Author: Nisarg Shah

Exploring Clinical Data and Analytics: The Trends Shaping the Market | LinkedIn Live

LINKEDIN LIVE

Exploring Clinical Data and Analytics: The Trends Shaping the Market

View the event on LinkedIn, which was delivered live on Wednesday, February 21, 2024.

In the rapidly advancing field of clinical technology, pharma enterprises are faced with challenges such as fragmented data, compounded by interoperability issues and inefficiencies. There is a growing need for real-time insights and a unified data platform to enhance clinical decision-making, interoperability, and operational efficiency. 📊

📢📢Watch this interactive LinkedIn Live session to explore how an end-to-end platform approach to clinical data and analytics can enable seamless data integration, improve data quality, accelerate trial timelines, and improve patient outcomes. Additionally, discover the evolving areas in clinical data and analytics and get a view of the outlook for this segment. 🚀

During this event, we dived into questions such as:

  • 💡 What are the existing pharma challenges around clinical data and analytics?
  • 💡 What are the key demand trends and expectations from clinical data and analytics platform providers?
  • 💡 What does the provider ecosystem look like in this segment?
  • 💡 What are some of the emerging or futuristic trends within this segment?

Meet the Presenters

Decentralized Clinical Trial Platforms PEAK Matrix® Assessment 2023 – Update

Decentralized Clinical Trial Platforms  

Decentralized Clinical Trials (DCTs) are transforming the landscape of clinical research by harnessing digital technologies to facilitate remote patient participation. This patient-centric approach not only benefits participants but also ensures more inclusive and robust study results while reducing the workload for investigators. In the post-pandemic climate, a hybrid model is emerging that seamlessly integrates technology advances with traditional site workflows, creating a new era of clinical trials that harnesses the strengths of both. In a funding-constrained environment, DCT platform providers are looking to strike a balance between client satisfaction and cost reduction. They are focusing on delivering comprehensive solutions that include real-world evidence, integration with sensors and wearables, and tools dedicated to enhancing patient engagement and retention throughout the trial process.

Decentralized Clinical Trial Platforms PEAK Matrix® Assessment 2023 – Update

What is in this PEAK Matrix® Report

In this report, we assess the capabilities of 22 platform providers specific to the decentralized clinical trial landscape. These platform providers are mapped on the Everest Group PEAK Matrix®, a composite index of a range of distinct metrics related to a provider’s capability and market impact.
 

In this report, we:

  • Examine the provider landscape for DCTs
  • Assess DCT platform providers on several capabilities and market success-related dimensions

Scope

  • Industry: Life sciences clinical development technology
  • Geography: global

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What is the PEAK Matrix®?

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Life Sciences Smart Manufacturing Services PEAK Matrix® Assessment 2023

Life Sciences Smart Manufacturing Services

In the past, the manufacturing industry primarily focused on designing standardized manufacturing procedures and managing labor and mechanical systems. However, the advent of Industry 4.0 has led to the widespread adoption of technology across various sectors, unlocking numerous benefits. Nevertheless, the life sciences industry has been slow in embracing technology to modernize its manufacturing setups. The pandemic, regulatory frameworks, and the drive for operational excellence are now propelling the adoption of smart manufacturing services.

Life sciences enterprises are striving to unlock benefits such as cost optimization, increased productivity, visibility, and efficiency through investments in vital use cases such as digital twins and predictive maintenance. They are also exploring high-growth opportunities such as sustainable manufacturing, batch-to-continuous manufacturing, and personalized medicine production. As the industry receives investments in smart manufacturing, providers are assuming the role of end-to-end digital transformation partners by collaboratively developing solutions to assist enterprises in their digital journeys.

Life Sciences Smart Manufacturing Services PEAK Matrix

What is in this PEAK Matrix® Report

In this report, we assess 16 life sciences smart manufacturing service providers featured on Everest Group’s Life Sciences Smart Manufacturing Services PEAK Matrix® 2023. Each provider profile offers a comprehensive picture of its service focus, key IP/solutions, domain investments, and case studies. The study will enable buyers to make the right sourcing decisions based on their requirements, while providers will be able to benchmark themselves against their competitors. 

This report features 16 life sciences smart manufacturing service provider profiles and includes:

  • The provider landscape for life sciences smart manufacturing services
  • The providers’ assessment on several capability and market success-related dimensions

Scope:

  • Industry: life sciences
  • Geography: global
  • The assessment is based on Everest Group’s annual RFI process for the calendar year 2023, interactions with leading life sciences service providers, client reference checks, and an ongoing analysis of the IT services market

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Life Sciences Clinical Data and Analytics (D&A) Platforms PEAK Matrix® Assessment 2023

Life Sciences Clinical Data and Analytics (D&A) Platforms PEAK Matrix® Assessment

The pandemic accelerated the need to extract value from data as it led to a substantial increase in data generation from various clinical sources, characterized by high veracity, variety, and volume. As a result, there has been a notable surge in the adoption of clinical data and analytics platforms in clinical development. This has sparked considerable interest in assessing their impact on clinical trials, patient care, treatment outcomes, and health system efficiency. Currently, the industry faces a significant challenge in integrating complex clinical data sources, including Electronic Health Records (EHR), Electronic Medical Records (EMR), laboratory data, Clinical Trial Management Systems (CTMS), connected devices, and Real-world Data (RWD).

Clinical data and analytics platforms offer significant benefits in the realm of clinical development. These platforms enhance data management by facilitating centralized and standardized data collection from various sources, resulting in improved data quality and integrity. Real-time monitoring capabilities enable stakeholders to track trial progress, identify potential issues, and make informed decisions promptly. Advanced analytics tools help uncover patterns, trends, and correlations within the data, providing valuable insights for optimizing trial protocols and treatment strategies.

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What is in this PEAK Matrix® Report

In this report, we assess 14 clinical Data and Analytics (D&A) platform providers. The providers are positioned on Everest Group’s PEAK Matrix®, a composite index of a range of distinct metrics related to the providers’ capabilities and market impact. The study will enable buyers to choose the best-fit provider based on their sourcing considerations, while providers will be able to benchmark their performance against each other.

In this report, we:

  • Examine the provider landscape for clinical D&A platforms
  • Assess clinical D&A platform providers on several capabilities and market success-related dimensions

Scope:

  • Industry: life sciences

  • Geography: global

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Generative AI in Clinical Development: Unlocking Possibilities and Addressing Challenges | Blog

The prospects for Generative AI in clinical development look encouraging. GAI can help speed drug development, improve protocol design, personalize treatment, and bring other benefits. However, the industry must be aware of the risks and responsibly operate GAI. Read on to learn more.

Contact us directly to discuss this topic further.

Given the lengthy, labor-intensive, and expensive process of bringing a new drug to market and conducting a clinical trial, clinical development stakeholders are constantly searching for technological solutions to automate workflows, streamline operations, reduce site and patient burden, and accelerate trial timelines.

Leveraging cutting-edge technology, Generative Artificial Intelligence (GAI) may unlock new horizons and revolutionize the entire clinical development value chain. Let’s explore this further.

GAI works by training underlying large language models (LLMs) on huge datasets containing billions of parameters. For example, ChatGPT is built on GPT-3, a model trained with over 175 billion parameters, far exceeding prior LLMs.

GAI outperforms conventional AI by not only analyzing and interpreting existing data but also generating text, images, audio, video, and other content. The latest innovation, GPT-4, takes this a step further by introducing multi-modality, allowing it to process non-text inputs like images and generate high-quality outputs.

The potential for generative AI in clinical development 

In the clinical development field, the opportunities with GAI are simply too big to ignore. It has the power to accelerate drug development, enhance patient engagement, improve protocol design, personalize treatment approaches, generate synthetic data, and much more.

Let’s take a closer look at the potential applications in clinical development to better understand the role that GAI can play in this domain:

  • Patient recruitment and screening: By scanning a plethora of health and medical records, GAI can reduce the recruitment funnel and better identify suitable patients for clinical trials. The outcome of these interactions can be fed to a validated digital biomarker for the desired indication. This, in turn, can accelerate patient recruitment and improve diversity ratios
  • Synthetic data generation: Synthetic datasets that closely resemble real-world patient data help researchers with limited data or no control group in randomized controlled trials (RCTs). GAI can help conduct simulations, test hypotheses, and accelerate the time to market. With the regulatory push in favor of synthetic control arms, GAI can be instrumental in generating synthetic data
  • Protocol authoring: GAI can optimize the process of authoring protocols for clinical trials. It has the ability to scan through vast amounts of scientific literature, past trial histories, and databases and generate insights on the appropriate endpoints, dosage, patient population, treatment arms, and analysis procedures
  • Patient engagement: AI-driven by LLMs can analyze patients’ medical history data and preferences and create personalized content. GAI-powered digital avatars can significantly improve patient engagement by providing personalized communication and educational information that ensures patients are included in the process and remain engaged and informed
  • Real-time decision-making: During a trial, GAI can continuously monitor patient conditions (through data coming from wearables and sensors) and provide real-time support to investigators and researchers. This may entail preventive interventions, dosage modifications, improving medication adherence, and early detection of adverse events
  • Study Data Tabulation Model (SDTM) transformations: SDTM involves mapping clinical data to a standard structure for regulatory submissions. GAI can analyze data from multiple sources and generate mappings that meet SDTM standards. This use of GAI would provide validation and quality controls, and automate repetitive tasks, expediting the whole process

Figure 1: Prominent use cases and the potential impact of Generative AI in clinical development in the near future

While GAI is generating buzz across industries, like any new technology, its benefits come with challenges. Pharmaceutical enterprises must be aware of the following risks and biases so they can be prepared to address them:

  • Data quality and bias: GAI relies on the quality of training data for generating meaningful outcomes. The data used to train the models can have biases that can lead to disparities in patient recruitment and treatment recommendations. New AI models may not be well-suited to handle diverse languages, dialects, and cultures effectively
  • Data security and privacy: A crucial consideration for using Generative AI in clinical development is patient data security. Ensuring compliance with the ambiguous regulations for digital technologies and AI in clinical development is complicated. AI models should comply with existing regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) and be prepared for potential upcoming ones
  • Ethical considerations: The use of GAI raises ethical questions, especially in using patient information to train the models. Patient data must be handled with strict confidentiality, and consent must be given at all times before using it to train any model
  • Accountability: GAI cannot be held accountable for inaccurate treatment recommendations or a mistake in the protocol design. Completely relying on automated systems raises liability concerns and makes determining accountability challenging
  • Sustainability: GAI models are trained on billions of parameters that require extensive computational resources often managed at large-scale data centers. As the use of GAI models and queries continues to grow, the carbon footprint also rises

Over-reliance on AI models without human intervention for decision-making can lead to unwanted consequences. Domain experts, clinicians, and researchers must be present to validate the outputs and use GAI responsibly while having minimal environmental impact.

Technology advances have always brought disruptions, and GAI is no exception. GAI can revolutionize data management processes and become an invaluable tool in clinical development. By better understanding its risks and biases, pharmaceutical enterprises can use GAI responsibility and reap its full benefits – making GAI’s future in clinical development look promising.

To discuss the future of Generative AI in clinical development, contact Anik Dutta, Nisarg Shah, and Madhur Kakade.

Learn more about the use cases and potential of GAI technology in our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

Decentralized Clinical Trials: What You Need to Know to Unlock Value | Webinar

On-Demand Webinar

Decentralized Clinical Trials: What You Need to Know to Unlock Value

Decentralized clinical trials (DCTs) were a boon during the pandemic. Pharmaceutical enterprises capitalized on the opportunity to conduct remote and virtual trials during a time when isolation was critical. However, in 2023, pharmaceutical enterprises are turning their focus to address new challenges such as scale, data security, and site engagement.

In this webinar, we’ll present insights on the trends pharmaceutical enterprises should expect this year, along with data from our 2023 DCT Platforms PEAK Matrix® assessment to shed light on both the demand and supply side of this market.

Our speakers will discuss:

  • How is the DCT technology market evolving and what does adoption look like across therapy areas and geographies?
  • What sourcing criteria should pharmaceutical enterprises consider for evaluating their DCT needs?
  • What are the supply landscape characteristics and their value propositions?

Who should attend?

  • CEOs, CMOs, CDOs, CTOs
  • Heads of Product Marketing
  • Chief Strategy Officers
  • Chief Commercial Officers
  • Chief Innovation Officers
  • Chief Scientific Officers
  • Heads of Strategy
  • Heads of Analyst Relations
  • Heads of Product Development
  • Heads of Innovation
  • Sourcing Heads
Satija Chunky
Vice President
Shah Nisarg
Practice Director

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