'Big data' can revolutionize gastroenterology

By Anastasia Climan, RDN, CD-N | Fact-checked by Barbara Bekiesz
Published April 24, 2024

Key Takeaways

  • Gastroenterology is suited to benefit from “big data” because it’s a complex speciality with wide-ranging contributing factors.

  • Machine learning and artificial intelligence can learn from big data to improve GI diagnostics.

  • Greater protections are needed to ensure informed consent and prevent the misuse of patient information.

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. 

Some experts believe “big data” will revolutionize how we diagnose and treat disease. However, larger and more complex datasets must be handled carefully to avoid magnifying mistakes and biases. 

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. 

There is limited utility when viewing data from isolated studies. Computer power could help overcome logistical barriers to evaluating a wider range of information at once.

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

Big data could continue to advance our understanding of multifactorial inflammatory bowel diseases, improve biomarker analysis for colon cancer screening, and inform the design of precision medicines. 

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.”

Finding more tailored treatment approaches can help GI docs avoid unnecessary interventions and save patients from the burdens of advanced and recurrent disease.

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. 

Concerns about informed consent and data ownership must be addressed. As large volumes of sensitive health information are collected, stored, and analyzed, there is an increased risk of data breaches, unauthorized access, and misuse of patient data. 

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.

What this means for you

Wielding the power of big data is a significant responsibility with the potential for both huge payoffs and disastrous pitfalls. Gastroenterology patients and providers stand to benefit from advances in analytics, but they must remain skeptical and aware of what big data can and cannot predict about individual patients.

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