a myriad of other electronic sources of data such as patient-reported outcomes, diaries, and wearable devices. EHR optimizations can enable organizations to respond nimbly to public health issues, deliver evidence-based treatments, and address patient-specific care.
Reflecting on the swivel chair interface that clinical research professionals face each day and the promise that interoperability holds for EHR and EDC systems, it stands to reason that countless manhours can be saved. Even where half of the clinical trial data is derived from EHR sources, this translates into the following potential incalculable savings:
It takes all of us to embrace the new laws and mandates for interoperability. Putting the framework of the standards into actionable success stories will continue to take time.