Over the past two decades, we have witnessed the widespread adoption of Electronic Health Records (EHR) systems by hospitals and clinics to streamline, improve and enhance patient-centered care. Consequently, there is a heightened emphasis on coupling patient care with the reuse and synthesis of clinical and administrative data.
Data processing frameworks whether at information ingestion stage or during reporting and analytics are built on a data storage substrate. Data standardization is central to collaborative research data-sharing, health registry reporting, and the adoption of crowd-sourced solutions. These trends are disrupting the healthcare analytics landscape and spurring institutions to explore standardized data models.
This thesis evaluated two popular common data models (CDM) - Observational Medical Outcomes Partnership (OMOP) and the Patient-Centered Outcomes Network (PCORnet). The evaluation process consisted of identifying candidate standard data models, generating requirements criteria based on past trove of data requests received by Emory Library and Information Technology Services department, longitudinal health registry reporting and departmental data marts. These specifications informed the evaluation criteria formulated by the synthesis of established data model assessment frameworks in published literature - namely Moody and Shanks, which was further adapted by Kahn et al. and operationalized by Garza and colleagues.
The evaluation criteria of domain coverage, flexibility, use of controlled vocabularies, ease of querying, understandability, stability, adoption and community support found the OMOP CDM to be the data model of choice. The OMOP CDM supports the highest number of data domains, attributes and offers comprehensive coverage of standard terminologies.
Table of Contents
What is a Data Model? 7
Need for a Common Data Model (CDM) 13
Decision Support System (DSS) 15
Evaluation Process 17
Candidate Common Data Models 21
OMOP CDM 22
OMOP Table Names and Description 25
PCORNet CDM 33
PCORnet Table Names and Description 35
Evaluation Criteria 40
Moody-Shanks Evaluation Model 42
Completeness - Domain and attribute coverage. 49
Simplicity - Ease/Complexity of Querying 54
Implementation - Field Experience, Stability, Adoption. 54
About this Master's Thesis
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