Clinical Trial Analytics: Transforming Medical Research

Key Takeaways:

1. With the help of RWE, researchers can easily check the long-term safety of drugs. This gives insights into adverse events that may have been missed during randomized clinical trials.
2. Big data allows researchers to reduce the time taken to start clinical trial analytics. Because, it allows them to select the right kind of patients that are a perfect match for the trial, and not waste time on the wrong patients.
3. AI and machine learning technologies conduct real-time monitoring of patients during trials. This gives valuable insights into patients’ health status and adverse reactions to drugs.

Clinical trial analytics is a process of deciphering the vast amount of data captured in clinical studies. This process involves the analysis of diverse data resources such as EHR, wearables, laboratory data, and genomic information.

Pharmaceutical companies end up spending billions of dollars on clinical trials to enhance drug efficiency, patient outcomes, and safety. Around 40 percent of the US pharma companies‘ research budget is approximately $7 billion per year, and the cost of patient recruitment for trials is about 40 percent.

Clinical trial analytics allows researchers to gain real-time insights into drug trials. Based on this they make decisions on whether a specific drug is ready to market or not.

Here’s a gist of the role of analytics in clinical trials:

1. Data Visualization: This refers to creating visual representation of data to facilitate decision-making and interpretation.
2. Predictive Modeling: Here, historical data and machine learning are used to predict patient responses and trial outcomes.
3. Statistical Analysis: This refers to applying biostatistical methods to scrutinize the safety and efficacy of treatments.
4. Data Management: This involves handling study data with accuracy and consistency in mind, along with data storage and data backup processes in mind.

Leveraging Real-World Evidence in Clinical Trial Analytics

Real-world evidence (RWE) refers to information derived from real-world data (RWD) such as claims data, EHRs, lab reports, registries, etc. RWE is increasingly used alongside conventional clinical trial data to offer a comprehensive understanding of treatment effects.

RWE is anticipated to leave a transformative effect on clinical trial analytics and reporting. With the help of RWE, researchers can easily check long-term safety of drugs. This gives insights on adverse events that may have been missed during randomized clinical trials (RCT).

Furthermore, real-world evidence is used to analyze and identify patient groups that respond positively to treatment based genetic markup. Real-world data decreases the resources and time required for data collection. This increases cost-effectiveness and speeds up the research process, including clinical trial analytics and reporting.

By utilizing readily available data, researchers can conduct trials at minimum budget, without compromising on the validity and rigor of their findings. Apart from this, researchers and manufacturers can leverage RWD to detect treatment durability and recheck prescribing guidelines, assuring that patients receive evidence-based care.

Role of Big Data in Clinical Trial Analytics

With surplus data available on fingertips, pharma companies can bring in big data to conduct clinical trial analytics. However, there’s one glitch and that is regarding the quality of data. The issue can be resolved by inducting IoMT technology because it can transfer data wirelessly without human intervention. Additionally, cloud computing is a plausible option to store data, making it easier for the researchers to manage data.

Furthermore, big data plays a pivotal role in patient recruitment for clinical trials. In a traditional patient recruitment process, researchers would reach out to providers and explain them about the clinical development program, and ask them whether they have patients who are willing to participate in the trials.

So, based on this, healthcare providers would highlight five to ten patients. However, out of the lot researchers would get only one or three. But, with big data in the picture, clinical researchers can heatmap the world to locate patients suffering from specific conditions, all with the help of EHRs and fitness trackers. They can reach out to those patients and conduct trials.

With big data, researchers can reduce the time taken to start clinical trial analytics. Because, it allows them to select the right kind of patients that are a perfect match for the trial, and not waste time on the wrong patients. Less time also means less money for drug development.

How AI and ML Contribute to Clinical Trial Analytics?

AI and machine learning technologies conduct real-time monitoring of patients during trial. The data obtained is processed and analyzed by artificial intelligence. It offers valuable insights of patients’ health status and adverse reactions to drugs.

Using AI and machine learning tools in clinical trial analytics enhances patient safety and ensures timely interventions. Thereby, optimizing trial outcomes and mitigating risks. It can also contribute in predicting drug interactions and examining pharmacokinetics profiles. This results in a deeper understanding of drug efficiency.

Furthermore, AI and ML facilitate drug repurposing, drug design, and lead compound optimization. It speeds up the drug development process by analyzing trial results much quicker than a human. Quick analysis means reduced expenses and higher chance of regulatory approval. In addition, artificial intelligence is a handy tool for predicting drug properties such as bioavailability, solubility, and toxicity.

Conclusion

Clinical trial analytics field is rising exponentially and it holds the promise of transforming the clinical research sector. By harnessing the power of AI and machine learning, big data, and real-world evidence, clinical researchers can design efficient trials and analysis. This will help to accelerate the development of new therapies and ultimately improve patient outcomes.

If you’re looking for software that will help you in clinical trial analytics and reporting, then you’re at the right place. We at Arkenea can help you develop pharmaceutical software that will allow you to conduct clinical trials. We also offer drug trial management software that will automate the process of preclinical drug trials. Along with this, if you’re a pharmacovigilance company, then we’ve got just the right software for you. All you need to do is get on a consultation call with us today.