Current big data approaches to clinical questions in otolaryngology

The amount of data available within healthcare has skyrocketed over the last several decades from the introduction of electronic medical record system and healthcare digitalization. The digital healthcare shift has given rise to an era of “Big Data,” which has the potential to revolutionize health research and delivery. Big data has multiple forms within healthcare; large databases such as administrative databases and clinical registries, which broadly source information from patient encounters, but there are other “richer” sources such as “omics” fields like genomics/proteomics, imaging (e.g., CT scans, MRIs), healthcare wearables, and electrophysiologic data. Healthcare informatics and data science has taken shape out of necessity, allowing clinicians and scientists to transform this heterogenous and unorganized data into digestible, relevant, actionable, and outcome-directed formats. This transformation has ushered us into a period of innovation fueled by data science, artificial intelligence (AI), and promise of personalized medicine. The use of big data in otolaryngology is a relatively new, but exciting and a blossoming field. Administrative databases have been used to analyze prescription drug use and trends, healthcare costs, and adherence to guidelines. Clinical registries have contributed similarly while offering more granular and specialty-specific data. An otolaryngology-centric registry, Reg-ENT, is still in its early stages but may offer powerful big data tool. Big data has allowed novel innovations like AI to appear in otolaryngology that can augment clinical decision making, improve prediction modeling, and improve precision medicine healthcare delivery. The future of big data in otolaryngology is bright but will require significant work and participation from clinicians and researchers to improve standardization, harmonization, and collaboration so it may reach its full potential.