Next-level tech elevates clinical data management: AiCure

By Jenni Spinner

- Last updated on GMT

(Paper Boat Creative/iStock via Getty Images Plus)
(Paper Boat Creative/iStock via Getty Images Plus)

Related tags AiCure Artificial intelligence machine learning Data management data analysis

A leader from the artificial intelligence solutions firm discusses how advanced analytical tools can help researchers make the most of mountains of data.

In recent years, the clinical research industry has made significant progress in collecting all sorts of highly useful data from studies. Another challenge is determining the best way to analyze this influx of data, in order to come up with useful insights that can be applied to the discovery and development of novel treatments.

Outsourcing-Pharma discussed advanced data management with Ed Ikeguchi, CEO of AiCure, who shared how artificial intelligence (AI), machine learning (ML), and other tools can be used to help researchers get the most from their data.

OSP: Could you please share your perspective on how data management has changed/shifted in clinical research over the past several years?

EI: Today’s technologies allow us to collect data around the clock from patients wherever they are. This not only helps to improve access to trials but also increases the amount of data at a sponsor’s disposal to derive meaning from.

This data presents a great opportunity to get at the heart of a trial’s performance and how patients are responding. But analyzing it can also be a daunting task, especially as sites are faced with an increasing number of patients and there’s more data than ever before from often nuanced sources and new technology.

However, new AI-driven innovations in the clinical trial space are making it easier to solve the challenges posed by the influx of new data and help weed through it to extract actionable insights. This technology is changing how studies are run by equipping sites with quality data that empowers them to make informed decisions and run their trials more efficiently.

From increasingly robust remote trial solutions facilitating stronger patient engagement to new capabilities leveraging AI that help manage and organize the data flowing in, innovations in data management are now providing sites with the tools they need to optimize study performance.

OSP: How are the demands placed on site/sponsor staff in that regard becoming more complex?

EI: Especially as clinical trials become more decentralized thanks to remote monitoring technology, pharmaceutical companies need to manage more patients outside of the clinic and stay one step ahead to anticipate any changes in a patient’s behavior or response to treatment. With the increase in data, there’s a growing need to leverage advanced data management platforms to gain meaningful patient insights and empower sites to provide personalized care from afar.

Data management tools can give sponsors clearer lines of sight into how their studies are performing and how they are progressing towards answering the clinical questions they were designed to answer. It also gives them the means to more effectively collaborate with sites, provide support as needed, and train them to optimize their use of data.

Through holistic, intelligent data capture and optimization, sponsors will better understand if their trial is on track and if not, what course correction is required so they can stay ahead.

OSP: What technological solutions are available to help trial teams manage the data they collect and disseminate?

EI: Advanced data ingestion and visualization platforms can help sites and sponsors manage and analyze the large influxes of data flowing in, and give them a holistic view of their trial’s health to drive timely risk mitigation.

By offering sponsors a single, customizable platform view of their trial data, from patient behavior to individual site performance, this technology answers critical questions needed to optimize a trial, such as how a particular site is performing and how engaged their patients are, empowering data-driven decisions to mitigate potential issues while it still has an impact on the study outcome. These types of tools can cut through the noise by spotting trends, identifying where to allocate resources, and minimizing risk.

OSP: How can AI, ML, and other high-end data-management technologies help with the arduous task of managing patient-behavior data, site-management info, and other types of data?

EI: In terms of site management, advanced AI can specifically help with data management through its predictive capabilities, which help sponsors foresee scenarios likely to take place at certain sites and allow them to make informed site selection decisions as well as allocate resources accordingly for any that are underperforming. Having a single, configurable dashboard that illustrates the effectiveness of a site’s patient intervention and engagement tactics can help sponsors optimize patient management and optimize site performance.

By aggregating different types of patient-level data, such as ePRO, adherence, and digital biomarker data, sponsors can obtain clarity regarding the actual performance of the treatment, predict a patient’s future behavior, and optimize future recruitment. Using AI to predict a patient’s ability to adhere to a trials’ protocols based on previous behavior can help guide timely interventions, and help sites focus on patients who may be less engaged. When it comes to recruitment, sponsors can use this data to appropriately stratify patients who may need more support with those who are better positioned to contribute quality data to answer the questions the study is asking, ultimately ensuring balance across study arms.

OSP: You mention that data intelligence can empower sponsors to focus on the “right data.” Could you please explain how that works, and what you mean by the ‘right data’?

Ed Ikeguchi, CEO, AiCure

EI: AiCure Data Intelligence’s predictive capabilities and visualization functionalities help sponsors cut through the noise and focus on the data that matters most to their study in a digestible layout. For example, sponsors can review data and create a profile of each site, allowing them to quickly and easily see which sites meet performance criteria for a given study.

Further, sites can predict a specific patient’s ability to adhere to a trial’s protocol based on their previous behavior and stratify patients who may need more support. So that sponsors can visualize and make sense of this data, Data Intelligence provides holistic overviews, which can be easily configured and customized depending on their needs, ultimately empowering them to make better decisions.

Being able to view the ‘data health’ of the study in real time not only helps optimize patient engagement and resource allocation but also serves as an indication of the trial’s ability to live up to the scientific and clinical rationale behind its design.

OSP: Specifically, how can AiCure’s tech/service offerings contribute to improved data management for clinical research teams?

EI: AiCure’s Data Intelligence offers a single, customizable platform view of a trial’s data. It helps with data management through its ability to mitigate risk both at the patient and site level, extract meaningful insights from multiple data sources, and improve recruitment strategies.

By providing comprehensive overviews of patient dosing behavior and unique site insights to sponsors, they are equipped with the insights they need to make informed decisions about which sites would benefit from intervention to optimize trial performance. When faced with large amounts of data, Data Intelligence can provide comprehensive, consolidated overviews in an easy-to-use interface.

With the ability to aggregate the data, marry it with dosing information and deliver predictive, actionable insights, Data Intelligence is uniquely positioned to help sponsors and sites manage their data and run better trials.

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