Remarque Systems

Making Patient Data Work for You: Spotlight on the Remarque Medical Monitor

The success of any clinical trial rests on data. Data drives decisions at every important point throughout the trial, informs risk management directives, and proves that effective risk management measures were implemented.

Yet, data alone is not enough.

To be useful, that data must not only be high quality, it must be presented in such a way that it can be easily analyzed — ideally, in real time, so that researchers can make appropriate decisions that advance trials, while managing risk and supporting patient safety.

The Remarque Quality Management System (QMS) meets this need.

Remarque draws clinical data from a trial’s electronic data capture (EDC) systems, so it is always accurate, always verifiable. Then it enables sponsors and CROs to examine that data from three distinct vantage points: the patient level, the site level, and the study level, drilling down so you can not only visualize trends and flag anomalies, but understand and react to the forces underpinning them. In this blog post, we’ll illustrate how the Remarque QMS allows you to maximize your patient data through the Medical Monitor to help you maintain control even as you scale studies across hundreds — or thousands — of patients, relieving stress and improving outcomes.

Patient Data That Works for You

Remarque’s system-agnostic risk-based monitoring platform draws in data from multiple sources — electronic medical records to mHealth wearables, central lab systems to issue management systems — then magnifies the data’s utility by quickly identifying study integrity risks and anomalies. The application’s three different monitors – the Medical Monitor, the Central Monitor, and the Statistical Monitor – allow you to filter this data at the patient, site, and study levels respectively, then refine it based on specific subsets within each category.

With the Medical Monitor, you can filter individual patient data by parameters such as demographics, vital signs, conditions, medications, and adverse events.

Screenshot of patient data dashboard showing exceeded risk thresholds

Sponsors view all data collected on a patient during the trial — labs, vitals, and endpoints — as a dashboard of sparkline graphs. The red outline highlights any data value that exceeds the trial-specific threshold, making it simple to identify outliers and manage risk.

Real-time Analysis of Multiple Patient Data Views

Look at an individual patient, look at patients from a given site, or look at all patients within a given study. Then configure their data, using multiple filters to drill down to reveal precise results to specific questions — for instance, focusing on adverse events, then sorting by vital signs and treatments administered on a certain day.

Filtering down to individual patient data

In this example, a sponsor begins with adverse events experienced by all trial participants, then adjusts filters to examine diastolic blood pressure, total calcium, and abdominal pain.

Viewing individual patient data

The sponsor then focuses on a single patient to find that the patient was given naproxen from October 10 to October 25, and the reason for it.

Viewing naproxen data for an individual patient

Machine Learning Bolsters Insights

The Remarque QMS also includes machine learning capabilities to cull millions of data points in near real time, then identify issues and risks humans cannot. For instance, the system can flag missing informed consent forms, identify potential fraud by research sites, and provide a comprehensive electronic audit trail while you focus on what matters. In addition, you can customize Remarque’s workflows to fit your individual trial demands.

At the patient level, you can compare individual results with the study mean to understand how a specific patient’s results compare with their peers in the study. The system can also chart an algorithmically predefined cluster of patients similar to an individual patient. These comparisons mean you can quickly identify patients who may be at risk, as well as those whose results are superior to the mean, then probe to discover potential reasons those patients lie outside the norm. As a result, you can:

  • Reduce risks and improve outcomes for individual patients
  • Iterate and adapt a trial in real time based on ongoing insights
  • Meet regulatory demands for risk-based monitoring—and have the data trail to prove it

DBP individual, patient, and similar patients

Translating Data into Action

In any given trial, you have years of work and millions of dollars at risk — and any error or delay compounds those risks. Far more importantly, the safety and hopes of your clinical trial participants are at stake. To protect them, you need access both to the highest quality data possible and to the insights that data can deliver. Harnessing the power of machine learning and the ease of the Remarque Medical Monitor can help you see beyond the top line data, to understand results in real time — and take immediate action. To find out more, request a demo today.