Perspectives on clinical informatics: integrating large-scale clinical, genomic, and health information for clinical care

Abstract

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

Keywords: clinical data warehouse; database; electronic health records; medical informatics.

Fig. 1

Electronic medical record (EMR) databases and PheWAS. (A) Dataflow shows how EMRs of enrolled patients are curated and incorporated into a database. (B) In silico cohort is generated from an EMR database with respect to various categories (e.g., billing diagnoses or disease-related genotypes). The cohort is tested for the association with various phenotypes, as available in EMR databases (PheWAS). R/O, rule out; BMI, body mass index.