Towards a Common Data Model: An Evaluation of Standardized Healthcare Reporting Data Models Restricted; Files Only

Nair, Shailesh (Spring 2018)

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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


Introduction  5

What is a Data Model?  7

Rationale  11

Need for a Common Data Model (CDM) 13

Decision Support System (DSS) 15

Method  17

Evaluation Process  17

Candidate Common Data Models  21

OMOP CDM    22

Background  22

Introduction  22

OMOP Table Names and Description  25

PCORNet CDM    33

Background  33

Introduction  33

PCORnet Table Names and Description  35

Evaluation Criteria  40

Moody-Shanks Evaluation Model 42

Completeness  43

Simplicity  44

Flexibility  44

Understandability  45

Integration  46

Implementability  46

Results  48

Completeness - Domain and attribute coverage. 49

Integration  52

Flexibility  53

Simplicity - Ease/Complexity of Querying  54

Implementation - Field Experience, Stability, Adoption. 54

Discussion  58

Limitations  62

Conclusion  64

References: 65



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