Remarque Systems

EHRs: A Robust Source of Clinical Trial Data at Your Fingertips

The clinical trial model — collecting data on patients in a trial, then publishing results based on those data — was established more than a century ago. Though modern trials often use electronic platforms to manage their data, the data collected has remained largely the same; trial investigators now simply capture electronically exactly what they used to capture on paper.

It is time for a change.

Today, people’s entire health history — from laboratory test results to fitness tracking — is part of their electronic health record (EHR). It seems only logical for clinical trial data to be included in this overall record, too; clinical trials are, after all, integral to their participants’ healthcare. Conversely, historic and ongoing EHR data can provide critical information with which to expand and guide clinical trials. What better way to gain insight into a drug’s real-world performance than by monitoring patients’ real-world health? Moreover, by utilizing the information already being tracked in existing records, clinical trials can conduct far larger-scale studies for a fraction of the cost.

Say, for instance, that you want to study cardiovascular disease in patients with diabetes. You could spend millions of dollars running a study with 15,000 participants — or you could harness the actual EHRs of 250,000 patients, thus magnifying both the depth of the study and the resultant confidence of regulators and healthcare professionals alike.

It seems clear that the inclusion of EHR data can transform the data-collection paradigm, offering a powerful evolution in clinical trials. Based on recently issued guidance, the FDA agrees.

FDA recognizes EHRs as a source of clinical trial data

In a guidance paper published in July 2018 [PDF], the FDA notes that the prevalence of EHRs affords opportunities to improve both data accuracy and clinical trial efficiency by incorporating relevant portions of EHR records into trial databases. The paper suggests that a wide range of data, from clinical notes and pharmacy records to radiology and laboratory results, can be aggregated and analyzed. It also describes three specific scenarios in which such data may be of particular value:

  • Providing investigators access to real-time data during a study
  • Facilitating post-trial follow-up of a drug’s long-term safety and efficacy
  • Enabling long-term follow-up of large numbers of patients in prophylaxis studies

The paper then outlines recommendations for selecting appropriate EHR systems, ensuring the quality and integrity of the data collected through those systems, and certifying that such data meets FDA standards for inspection, recordkeeping, and record retention.

Success requires a source-agnostic quality management platform

With these new, broader guidelines — and potential new sources of data — a source-agnostic quality management platform becomes even more crucial. Sponsors need a platform that can aggregate diverse data sets, apply appropriate rules, and then analyze the information in order to deliver a complete portrait of the patient and a comprehensive view of the trial activity.

While studies often measure improvement using rating scales such as an ACR 20 scale to score arthritis pain or a Hamilton Depression Scale when studying mental health, few typical EHRs would include such data. Yet, with appropriate rules, a quality management platform can translate EHR data into a format that enhances the data collected by a traditional electronic capture system, enabling the sponsor to lead with real-world data without losing quality. Indeed, the resulting trial data is far richer; not only is the study not relying on a single source for information, it can cross-check data quality between sources. 

Machine learning expands ongoing, comprehensive risk assessment

With the right quality management platform, the inclusion of EHR data also provides the potential to strengthen overall risk assessment.

When organizing quality management for a clinical trial, investigators first identify the potential risks the patients face — both standard risks and those specific to a certain therapeutic area, such as cardiovascular events in diabetes. The right platform will not only pull information from EHRs, ePROs (electronic patient reported outcomes), and health devices, it will harness machine learning to continuously build the risk knowledge base, refining the list of risks that should be monitored and managed throughout the life of a study for every disease state.

That not only fortifies the trial that is in progress but bolsters future trials the sponsor may undertake in the same therapeutic area.

The right platform already complies with FDA requirements

Of course, sponsors and investigators are primarily concerned that clinical trial data be both clean and concise — and above all, that it meets the needs of regulatory authorities.

That means using a system that can supply an audit trail, one that can blind the data and comply with other HIPPA mandates, one that addresses FDA inspection and record-keeping requirements — all in addition to being able to harmonize and analyze EHR data in conjunction with other trial-collected data.

Yet, with such a system in place, both the volume and depth of data available to trial investigators grows exponentially, and the cost of such data relative to the size of the overall trial population has the potential to decrease. That can only benefit long-term public health. Learn how the Remarque QMS can help you incorporate EHRs into your trial data — schedule a demo today.