The Role of AI in Clinical Data Management

A clinical data management system is responsible for managing the massive amounts of data collected during clinical trials. Artificial intelligence (AI) with clinical data management systems have a symbiotic relationship. The more data a system has, the better the AI will function. AI is capable of simplifying, optimizing, and automating many data management processes. 

Effective data management is a mission-critical function of clinical data management. The vast majority of clinical research organizations have recently begun adopting ML and AI-powered solutions in clinical data management. Clinical teams have to spend long hours managing the data cleaning process instead of analyzing the data their systems collect. Manual data management is helpful, but it takes more time and budget when compared to AI-powered data management systems. 

Today we’re going to discuss the role of AI in clinical data management and ways in which it optimizes the clinical trial process. 

Reasons to Use Artificial Intelligence in Clinical Data Management

AI reveals undiscovered potential for accelerating clinical trials. It can reduce costs and speed up every stage of the drug development process. Moreover, AI can be effectively involved in different parts of drug discovery. Exscientia, a pharmatech company in Oxford, uses AI-powered clinical molecules to target the behavioral and psychological symptoms of Alzheimer’s disease. 

Even though the pharma and clinical research industries lag in adopting modern technologies, in some way, the COVID-19 pandemic has served as an accelerator for the pharmaceutical industry. It forced the industry leaders to innovate their processes, rethink their approaches, and adopt new ways of working at a speed never seen before. 

AI and Big Data are the most intricate technologies entering the clinical trial and pharmaceutical industries for 2023. According to a report by GlobalData, more and more pharmaceutical and clinical research companies are hiring AI and big data engineers to take advantage of these emerging technologies.

Data Optimization 

Data collection is the primary goal of research and clinical trials. Thanks to modern technologies, researchers can collect even more valuable information to access the effectiveness of a treatment plan or a drug. But data collected doesn’t mean data used. It can result in many key data points getting lost or ignored during the research period. 

Here’s where AI comes in handy. The role of AI in clinical research is to help researchers at every phase of the data cycle or in any process that requires aggregation, storage, and data retrieval. AI-powered algorithms can identify data types, find connections between datasets, and even recognize knowledge via natural language processing (NLP) algorithms. 

The application of AI algorithms leads to a higher ROI over time since the data systems can be trained to highlight and retrieve more data. The following algorithms can also improve the processes of data cleaning. Hence, AI  can reduce collection errors and system hiccups to a minimum. 

Multiple Data Sources

Over the years, the pharmaceutical industry has generated tons of data from observational studies and clinical trials. The amount of data is growing exponentially and can be used in multiple research fields. 

Effective digital infrastructure powered by AI algorithms could enable a continuous stream of clinical data. In addition, consolidating all data can enhance collaboration and provide more insights over vital metrics from multiple sources. Since AI requires access to more data to learn, access to multiple databases could significantly impact medical research. 

But there are several challenges to consider, such as appropriate governance, ownership, privacy, and security. It gets even more complicated when multiple stakeholders are involved.

Faster and Better Results 

The primary goal of using AI and ML in the clinical data management process is to get pharmaceutical treatments to market faster. This, in turn, helps reduce the price of research for businesses and the waiting time for patients. 

AI algorithms can analyze millions of pharmaceutical compounds and find the most effective solution much faster than a human researcher. Take a look at the Human Genome Project as an example. It took scientists 13+ years to create a remarkable advancement in fighting genetic diseases. With the adoption of artificial intelligence, it would take up to 24 hours. 

AI and ML tools are fast, so they can spot problems early on and prevent an entire trial from being corrupted by bad data. Thanks to natural language processing (NLP), computers can analyze spoken and written words. This allows for searching through things like doctors’ notes and pathology reports for more valuable information. 

Data Structuring 

AI and ML-powered tools, like, natural language processing, text analysis, and sentiment analysis, are capable of making unstructured data quantitive. The following AI-powered model can analyze the text from any PDFs or faxes sent and provide more insights into the research and its results. All the unstructured data that doesn’t fit into any pre-defined data models cannot be stored in a relational database and, as a result, cannot be processed or used.

Examples of unstructured data:

• Media 
• Text files 
• Emails
• Communication data 
• Social media and websites 

The adoption of AI and ML for insights from structured and unstructured data will increase the efficiency of clinical data management and facilitate well-informed decision-making.

How Unicsoft Helps 

Pharmaceutical companies and clinical research organizations face a rising demand for fast-track drug development and research processes where the fast and smart Clinical Data Management system is one of the greatest concerns. 

Big data, ML, and AI help in clinical trials and streamline the data collection and management processes. AI can leverage more data from each patient and provide more reliable, faster, and patient-centric clinical trials. However, the following technologies cannot solve every problem facing clinical research. They can allow for accomplishing tasks in a few hours that would take months or years when doing so manually. Hence, researchers will be able to deliver the right therapies and treatments to patients faster and save more lives. 

FAQs

1. How is AI used in clinical research?

AI is used in various aspects of the research process and advances medical knowledge. Some of the aspects to which it applies include but are not limited to:

• Data analysis: AI algorithms can analyze large amounts of patient data, such as electronic health records (EHRs), medical images, genetic data, and clinical trial data, to derive further insights from complex data sets.
• Clinical trial design: AI can reduce cost and time by analyzing historical data, identifying patient populations, and predicting outcomes.
• Drug discovery and development: ML algorithms can speed up the ealy stages of drug development by analyzing pharmacological data to identify potential drug candidates and predict their efficacy.

2. What is the use of AI in clinical trials big data analysis?

AI in clinical trials enhances the efficiency, accuracy, and effectiveness of the process by analyzing large and complex datasets. In particular, it applies to data cleaning, data integration from multiple sources, predictive modelling, and data structuring, among others. 

3. What are the challenges of AI in clinical trials?

Some of the issues that might occur when using AI in clinical trials are data quality and availability, data privacy and security, validation from regulatory bodies, and integration with existing workflows. Oftentimes, guidance from researchers and top tech experts is unavoidable for a successful project completion.

4. How can Unicsoft help me with AI-powered clinical data management?

As an AI consulting and development company, Unicsoft can power your healthcare transformation by building and integrating even the most complex AI solutions for your business. We will take over all data preprocessing issues, ensure regulatory compliance, and bring a transparent solution that will support medical researchers in the clinical data management process. Check out more AI solutions for healthcare Unicsoft provides and see our related case studies.