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. 2018 Jun;18(6):1321-1327.
doi: 10.1111/ajt.14892. Epub 2018 May 22.

Expanding transplant outcomes research opportunities through the use of a common data model

Sylvia Cho  1 , Sumit Mohan  2   3   4 , Syed Ali Husain  2   4 , Karthik Natarajan  1
Affiliations

Expanding transplant outcomes research opportunities through the use of a common data model

Sylvia Cho et al. Am J Transplant. 2018 Jun.

Abstract

The volume of solid organ transplant in the United States is increasing, providing improved quality of life and survival for patients with organ failure. The growth of transplant requires a systematized management of transplant outcomes assessment, especially with the movement toward value-based care. However, there are several challenges to analyzing outcomes in the current registry-based, transplant reporting system: (1) longitudinal data points are difficult to capture in outcomes models; (2) data elements are restricted to those that already exist in the registry data; and (3) there is a delay in the release of outcomes report. In this article, we propose an informatics approach to solve these problems by using a "common data model" to integrate disparate data sources, data elements, and temporal data points. Adopting such a framework can enable multicenter outcomes analyses among transplant centers, nationally and internationally.

Keywords: United Network for Organ Sharing (UNOS); clinical research/practice; health services and outcomes research; informatics; organ transplantation in general; registry/registry analysis.

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Conflict of interest statement

Disclosure

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Figures

Figure 1
Figure 1. Data Standardization using OMOP CDM
Data extracted from sources with differing methods of data organization are combined with UNOS data and transformed to a common model that allows easy compilation and comparison of data from different centers for outcomes analyses. As we can see in the figure, the layout and shape that contains data are different to show the distinct structure between databases, and wordings for clinical concepts are different to show that disparate information systems represent the same clinical concepts differently. Once the disparate data sources are transformed to the OMOP CDM, the structure and clinical concept representation are standardized regardless of which data source you are dealing with. This allows institutions to apply the same analysis methods and aggregate results. Source: https://www.ohdsi.org/data-standardization/
See this image and copyright information in PMC

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