Remarque lands patent for risk-based quality management platform

By Jenni Spinner

- Last updated on GMT

(metamorworks/iStock via Getty Images Plus)
(metamorworks/iStock via Getty Images Plus)

Related tags Risk-based monitoring Data management Quality assurance Quality control Clinical trials software

The technology is designed to enable connection to any source data system, enabling connectivity in identifying, monitoring, and managing trial risks.

When a regulatory body comes knocking on a clinical research operation’s door looking for information, it is beneficial to be ready with a quick, thorough response. With an increased focus on risk-based monitoring, trial teams need to be able to comply rapidly and be prepared to go back into the archives for data from months, even years, ago.

Remarque Systems has secured a patent for its risk-based quality management (RBQM) platform, designed to enable users to better pinpoint and avoid risks, and to get a more solid hold on data management. Michael Arlotto, president and founder of Remarque Systems spoke with Outsourcing-Pharma about the company’s newly patented RBQM platform, how the technology has advanced, and how their technology could benefit clinical research operations.

OSP: Could you please talk about the importance of risk-based quality management (RBQM), and how the technology has evolved in recent years?

MA: The term 'risk' covers a range of potential issues. It could mean risk to the patient, data quality, and overall trial execution. In risk-based quality management, investigators take a systematic approach to identifying and mitigating these risks; they not only define potential risks but prioritize them, then proactively create mitigation and contingency plans.

This approach is an expectation of ICH’s Good Clinical Practice guidelines, quality standards that are accepted internationally for the design, conduct, data collection, and results-reporting for clinical trials.

Technology is critical to RBQM’s success, simplifying data gathering and analysis. But of course, not all technology does the same thing.

OSP: What have been some of the challenges in implementing and running effective RBQM technology in clinical settings?

MA: Most companies have ways to analyze their clinical trial data and identify potential risks. However, that analysis is often after the fact—often quarterly, but sometimes even retrospectively at the end of a study as they are cleaning the data for regulatory review. Either one may be too late for a patient who was at risk—and may also be too late to adjust trial execution strategies that could impact the trial outcome.

Further, the audit trail is a mess because the data and subsequent actions are not integrated into a single system. When sponsors meet with regulatory agents, they must present the data, then show separately how any issues were resolved. They might use tracking logs, a spreadsheet, or even a series of emails tracing discoveries, directives, and resolutions. It can be an organizational nightmare.

OSP: Please tell us about Remarque’s RBQM platform, and how it helps users overcome some of these issues.

MA: Remarque Systems is the industry’s first-ever patented, trail-audited, real-time RBQM platform. It’s a comprehensive clinical trial management system; any source data system can plug into the platform—electronic data collection systems, electronic medical records, mHealth outputs, lab tests, etc. Having everything in one place supports an all-inclusive approach to identifying, monitoring, and managing risk.

All analysis happens in real time, so sponsors immediately know if there is a risk and can take swift action. Centralizing all the data also makes it easy to collaborate with teams on both risk management and compliance issues. That may be important for an individual patient’s safety. It can also help identify and end error-prone processes, improving the quality of trial data.

As we said above, most technology typically used for RBQM requires actions to be taken outside the system. With Remarque Systems, that is reversed; all the actions can take place within the system. Because everything is centralized and recorded within the system—data storage, risk identification, communications, actions—there is a built-in audit trail. That difference was the crux of our patent.

OSP:  Please tell us more about the risk identification and the audit trail.

MA: At the start of a trial, the sponsor identifies a series of potential risks, and programs them into the platform. For each trial, a checklist delineates precisely when and why a monitor should review the data to ensure that everything is happening correctly. A time- and date-stamp records every time the data is examined. 

In addition, as new data enters the platform, the system searches it in real-time, using machine learning to analyze it and sounding an alert whenever it encounters a possible issue, based on the pre-identified risks. That serves two functions:

  • It signals the researcher to examine the situation, presenting the problem in a meaningful way.
  • It creates an audit trail, recording the issue, the alert, and the subsequent actions taken by the researcher. This is invaluable to the research team if and when the study is audited; all the necessary information is already organized and instantly available.

And, of course, the system never takes the evening off; all this happens 24/7, which greatly increases safety, especially compared with traditional protocols that may monitor data quarterly, or worse, only once at the end of the trial.

OSP: Please tell us more about Remarque’s analytics.

MA: Remarque Systems uses artificial intelligence (AI) and machine learning to fuel its in-process analytics. Together they deliver effective risk prediction, detection, analysis, and management, using parameters that investigators tailor to each trial’s objectives (as we discussed previously).

Our system is designed to identify fraud, missing data, inconsistent data, and data anomalies.

AI can rapidly sift through data, pinpointing all of these issues in near-real-time. That enables on-the-fly data interrogation, and it enables the end user to visualize the data in any configuration they choose.

Our system fully supports compliance with ICH guidelines and ensures sponsors can demonstrate CRO oversight. 

OSP: What else would you add about this platform, RBQM, or other projects that Remarque has in the works?

MA: Any clinical trial generates a lot of data, but mHealth devices that are helping fuel the rise in decentralized clinical trials (DCTs) have increased that data output exponentially. As DCTs become more mainstream, sponsors are going to need a way to manage the increased risk and data complexity. Remarque Systems can do that. The system can provide all of the patient and site data, in one place and in a linear manner.

Though we’ve touched upon this in our responses above, this final point about our platform and why it matters to researchers and patients bears repeating. Historically, critical trial data resides in many different databases and applications; these silos make it extremely difficult and inefficient to view trial progress, ensure patient safety, or keep on top of data quality. Remarque Systems counters that by seamlessly bringing together all of the data, then layering on the ability to manage and monitor both unknown and known risks in a documented manner.

Related news

Show more

Related products

show more

Saama accelerates data review processes

Saama accelerates data review processes

Content provided by Saama | 25-Mar-2024 | Infographic

In this new infographic, learn how Saama accelerates data review processes. Only Saama has AI/ML models trained for life sciences on over 300 million data...

More Data, More Insights, More Progress

More Data, More Insights, More Progress

Content provided by Saama | 04-Mar-2024 | Case Study

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data in a single location

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Related suppliers

Follow us


View more