Getting critical breakthroughs in the medical domain is important to improve the healthcare system and make it more patient-centric. However, delays in clinical trials bring several challenges into the picture. The problem areas for the delays in these trials include planning, complex trial designs, and execution. It is seen that 85% of the clinical trials are delayed due to reduced patient enrolment. Often, inefficacy in the selection of sites, operational inefficiencies, and trial complexities lead to low patient enrolment and retention.

This is where artificial intelligence and data analytics solutions offered by consulting companies enter to help the pharma industry overcome these hurdles. Solving the key challenges through the power of data, clinical research consulting and trial processes. Today, multimodal, predictive, and generative AI have transformative potential for the pharmaceutical industry.

Limitations of Traditional Clinical Trials

Fragmented Data

One of the main challenges of traditional clinical trials included fragmented trial artifacts. Data was collected from various sources, which were unstructured and missed several data points.

Manual Effort

It required a huge amount of manual effort to transcribe data from various sources, documents, and systems. It highlighted many other challenges, such as data readiness, and visibility.

Repetition and Error

In traditional clinical trials, the same database is used for multiple trials. It means human resources are occupied with the repetitive work of building a database from scratch in each trial. It is tedious and time-consuming work.

Benefits of Incorporating Artificial Intelligence Solutions in Clinical Trial

Streamlined Patient Data

AI and data analytics consulting play a crucial role in providing the required database to analyse patient data, medical research, and electronic health records. These records help in matching patients with clinical trial criteria. Deep insights are drawn from varied factors such as location, demographics, and site performance history, aiding in patient enrolment and retention.

Efficient Data Management

AI solutions offered by consulting companies can help in efficient data management. The data generated from clinical trials can be easily analysed and organized through an agile data management system. It helps in identifying clinical patterns in a truly short time, which would have taken a long time if done manually. The research and development team can quickly access the real-time data, which is error-free and can help speed up the trial process.

Cost-Efficiency

Implementation of AI in the clinical trial process brings down the overall cost of medicine development. This is because it eliminates the need for extensive manual labour by automating mundane work. It saves on labour and operational costs of the pharmaceutical company. Besides this, efficient data analysis identifies any gaps in the clinical trials, which helps in efficient resource allocation and risk mitigation.

Regulatory Compliance

There is strict regulatory compliance in the clinical trials, which the pharma industry needs to adhere to. AI based data analytics consulting helps immensely in clinical development by eliminating any hurdles. Real-time monitoring, documentation, and audit trials of clinical development bring transparency and help ensure regulatory compliance to eliminate trial delays.

Ensure Better Results

Real-time insights gathered help in providing customized treatment to patients after clinical trials. Biomarker identification, predicting treatment response, and maintaining clinical trial protocol improve the success of the trial.

Artificial intelligence is the future of drug discovery and development, which will help in business scaling and patient-centricity in the pharmaceutical industry. It is key to increasing the number of successful trials in a cost-effective and time-bound manner. The pharmaceutical industry can benefit from the deep insights offered by the system that can be implemented at the planning, optimizing, and execution levels.