In addition to direct biomedical interventions, precision health faces challenges in creating, understanding, analyzing, storing, and securing large amounts of heterogeneous data. To overcome these obstacles, informatics is integral.
Advanced Phenotyping for Precision Health
Phenotyping refers to the characterization of a population of interest based on clinical, behavioral, social, economic, or other non-genotypic features. For precision health, phenotypes imply a well-specified characterization of a patient cohort of interest (e.g., patients with a particular disease or having received a particular treatment) using explicit concepts such diagnostic codes, medications codes, laboratory values, or other standardized data.
Clinical Decision Support
Precision health will soon exceed human cognitive capacity, removing any doubt that health care’s future is entwined with computers and software. IU School of Medicine researchers are leveraging electronic health records (EHRs) with integrated clinical decision support (CDS), expanding the Indiana Biobank with subjects who meet particular study criteria, creating a research pipeline to medical providers across Indiana, and catalyzing highly competitive precision medicine research.
Privacy, Security and High-Performance Computing
High throughput data, such as next generation sequencing (NGS), are critical for precision health. IU School of Medicine is working to apply novel privacy-preserving techniques to analyze and share sensitive human genomic data without undermining participants’ privacy. Faculty and staff are designing and prototyping the necessary hardware and software for an optimized data management system and a closely linked system for data analytics
Informatics Infrastructure
The precision health initiative has created a novel infrastructure that is flexible to manage a wide array of data of many different formats described in different ways; powerful and able to scale to manage large data sets and complex analyses; accessible by investigators, care providers, and patients; and integrative to support broad new applications and uses. The team is developing the precision health research database using OHDSI (Observational Health Data Science and Informatics), including OMOP (Observational Medical Outcome Partnership) database model and ATLAS and Leaf for cohort definitions.
