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As discussed in Part 1 and Part 2 of AI in Clinical Trials, to process a large and continuously flowing stream of data, the pharma industry will need to employ an equally swift platform to ingest, standardize and manage the data, i.e. a holistic clinical data management platform. With the help of AI, MaxisIT’s Clinical Trial Oversight Platform ingests data from different sources, aggregates, and stores them into a repository. It also runs analytics without the need for coding and delivers actionable insights.
With improvements in electronic data capture, human errors in data capture will be eliminated or at the least be reduced drastically to enable instant integration with databases. Such seamless data management should reduce the amount of time and manual effort put into clinical data management processes.
AI will also help in reducing the overall burden of clinical data management by generating queries and reducing unnecessary and low-impact queries. This reduction in unnecessary queries will give clinical study stakeholders more time to concentrate on higher-value clinical tasks.
Modernize the clinical development process by integration with MaxisIT’s AI-based Clinical Trial Oversight platform
At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical trial operations as well as patient data, allows efficient trial oversight via remote monitoring, statistically assessed controls, data quality management, clinical reviews, and statistical computing. Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring.
MaxisIT’s Clinical Trials Oversight System (CTOS) enables “data-driven digital transformation” by its complete AI-enabled analytics platform from data ingestion, processing, analysis to in-time clinical intelligence by establishing the value of data; which empowers clinical stakeholders to mitigate risks or seize the opportunity in the most efficient manner at a reduced cost.
In Part-1 we discussed the problem statement and the focus areas for clinical development. We also concluded that quick access to relevant information decides the efficiency of a clinical trial. Let us now see how AI can help.
An end-to-end clinical data management platform powered by artificial intelligence is the right choice for streamlining, overseeing and managing trials in a coordinated way. With the help of a platform, study stakeholders can have defined role-based access and personalized views to data which makes it easy to monitor KPIs and stay on top of their tasks.
AI can predict risky sites
AI can predict risky sites by matching real-time data to historical benchmarks like drop-out rates and flag processes accordingly. Early red-flags makes it easy for Managers to keep the clinical trial on track and within budget. Such ability to find insights from big data would have been a tedious task for humans and would have consumed a lot of time but not for an AI platform. These insights would be on the dashboard by the time you make yourself a cup of coffee and this becomes instrumental in meeting the timelines and getting the drug faster to the market.
AI can tackle the need for Source Data Verification (SDV)
Source Data Verification (SDV) is one of the most important but time-consuming activities taken up during a trial. With all the constant manual checks to ensure compliance, it is also a major contributor to the cost of a trial. An AI-enabled platform can keep a tab on all types of inputs including data collected from patients, site performance data and completion metrics on Source Data Verification. The platform can run automated checks regularly to ensure that the processes comply with regulatory standards.
The platform makes it easy to manage data by standardizing the data from all sources into a singular regulatory format and storing them to be accessed as a singular source of truth. KPI’s can be predefined which will be tracked by the AI platform to identify potential protocol deviations and recommend course correction accordingly. It also records any deviations in the data and stores it to understand the pattern and detect much earlier in the future. After all, AI is a self-learner.
AI can collect and share information in real-time to improve the site and patient outcomes
AI provides users with access to data in real-time. This data is harmonized and is ready for analysis. The user can use a self-service visualization tool to keep a visual track on study milestones and checkpoints and immediately address any potential red flags. AI can also reduce patient dropouts by identifying the sites with access to the most relevant patient population.
According to visionary leader Steve Jobs, if one defines the problem correctly, that person almost has the solution. And here we are discussing AI as the bellwether solution to every business/operational challenge in this world. Throw in a few AI-related words and the conversation suddenly sounds futuristic and efficient. Sure, AI is already impacting us in various walks of life from online ads to streaming services to smart cars but what about business? In particular, drug development. Is AI ready to make its magic work for clinical trials? Let’s deep dive and try to find the answer.
How well have we defined the problem?
Challenges in patient recruitment, medication adherence, population health management, risk monitoring, data collection and aggregation for clinical trials are well documented. With the introduction of CDISC standards and ICH regulations, the process of recording and reporting data has undergone much-needed standardization. Yet as technology evolves, it adds new operational challenges. Here I am talking about new data sources, in particular, the rise in wearable technologies.
More and more patients are now using wearable devices to track health data thus creating a stream of continuous flowing data. This is a problem for an industry grappling with archaic systems and suboptimal processes for data management. The existing infrastructure is not capable of supporting big data and the processes require far too many human interventions to achieve the required level of efficiency. This is where cloud-based platforms and AI-driven workflow automation can make things better.
The Focus Areas of Clinical Trial Operations
To ensure efficient operations one must focus on the critical parameters of a clinical trial, which are
It all boils down to one crucial aspect, quick access to relevant information. Sounds easy but this is the deciding factor between the success and failure of a study. Currently, trial managers have to sift through tonnes of recorded information which requires a lot of time and patience. As a consequence, many of them miss crucial red flags leading to delays and in many cases, failure of the study itself.