How AI Can Uncover Data Outliers and Patterns in Patient Behavior

AI can help clinical experts uncover and identify patterns, trends, anomalies and outliers in their patient data. Discover what AI can uncover in your data.

Within clinical trials, clinical experts (including medical monitors, clinical scientists, and data managers) work with a staggering amount of data. With trial costs climbing in tandem with trial duration, it’s imperative for researchers to uncover trends, patterns, anomalies, and risks as quickly as possible. But, doing this is time-consuming, exhausting, and sometimes beyond a data scientist’s scope of expertise.

AI, when trained accurately, can be a powerful asset for quickly identifying outlier data and patterns in patient behavior. This data could easily be missed otherwise, potentially impacting the accuracy and success of a trial’s outcome.

From a review standpoint, a clinical expert of any kind has two primary roles to perform: identifying clinically relevant data issues from patient and trial data and pinpointing potential patterns and trends within an individual patient or group of patients for safety purposes. AI and machine learning (whether supervised, unsupervised, or LLM-based) can aid clinical experts with these tasks.

How Can AI Help Identify Clinically Relevant Data Issues and Patterns/Trends?

AI can aid in identifying clinically relevant data issues in several ways, including:

  1. Identifying data patterns and trends – an AI-powered machine learning model can rapidly analyze and identify certain data patterns and trends across values, columns, and tables. It can then present these findings to a medical monitor.
  2. Identifying correlations between independent or related datasets – AI can flag potential correlations between datasets, for example lab values, and dose modifications for a drug being tested.
  3. Making predictions for risk mitigation – by analyzing historical data, AI can predict potential patient outcomes like adverse events or reactions or early patient dropouts or withdrawals.
  4. Provides if/then conditional rules – AI can generate if/then rules that help govern its observations of and detections within patient and clinical trial data.
  5. Offers custom rule creation – using generative AI, non-technical clinical experts can use natural language to create and input custom rules for their specific study with zero dependency on programmers or developers.

 
Using these AI-driven solutions, medical monitors and other clinical experts can identify clinically relevant data faster. They can then develop responsive strategies more quickly and take mitigative actions to reduce risks, prioritizing patient safety and improving trial efficacy and success.

Identifying Patterns and Outliers in Clinical Trial Data Using AI

A clinical data manager plays a primary role in ensuring data quality and integrity throughout a clinical trial. AI or machine learning is only as capable as the data that trains the model.

Poor, unlabeled, or incorrectly labeled data will affect your AI tool’s ability to identify trends, patterns, anomalies, and outlier cases in your datasets. Trying to manually clean, standardize and centralize training data to train an AI model from scratch is time-consuming, expensive and often beyond a clinical expert’s skill set.

Leveraging pre-trained, out-of-the-box AI software for effective data management is a faster, more cost-effective solution.

An AI-powered software solution that helps organizations clean, centralize, and standardize data, like Saama’s Data Hub, makes it easy to immediately deploy your data for further analysis.

Additionally, an AI tool like Smart Data Quality (SDQ) that reviews data to identify discrepancies in trial data and provides custom rule-building speeds up an otherwise lengthy manual review process. Lastly, Patient Insights uses AI to quickly identify patterns, trends, and correlations, making it easier to identify and flag patient outlier data, while also providing predictions based on a patient’s behavior to mitigate risks.

Conclusion

It’s important to stress, though, that AI cannot replace a clinical expert nor does it have the expertise to do so. The goal of AI and machine learning is to guide clinical experts to the data and insights they need more quickly, so they can leverage their subject matter expertise as needed and take action.

AI is invaluable for quick, accurate data analysis as well as pattern recognition.. Clinical experts are always in control of their tasks and decisions – the role of AI is not to make these decisions for them. It acts as a springboard that helps accelerate their workflows and improve their efficiency. At Saama, we live by this belief by always ensuring we keep a human in the loop of all of our AI and machine learning processes.

Book a demo to discover how our innovative AI solutions can drive greater speed, accuracy, and efficiency across your clinical development workflows.

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