Value-Based Data: Unlock Business Value from Clinical Data with An Outcomes-Based Approach

Value-Based Data: Unlock Business Value from Clinical Data with An Outcomes-Based Approach AUTHOR: MOULIK SHAH, FOUNDER AND CEO, MAXISIT

1

Clinical trials generate zettabytes of data.

But if it’s inaccessible or inconsistent, it’s not consumable.

Data management and analytics platforms can help pharmaceutical and biopharmaceutical companies integrate, standardize, and analyze clinical data for streamlined study execution. Technology can also help pharma/biopharma derive business value from clinical data. However, implementing new software only solves part of the problem.

To extract quantifiable business value from clinical data, organizations must couple advanced technology with a targeted data management strategy. That strategy includes identifying data that relate to business outcomes, applying analytics to gain useful information from those data points, and translating that information into insights that inform business strategies.

In this article, we discuss the importance of outcome-based data management in clinical trials, as well as how to develop a strategy based on relevant data.

1. Unlocking Business Value from Clinical Data

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323266 https://www.zs.com/insights/how-to-talk-about-the-value-of-your-data-strategy

Data Strategy in Action With so much data available, organizations must develop a strategy to determine which bit of information will best answer clinical, commercial, and business questions.

The strategy can thus best incorporate that data which has been collected.

Let’s use patient-reported data as an example. An ePRO app collects valuable patient-generated data to support clinical trials. By examining that app’s usage data, developers gain information that can help them determine which features to enhance and which to retire. This also helps them assess whether patients are engaging with the app, which helps develop tactics around patient retention and adherence.

However, some sponsors collect the wrong data or collect it in an inappropriate format, which hinders their ability to improve the product. While it’s easy enough to collect data, the challenge lies in knowing what to collect and how to convert it into valuable insights.

This is the type of information one would include in an outcome-based data management strategy. The strategy would include similar analyses for the various teams involved in advancing research and development.

2. Unlocking Business Value from Clinical Data

The Philosophy Behind an Outcomes-Based Approach

With respect to clinical trials, an outcomes-based approach refers to achieving objectives such as demonstrating treatment efficacy and safety, meeting predefined endpoints, and gathering patient-reported outcomes. This comprehensive approach involves assessing survival outcomes, biomarker responses, and health economic impacts, contributing to informed decision-making throughout drug development.

Ultimately, these strategies aim to ensure regulatory compliance, support precision medicine, and generate real-world evidence for a holistic evaluation of a treatment's effectiveness. Furthermore, they could be intertwined.

With an outcomes-based approach, sponsors reverse engineer data collection. They focus on data points that can bring about a desired outcome and collect that or use secondary information to infer desired outcome without having to collect the data itself. That’s in contrast to a bottom-up approach of collecting any and all data and figuring out what to do with it later. To develop this type of strategy, sponsors must consider the business value associated with each data point and move the clinical trial forward in accordance with this value.

3. Unlocking Business Value from Clinical Data

A Collaborative Approach to Data Strategy Input from across departments is essential for a comprehensive data strategy. It also enables teams from across the organization to benefit from the strategy’s results.

Divisions to involve include: • Study design and planning • Clinical operations • Clinical data management • Data analysis and reporting • Market access • Information technology It is important to provide training where necessary to ensure users can access and leverage necessary data within their respective roles. By identifying the decision points within the organization and enabling study teams to access and use the necessary data, organizations can unlock the true business value that it is intended to deliver.

4. Unlocking Business Value from Clinical Data

Identify and Standardize The first step to developing a data strategy focused on business value is to establish goals. Key performance indicators (KPIs) are measurable, quantifiable metrics used to evaluate and assess performance over time. Clinical trial KPIs include numbers and timelines tied to first patient in (FPI), last patient in (LPI), patient enrollment, screening, and screen-fail rates. KPIs related to business value could include budget, study outcomes, and on-time performance. Prescriptions, revenue, and supply chain effectiveness are metrics to consider for commercialized products.

With KPIs established, it’s time to standardize. Organizations running multiple studies can limit complications by settling on a unified naming convention for files and data fields, as well as a standardized data format. With these parameters established, organizations will gain more useful, comprehensive insights. Clean, standardized data also helps streamline operations issues and mitigates risk of errors caused by duplicates.

Once analyzed, data must be presented to stakeholders in a manner that’s relevant and easy to read. Customized dashboards enable users to view relevant KPIs, as well as drill down to review specific site, study, or budget-related data.

5. Unlocking Business Value from Clinical Data

Get Predictive Retrospective data helps organizations better plan future studies based on past results.

Predictive analytics take that one step further, using machine learning algorithms to project future outcomes.

Examples include: •

Assessing patient data to determine how different populations would react to certain drugs or biologics

•

Examining electronic medical records (EMRs) to find patients who may be eligible for future clinical trials

•

Examining existing trial data to predict adverse reactions or efficacy before moving to the next phase

6. Unlocking Business Value from Clinical Data

How to Talk About Data Strategy As clinical trials become more complex and data intensive, data managers have a more prominent seat at the table. When communicating with leadership, focus on the value created rather than digging deep into the nuances of data science. This is even more important when you need buy-in on a technology investment.

A few angles to keep in mind include: Enhance decision making

AI-based techniques help uncover patterns that can inform business decisions.

Certain performance data, for example, may help leaders set an optimal price or develop a more targeted commercialization strategy.

Improve clinical operations

Automation can fuel substantial productivity gains by taking over routine, repetitive tasks like data entry. Focus on how this technology can lead to bottom-line improvements due to shorter timelines and reduced labor.

Generate new revenue streams

Leveraging real-world data can inform submissions for expanded indications available for existing drugs. Other data strategies can help determine the value of developing a digital health companion app or the viability of partnering with an early-stage start-up.

Endpoint on the Business Value of Clinical Data A data management strategy focused on clinical and business outcomes is an effective path to successful trials and profitable outcomes.

By identifying relevant data sources, establishing meaningful KPIs, and collaborating across teams, organizations can make better use of all the data at their disposal to deliver across-the-board value.

7. Unlocking Business Value from Clinical Data

About MaxisIT MaxisIT’s purpose-fit and intelligent clinical data analytics platform helps improve clinical trial performance, mitigate risk, and optimize clinical outcomes. We provide a centralized and reliable source of truth for diverse data types from various sources, giving life sciences companies real-time insight to shorten cycle time and increase return on investment.

Incorporating an end-to-end clinical data pipeline from intake to visualization, MaxisIT's solutions are powered by AI/ML and metadata-centric approaches. Our impressive portfolio of over 3,300 clinical trials and an unparalleled 100% customer retention rate affirm the quality and reliability of our services.

Moulik Shah is a passionate healthcare technology entrepreneur and the visionary CEO of MaxisIT, where he has been at the forefront of leveraging technology to transform pharmaceutical and life sciences clinical trials.

Moulik Shah

Founder & CEO, MaxisIT

His dedication to improving patient outcomes and his leadership in directing patient-centricity, patient diversity, interoperability, and real-world-data-led collaborations have been at the core of his vision of an integrated healthcare ecosystem based on effective use of data and analytics platforms.

He has been instrumental in driving innovation and progress in the industry.

Under Moulik’s leadership, MaxisIT has become a leading provider of clinical data and analytics which is driving real-world impact in the pharmaceutical and life sciences clinical trials.

https://www.maxisit.com/

© 2024 MaxisIT LLC. All Rights Reserved. This article contains proprietary and confidential information.