loading page

Leveraging Human Gut Microbiome Data for the Enrichment of EHRs
  • warren,
  • PeterVH,
  • alan christoffels
warren

Corresponding Author:[email protected]

Author Profile
PeterVH
South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
Author Profile
alan christoffels
University of the Western Cape
Author Profile

Abstract

In order to advance our understanding of the role the Human Gut Microbiome (HGM) plays in our health and its link to disease, integrative ‘omics’ techniques are required. Microbiome-wide association studies provides insight to deepen our understanding of the links between our microbiota and disease states \cite{Gilbert2016}. Various studies have shown links between HGM and Rheumatoid arthritis, and Cardiovascular disease to mention a few \cite{Scher2013,Yin2015}. Thus, integrating HGM data into electronic health records (EHRs) would promote association wide microbiome studies. Biomedical informatics teams are looking into the integration of HGM data into EHRs. However, the progress of medical care and health services research and applications increasingly depends upon combining different types, sources, and volumes of data efficiently. It is not just a matter of data scale but also a matter of reaching agreements on how these data are represented and accessed. Currently in South Africa there exists no standardized EHR, due to failures to develop one by the National Department of Health (NDoH) and the Council for Scientific and Industrial Research (CSIR), and thus no standard pertaining to the integration of HGM information. This implies that no international standardized EHR exists. Various medical record systems are currently in use throughout South Africa which further shows the fragmentation of EHRs \cite{eHealthStrategy2012}. By making use of standards such as Fast Health Interoperability Resource (FHIR) and open source platforms OpenMRS, and SMART, together with human gut microbiome analysis pipelines, we aim to develop a standard for the integration of microbiome data into EHRs which will give clinicians more contextual information about the microbiome, and to develop a clinical application that provides clinicians with a means to efficiently and thus more quickly derive insights. Without this integrated information clinicians usually have to manually sift through various databases to glean insights. Additionally, the developed standard will promote microbiome-wide assoication studies. Finally, we aim to investigate whether a comprehensive microbiome annotation can be made available via the EHR.