'Big data' can revolutionize gastroenterology

An ever-expanding database of clinical trials, population studies, and EMRs has generated more data than we can use—until now. New technology offers an opportunity to harness information like never before. 

Here’s how the field of GI is suited to benefit from the careful application of big data.

Capturing the ‘multi-nomics’ of GI

Gastroenterology raises complex biological questions involving the interplay between the gut (and its microbiome), the brain, and the immune and endocrine systems. In addition, environmental and lifestyle factors impact risks and outcomes. 

According to a review article in the International Journal of Molecular Sciences, datasets from the following areas may be especially relevant for GI:^[]

• Genetics and epigenetics

• Metabolomics

• Microbiome research

• Medical informatics (from EHRs) and patient imaging data

• Proteomics

• Transcriptomics

The review authors write, “The generation of ‘big data’ from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions.”

The advantages of big data in GI

Leveraging data from multicenter clinical trials, genomic studies, and real-world evidence has the potential to accelerate the speed of scientific discovery and innovation in gastroenterology. With access to medical histories, laboratory results, imaging studies, and genetic information from vast datasets, clinicians can better identify patterns that enhance GI diagnostics.

Machine learning (ML) is actively being studied for use in colonoscopies. A review published in Frontline Gastroenterology mentions a UK study in which AI algorithms were trained on 1.5 million images of polyps that were manually annotated by expert endoscopists. In a real patient setting, AI was highly effective at detecting polyps but had a high rate of false-positive results.^[]

Big data also enables personalized treatment plans based on a patient's genetics, microbiome composition, lifestyle, demographics, and lab results. As the Frontline Gastroenterology authors noted, “Gastroenterology as a specialty is potentially well placed to making use of ML in this way, since the management of many gastro-intestinal conditions (eg, IBD) requires multiple laboratory tests and imaging over time that can provide the type of deep, variable-rich datasets well suited to ML methods.”

Potential pitfalls in medicine

Despite the advantages, there are potential pitfalls at each step of the process, from dataset selection to its analysis and interpretation. With big data comes a big responsibility to protect patients against privacy breaches and harm. 

Healthcare organizations must implement robust security measures, encryption protocols, and data anonymization techniques to safeguard patient confidentiality and comply with regulatory requirements.

In addition, big data analytics relies on the quality of the underlying data sources. However, inconsistencies, errors, and missing data can compromise the validity and reliability of the reports and misinform decision-makers. Big data analytics may also be susceptible to biases inherent in the data collection process, such as selection bias, sampling bias, and confounding variables. These biases can skew the results and limit the generalizability of findings to patient subpopulations. 

Successful use of big data requires interdisciplinary collaboration and new patient protections. Data scientists, clinicians, and statisticians are all essential to facilitate the transfer of research findings into clinical practice.