The clinical research and drug development industries are undergoing a rapidly evolving new era of innovation, driven by exciting new advancements in artificial intelligence (AI) and machine learning (ML) capabilities. The convergence of big data and data analytics with healthcare shows particular promise in the field of personalized medicine, also known as precision medicine. According to Grandview Research, the value of the global personalized medicine market was valued at US$538.93 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 7.20% from 2023 to 2030. Today, personalized medicine is closely intertwined with genomics, given the practice of using biomarkers or biomarker signatures to predict drug responses in patients.

Clinical trial sponsors and tech-forward contract research organizations (CROs) are increasingly recognizing the value of leveraging complex analysis of large datasets to identify data science-driven solutions. This article explores the significant role of data analytics in shaping the future of personalized medicine, as well as spotlights Medidata Detect, a revolutionary new data and risk surveillance tool that harmonizes data into a single location.  

 

Overview of Personalized Medicine in Clinical Research

Personalized medicine is an emerging new therapeutic practice that approaches disease treatments by considering the individual characteristics of a patient, rather than generalizing them across a population. This typically involves using specialized diagnostic tests to understand and potentially predict a patient’s disease type, risk, and response to treatment using biomarkers. Examples of biomarkers include genetic changes, disease severity, or lifestyle habits, all of which represent measures that can be used to determine an individual’s unique disease characteristics or risks.

Personalized medicine has several potential benefits, including the following: 

• Fewer side effects by avoiding treatments that don’t work for the patient 
• Lower healthcare costs through more effective use of treatments 
• Earlier disease diagnosis and prevention with the help of biomarkers 
• Better-designed clinical trials by selecting patients likely to respond to the treatment

This field is still undergoing development, but currently, its applications are most commonly seen in oncology, where systemic cancer therapies are being tailored to a patient’s genetic makeup. Given the reliance of personalized medicine in clinical research on our grasp of human genomics, the growth of this field depends heavily on the emergence of new computational solutions using data analytics.  

 

How Big Data and Analytics are Crucial to Advancing Precision Medicine  

Precision medicine is an approach to patient care that allows physicians to select treatments most likely to help patients based on a genetic understanding of their disease. However, personalized care is still undergoing some development, and to make it a widespread reality, clinicians require a massive amount of data. Data analytics tools are needed to handle and make sense of such large volumes of information. By combining large datasets of medical records and biomarker registries, AI-based data analytics can be used to classify patients into different subtypes, helping with disease prediction and diagnosis. Clinicians, sponsors, and CROs can use these tools to gain insights that can be used to explore different aspects of personalized medicine, including diagnosis, prognosis, and pharmacogenomics. Therefore, the use of computational solutions and data analytics platforms to leverage Big Data has significant potential to reshape the future of precision medicine and clinical research into targeted therapeutics.

There are four major types of Big Data analytics:

1. Descriptive analytics is used to convert data into useful information for understanding and analyzing healthcare decisions, outcomes and quality, as well as making informed decisions.

2. Predictive analytics is used in proper diagnosis and for appropriate treatments to be given to patients suffering from certain diseases. 

3. Prescriptive analytics leverages health and medical knowledge in addition to data to guide informed decision-making in personalized medicine and evidence-based medicine.  

4. Discovery analytics can be used to discover new drugs, previously unknown diseases and medical conditions, alternative treatments, and more.  

 

Medidata Detect: Leveraging Data Analytics to Improve Clinical Trials

Medidata Detect is a powerful data and risk surveillance technology that was developed to enhance the quality and safety of clinical trials. By collecting and harmonizing data from multiple different sources, this platform is helping to break down operational and data source silos commonly used in clinical trial research. This ensures a more efficient approach to managing trials for sponsors, CROs, and other key stakeholders. Medidata Detect also offers advanced analytics and role-based capabilities to data managers, medical monitors, and clinical operations teams, thereby improving patient safety, data quality oversight, and risk management. It enables much quicker data insights through near real-time data refreshes and optimizes resource utilization by supporting a shift away from the expensive, time-consuming risk surveillance method of on-site monitoring. Medidata Detect has a particularly strong potential for assisting sponsors and CROs execute decentralized trials, providing flexibility and actionable insights accessible from anywhere. 

 

Pioneering Innovation: TFS HealthScience is the First CRO to Implement Medidata Detect

In November 2023, the Sweden-based global clinical trial company, TFS HealthScience, announced it would be the first CRO to implement Medidata Detect into the company’s existing data surveillance and risk monitoring processes. This platform is designed to analyze, harmonize, and detect patterns and discrepancies in data from multiple sources compiled into one location, providing a convenient comprehensive view of clinical trials to drive efficient workflows. The introduction of Medidata Detect is expected to help address data inefficiencies in the industry and enhance data quality, risk management, and decision-making, thereby accelerating clinical trial success. This strategic investment in Medidata’s revolutionary data analytics tool signifies TFS HealthScience CRO‘s commitment to data quality, patient safety, and technological innovation. 

 

The Future of Clinical Research: Opportunities and Challenges with Data Analytics

To fully realize the potential of data analytics to improve clinical trials and advance fields like personalized medicine, there are several challenges posed by Big Data that must first be addressed. First, healthcare decisions can often require immediate action, necessitating regular monitoring of data and sufficient infrastructure to gather and analyze information. Privacy concerns are also a consideration; the risk of data breaches adds a layer of complexity to the use of data analytics in healthcare. Some tools may also not be designed to align adequately with existing decision-making structures in clinical research, which only further adds to their burden. Lastly, attempting to analyze large datasets faces the significant challenge of data silos in clinical research; data is often split across different entities and formats, making it difficult to build a cohesive database. Medidata Detect was developed to address this challenge in particular and enables more convenient data surveillance and risk monitoring with large volumes of information in clinical trials.

However, despite the challenges, big data offers various opportunities in healthcare that can be grouped into four main categories:  

1. Improving the quality of healthcare services 

2. Supporting the work of medical personnel 

3. Bolstering scientific and research activities 

4. Enhancing business and management processes

Data analytics enables physicians to make better diagnoses, detect diseases at earlier stages, as well as analyze human genomes to develop personalized treatment strategies, among others. The insights it provides can also streamline processes involved with risk prediction, patient health management, and analysis of patient profiles for preventative care approaches. Furthermore, for a pharmaceutical sponsor or CRO, big data supports the development of new drugs by allowing the analysis of all data, from both clinical trials and real-world datasets. From a business perspective, data analytics can help reduce clinical trial costs for sponsors, increase efficiency in financial operations, and eliminate unnecessary medical activities and procedures. 

 

Conclusion

In conclusion, technological advancements in data analytics tools hold significant potential to drive innovations in personalized medicine, making treatment more efficient and effective for patients worldwide. Companies like TFS HealthScience CRO and Medidata are at the forefront of this exciting development, representing the transformative landscape of leveraging Big Data in clinical research. 

 

About TFS HealthScience CRO

TFS HealthScience is a global CRO that supports biotechnology and pharmaceutical companies throughout their entire clinical development journey. Learn more here about our partnership with Medidata to learn how the Detect platform is changing the landscape of data analytics in clinical research. Visit our website to learn more about the data-driven solutions TFS can offer for your next clinical trial, or connect with a TFS representative today!