Accelerating tomorrow’s cures: Faster Enrollments for faster trial completions

Accelerating tomorrow's cures:
Faster Enrollments for faster trial completions


Clinical trials are at the forefront of medical advancement, playing a pivotal role in bringing new treatments and therapies to patients. However, despite their importance, patient recruitment remains a significant bottleneck in the clinical trial process. The challenges associated with identifying and enrolling eligible patients are complex and multifaceted, often resulting in delayed trials, increased costs, and a limited pool of diverse trial participants. To overcome these obstacles and accelerate tomorrow’s cures, the healthcare industry is turning to AI-powered patient recruitment solutions.

Challenges in Patient Recruitment

The numbers tell a compelling story of the difficulties clinical trials face in recruiting the right participants:

  • Missed Deadlines: Approximately 80% of clinical trials fail to meet their enrollment deadlines. Delays in patient recruitment can have a cascading effect, postponing the introduction of potentially life-saving treatments.
  • Terminated Trials: In a distressing statistic, 42% of clinical trials are prematurely terminated due to low enrollments. This not only wastes valuable resources but also squanders the research and development efforts invested in these trials.
  • Patient Dropouts: An alarming 30% of patients drop out of clinical trials before study completion. This attrition undermines the validity of the trial results and can be attributed to various factors, including stringent inclusion and exclusion criteria, distance to trial sites, and patient dissatisfaction.

AI-Powered Patient Recruitment: A Game Changer

AI-powered patient recruitment is a game-changer, leveraging artificial intelligence to expedite the recruitment process in clinical trials. Three key advantages stand out:

  • Enhanced Access: AI proactively identifies eligible patients for trials by analyzing vast datasets and patient records, broadening the pool of potential participants, and making trials more accessible to those who can benefit.
  • Reduced Timelines: AI improves the success rate of trial enrollments, swiftly identifying eligible patients and reducing recruitment time, ultimately lowering costs.
  • Empowered Physicians: AI equips physicians with a searchable repository of ongoing trials, enabling faster and more accurate patient recommendations, enhancing their role in the recruitment process. This innovation accelerates treatment development, making healthcare more accessible and efficient.

How AI-Powered Patient Recruitment Works

AI-powered patient recruitment solutions follow a structured approach to streamline the process. They aggregate clinical data from multiple sources, including electronic health records, hospital databases, and clinical trial-related information. Here’s a simplified overview of how they operate:

  • Data Collection: Aggregate clinical data for patients from multiple sources and clinical trials related information, creating a comprehensive patient and clinical trial database.
  • Natural Language Processing (NLP): An NLP Engine extracts key information on inclusion and exclusion criteria from clinical trial protocols and extract patient related data from hospital or healthcare data sources.
  • Fuzzy Matching: AI-based fuzzy matching engine matches patients to clinical trials based on inclusion and exclusion criteria and patient related information, enhancing precision and speed.
  • Recommendations: The system suggests clinical trial sites with target patient pools based on matching scores, streamlining recruitment.
  • Assessment: The AI assesses clinical trial designs to estimate their potential for finding matching patients. This innovation accelerates treatment development and elevates the well-being of patients.
Keeping in mind how AI-powered patient recruitment solutions have the potential to revolutionize the clinical trial landscape, TCG Digital’s TrialXch emerges as a comprehensive solution designed to address the challenges of patient recruitment in clinical trials. By harnessing the capabilities of the AI-powered analytics platform, tcgmcube, TrialXch empowers you with advanced algorithms and personalized recommendations that expedite patient recruitment. This enhanced efficiency and accessibility in patient recruitment play a pivotal role in accelerating the development of new treatments and improving the well-being of patients worldwide. With this cutting-edge tool at your disposal, you can confidently expect faster trial completions and the swift delivery of tomorrow’s groundbreaking cures.

Revolutionizing Patient Recruitment in Clinical Trials with AI

Revolutionizing Patient Recruitment
in Clinical Trials with AI


Patient recruitment in clinical trials has long been a challenging and time-consuming process, causing delays and increasing costs. Clinical trials come with stringent eligibility criteria, and potential participants often have reservations about safety, the time commitment required, or a simple lack of awareness about available trials. However, the advent of artificial intelligence (AI) is poised to revolutionize patient recruitment, offering a more efficient, cost-effective, and patient-centric approach.

The Power of AI in Clinical Trial Recruitment

AI has the potential to analyze vast amounts of data from various sources, including electronic health records, claims data, and registries, to identify patients who meet the complex eligibility criteria for clinical trials. Additionally, AI can help match patients to trials that best align with their individual needs and preferences, offering a win-win scenario for both patients and trial sponsors.

Addressing Inefficient Patient Recruitment

AI’s ability to analyze both structured and unstructured patient data from diverse sources is a game-changer for clinical trial recruitment. This technology can identify eligible candidates who meet complex inclusion and exclusion criteria. For example, a study published in the Nature Digital Medicine journal in 2023 demonstrated that AI-powered patient recruitment can reduce costs by up to 70% and accelerate clinical trials by up to 40%. This efficiency in patient recruitment not only benefits the trial sponsors but also enables quicker access to potentially life-saving treatments for patients.

Customized Visual Dashboards: A Window into Insights

Customized visual dashboards are more than just data presentation tools; they are the windows through which sponsors gain real-time access to invaluable insights. These user-friendly interfaces provide dynamic displays of complex data, offering real-time updates and customizable views. What sets them apart is their ability to enable sponsors to break down data silos and synthesize massive volumes of disparate data points into one single source of truth that reveals actionable insights. This breakdown of data silos fosters collaboration, enhances transparency, and empowers stakeholders at all levels to make data-driven decisions with confidence.

Imagine a clinical trial manager tracking patient enrollment on a real-time dashboard, while a safety officer monitors adverse events on the same platform. Customization ensures stakeholders see precisely what they need to make informed decisions.

Overcoming the Diversity Challenge

One of the persistent challenges in clinical trial recruitment has been limited diversity, particularly in underrepresented minority populations. AI can help address this issue by optimizing recruitment through network analysis. By doing so, it ensures that trials, especially those focused on rare diseases, have diverse and representative participant pools. This, in turn, leads to more generalizable treatment outcomes and a broader understanding of the trial’s impact on different demographics.

Reducing High Dropout Rates

High patient dropout rates, which can be as high as 30%, have been a significant issue in clinical trials. These dropouts not only lead to unreliable results but also cost overruns for trial sponsors. AI can mitigate this problem by effectively matching patients to trials, reducing the burden of manual screening. Furthermore, AI’s continuous engagement with patients can help minimize dropouts and improve participant retention, resulting in more robust and reliable data.

Enhancing Data Utilization and Site Selection

In many cases, patient data remains underutilized, missing out on potential recruits for clinical trials. AI addresses this issue by increasing identification rates by up to 50% through enhanced data utilization. Moreover, it can analyze enrollment patterns to optimize site selection and recruitment strategies, ensuring the most efficient use of resources.

AI's Transformation of Clinical Trials

Artificial intelligence is ushering in a new era for clinical trials by making them more accessible, faster, economical, and patient-focused. It smartly leverages data to match patients to trials efficiently, benefiting both patients and trial sponsors.

One notable solution leading this transformation is TCG Digital’s TrialXch, an AI-powered platform revolutionizing clinical trial recruitment. TrialXch utilizes AI to efficiently match patients to appropriate trials by analyzing complex health data. By optimizing the identification of eligible candidates, site selection, enhancing diversity, reducing dropout rates, and ensuring regulatory compliance, TrialXch is making clinical trial recruitment more accessible, swift, cost-effective, and patient-focused. Ultimately, it benefits all stakeholders involved in clinical trials, furthering the advancement of medical science and improving patient access to innovative treatments.

In conclusion, artificial intelligence is reshaping the landscape of clinical trial recruitment, addressing age-old challenges such as delays, high costs, limited diversity, and dropouts. This innovative technology promises to usher in a new era of patient-centric and efficient clinical trials, bringing us closer to breakthroughs in healthcare and treatments that can benefit us all.

Achieving End-to-End Visibility in Clinical Trials: The Power of Analytics and Dashboards

Achieving End-to-End Visibility
in Clinical Trials:
The Power of Analytics and Dashboards


In the world of clinical trials, achieving real-time end-to-end visibility has become more than just a trend; it’s a critical necessity. Modern clinical trials are complex endeavors involving numerous stakeholders, generating massive amounts of data that reside in disparate systems. To navigate this complexity and make informed decisions, pharmaceutical companies are turning to advanced data analytics and customized visual dashboards.

The Demand for End-to-End Visibility

Clinical trials are no longer isolated studies but rather complex ecosystems involving pharmaceutical companies, research organizations, regulatory bodies, and healthcare professionals. Each trial generates vast datasets, from patient recruitment to safety monitoring, often residing in isolated databases. This fragmentation creates blind spots and hampers decision-making.

However, end-to-end visibility is more than data integration; it’s about having a comprehensive view of the entire clinical trial landscape. This approach empowers stakeholders at all levels to proactively identify risks, refine strategies, and make data-driven decisions in real-time.

The Power of Advanced Data Analytics

At the core of achieving end-to-end visibility is advanced data analytics. These tools can process large datasets, analyze intricate relationships, and extract valuable insights. Sophisticated algorithms and statistical models can predict potential issues, improving resource allocation and patient safety.

For instance, predictive analytics can forecast patient recruitment rates, while machine learning algorithms can detect adverse events early. These capabilities are vital as clinical trials become more global and complex.

Customized Visual Dashboards: A Window into Insights

Customized visual dashboards are more than just data presentation tools; they are the windows through which sponsors gain real-time access to invaluable insights. These user-friendly interfaces provide dynamic displays of complex data, offering real-time updates and customizable views. What sets them apart is their ability to enable sponsors to break down data silos and synthesize massive volumes of disparate data points into one single source of truth that reveals actionable insights. This breakdown of data silos fosters collaboration, enhances transparency, and empowers stakeholders at all levels to make data-driven decisions with confidence.

Imagine a clinical trial manager tracking patient enrollment on a real-time dashboard, while a safety officer monitors adverse events on the same platform. Customization ensures stakeholders see precisely what they need to make informed decisions.

The Future of Clinical Trials: Data-Driven Visibility

The future of clinical trials revolves around data-powered, end-to-end visibility. The benefits are compelling: shorter timelines, enhanced patient safety, cost reduction, and better decision-making. Regulatory bodies are also beginning to support the use of advanced analytics and dashboards in clinical trials.

In conclusion, achieving end-to-end visibility in clinical trials is not just a possibility; it’s a necessity in today’s complex pharmaceutical landscape. By leveraging advanced data analytics and customized visual dashboards, sponsors can confidently navigate modern trial challenges. The organizations that embrace this data-driven paradigm will lead the way in medical innovation.

The Critical Role of Data Management Systems in Clinical Trials

The Critical Role of Data Management Systems
in Clinical Trials


In the world of clinical trials, data is at the heart of the quest for safer and more effective treatments. However, as trials grow in scale and complexity, the data they generate from various sources has surged to unprecedented levels. Traditional data management methods are no longer sufficient for efficiently handling this deluge. This is where robust data management systems step in, playing a pivotal role in modern clinical trial success.

Historically, clinical data management relied on fragmented, manual processes and isolated data silos. Yet, in today’s data-driven landscape, where trials generate vast and diverse datasets, this approach no longer holds. Modern trials demand a shift towards advanced data management solutions.

Centralized cloud-based data management systems

Enterprises are increasingly adopting centralized, cloud-based data management systems to meet these challenges. These systems serve as the central hub for data, offering a unified platform for seamless data integration. This integration fosters collaboration and facilitates real-time data access and analysis.

Enhancing efficiency through automation

Automation is another game-changing aspect of data management systems. By automating routine tasks like data entry and validation, these systems enhance efficiency, ensure data consistency, and expedite data management. In clinical trials, where data accuracy is paramount, automation is a game-changer.

Ensuring Data Quality and Compliance

Standardization and governance are crucial components of modern data management. Standardization ensures consistent data collection across sites and trials, simplifying comparisons and analysis. Governance, meanwhile, guarantees compliance with regulations and data security standards, safeguarding patient confidentiality and trial integrity.

Harnessing Real-Time Insights

One of the most transformative features of modern data management systems is their ability to provide real-time analytics. Researchers and sponsors can access and analyze data as it is generated, enabling swift, informed decisions. This empowers them to refine protocols, optimize patient recruitment, and accelerate therapy development.

In conclusion, data management systems are now indispensable in clinical trials. They not only streamline data processes but also unlock data’s full potential. As trials become increasingly data-centric, these systems are pivotal in advancing medical research, ensuring data accuracy, and contributing to innovative treatments. In an era where data holds paramount importance, data management systems stand as the cornerstone of clinical research.

Revolutionizing Aircraft Maintenance with Digital Twins

Revolutionizing Aircraft Maintenance
with Digital Twins

How Technology is changing the Aviation Industry


Airlines will continue to adapt to the evolving new normal, operate under strict budget constraints, and prioritize cash preservation to tackle the uncertainties and challenges. So, maintaining the aircraft at optimal operation levels is critical to run the airlines.

In spite of being new generation aircraft and having efficient and longer lasting engines, the cost of maintenance continues to increase due to shortage of resources, scarcity of spare parts of older aircrafts, and inflation. As such, airlines are shifting attention towards leveraging the latest innovative technologies, which were not widely available in the recent past, to tackle increasing challenges in maintenance operations. Among these latest cutting-edge innovative technologies, digital twin is playing a critical role in the space of aircraft maintenance.

Digital Twin (DT)

A digital twin refers to a computer-based replica of a tangible entity, system, or operation that exists in the physical world. However, it is more than just 3D models. Digital twin implements all of the data and models required to accurately represent the different aspects of a product or process in order to recreate how the product and process will behave in a changing environment in the real world. A critical prerequisite of the digital twin notion is that it must be a dynamic and a constantly updated representation of the real product, or process in question.

As per E. Glaessgen and D. Stargel “A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin”

Aircraft Maintenance and Digital Twin

Not all components/parts in an aircraft are good candidates for a digital twin. As implementation of DT is complex and costly, it is important to focus on parts/processes that are critical to business operation and will provide ROI. An aircraft has multiple components, and so, only a few qualify as candidates for DT e.g., engine, landing gear, hydraulic system, fuel system, and aircraft body.

Digital twin applies multiphysics and multiscale modeling techniques along with AI/ML models, which analyzes the behavior of aircraft components in different electrical, thermal, mechanical, magnetic, and chemical conditions. The sensors generate datapoints and the models perform the what-if analysis, which captures how different components individually and as a whole will behave in different conditions without changing the properties of the physical aircraft components.

Below is the illustration of the DT of aircraft and fleet:

The Digital Twin Advantage

Transforming maintenance
By collecting data generated by IoT sensors installed in the aircraft parts, digital twins along with AI/ML analytics models can forecast unscheduled maintenance. Additionally, digital twins can also predict the conditions when the parts/component could fail. By providing prior insights into the status of the aircraft components, digital twins facilitate early detection of a problem.
Increased safety
Leveraging digital twins and combining AI/ML enabled predictive analytics can assist in the earlier detection of a problem by capturing prior insights of the status of the aircraft components. The end result is an improvement in safety, which ultimately makes air travel safer and more reliable.
Lower financial impact
Digital twin enables the operator to predict probable maintenance failures before the breakdowns can occur. It gives lead time to the operator to minimize the downtime, increase aircraft utilizations, and profitability. By replicating the model in Digital twin, air carriers can forecast maintenance costs.
Improved supply chain
With the application of digital twin, operators can manage the inventory more effectively by appropriately ordering the parts that would fail as forecasted by the analytics model.
With advanced technologies like Digital Twin, aircraft maintenance has become even more effective and it is impacting the aviation industry significantly. To learn more about innovative technology in aviation, visit

Challenges of Migrating Legacy Applications to AWS

Challenges of Migrating Legacy
Applications to AWS


In a world of rapidly changing technology, many organizations still rely on legacy mainframes to keep their most critical operations running. These age-old systems have been tuned and customized to meet the functional requirements of the business, and as a result, have become locked-in to vendors over the years. However, maintaining and supporting these systems can be a challenge, as resources are scarce, and the lack of an integrated testing environment can limit flexibility, add risk, and increase test time. In addition, legacy technology can encounter problems with maintenance, support, improvement, integration, and user experience.
Recently my team and I were discussing how to help our clients unlock the value of their industrial and manufacturing data. These industries often wrestle with terabytes of time-series data from a myriad of sensors, machines, processes and external sources. Each data source could potentially include different features with various formats, have non-rationalized time intervals and be generated from various proprietary technologies. The challenge of making this data available on a platform where workers can exploit the data and discover its hidden value can be overwhelming. Here is where the vision of AI Democratization often hits its first significant roadblock.

Managers who are tempted to feel elated at the prospect of having access to all the data they could ever wish for, soon realize it is a data sword of Damocles* hanging by a thread, ready to snap and bury them if they can’t find a way to unlock its value.
Richard Westall’s Sword of Damocles, 1812
* The parable of the sword of Damocles teaches us that no matter how good someone’s life may appear, it’s difficult to be happy living under existential threat.

So, what's the solution?

The answer lies in application, information, and data migration. By migrating to the cloud, organizations can improve their operational efficiency, reduce IT costs, improve performance, and take their business to the next level. Modern technology solutions can introduce automation to manual processes, which are prone to errors, and enhance reporting and rich featured UI and rules engine, allowing businesses to manage data more efficiently, and changes will be reflected in real-time.

The ultimate objective

The ultimate objective is to sunset the legacy system with minimal disruption to the business and transition towards a more robust and scalable information technology platform to support current and future business needs in a cost-effective and collaborative manner. This also involves designing a common technology platform for operational applications to minimize data redundancy, decrease the cost of building, integrating, and maintaining new and existing applications.

However, migrating from legacy mainframes to modern technology solutions is not without its challenges.
The key challenges during the migration process includes:
  • Rewriting application architecture for the cloud
  • Complexity of the integration of data, systems, and processes
  • Compliance and security
  • Dealing with hybrid networking setups
  • Investing in people and tools needed to migrate successfully
  • Training users on the new systems
To overcome these challenges, businesses need to have a clear set of guiding principles in place.
Consider these solutions and guiding principles:
  • Create a reference architecture for the legacy application to migrate to a cloud-native architecture on AWS.
  • Compliance & Security, Hybrid connectivity – AWS Accounts/VPCs, including TGW, Direct Connect Gateway, multi region peering, Landing Zones, VPCs AZs Subnets, Security Groups, IAM role
  • Data Security – Encrypted at Rest (AWS KMS), Encrypted at transit (SSL/TLS)
  • Real Time Transactions and Streaming, Messaging Integrations– SNS, SQS, MSK, Kinesis
  • Adapters – On-Prem to cloud protocol bridge
  • Use Serverless components/services as much as possible – Lambda, StepFunctions for workflow
  • AWS API Gateway – Lambda functions are invoked through API Gateway
  • Computation – Application container in EKS
  • ALB – EKS pods are invoked using ALB
  • AWS Secret Manager – Store credentials securely

In conclusion, the migration from legacy mainframes to modern technology solutions, such as AWS, is no longer an option but a necessity for businesses that want to remain competitive and agile. While the migration process may seem daunting, it can be successfully achieved with careful planning and execution, along with adherence to guiding principles. By leveraging AWS’s cloud-native architecture and services, organizations can improve operational efficiency, reduce costs, and enhance their overall competitiveness. With the right strategy and tools, the migration journey can result in a more robust and scalable information technology platform that meets current and future business needs.