Ontology-Based Data Integration of Open Source Electronic Medical Record and Electronic Data Capture Systems

Abstract

In low-resource settings, the prioritization of clinical care funding is often determined by immediate health priorities. As a result, investment directed towards the development of standards for clinical data representation and exchange are rare and accordingly, data management systems are often redundant. Open-source systems such as OpenMRS and OpenClinica provide an opportunity to leverage available systems to improve standards and increase interoperability. Nevertheless, continuity of care and data sharing between these systems remains a challenge, particularly in populations with changing health needs, and inconsistent access to health resources. The overarching goal of this project is to enable sharing of data across low cost systems like OpenMRS and OpenClinica using ontologies. The project consists of three aims: 1) describing clinical research and visit data related to the treatment and care of HIV/AIDS patients, 2) developing a prototype data integration system between electronic medical record and electronic data capture systems, and 3) evaluating the utility of the prototype system using simulated and real-world data. In the first aim, I developed a patient identifier and a HIV/AIDS treatment and care ontology to represent the types of data and information created and used by clinicians. This was achieved by gathering data forms used in HIV/AIDS clinics in low-resource settings. From these forms, the patient identifier and HIV/AIDS variables were extracted and used to create the ontologies. In aim 2, the ontologies from aim 1, along with simulated data, were used to develop a prototype data integration system that improves the ability of developers to implement integration systems that meet the needs of users, based on previously created use cases. In the third aim, I evaluated whether the matching algorithm used in the prototype can correctly identify matching patients, and whether the prototype is generalizable to clinical care and research data collected in a real world setting. This work contributes two ontologies to the medical and public health fields that are useful in providing standardization of data elements. Additionally, I provide a prototype data integration system that is useful in facilitating access to previously siloed data and helps reduce the burden of integrating future systems.