View source for Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research

This is a review of an article by Kurian et al. (2014) titled Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research.<ref name="2014 Kurian"> Kurian, A. W., Mitani, A., Desai, M., Yu, P. P., Seto, T., Weber, S. C., ... & Das, A. K. (2014). Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research. Cancer, 120(1), 103-111. http://www.ncbi.nlm.nih.gov/pubmed/?term=24101577</ref>

==Introduction/Background==
Cancer outcome data research has been limited by research that is the fragmented and lacks details regarding the variability of data. of detail in variable data. Linking [[EMR|EMR]]-derived data across health care systems and population-based cancer registries, offers the promise of more complete information. This paper analyzes the breast cancer diagnosis and treatment. Data is derived from three sources: electronic medical records (EMRs), two health care systems, and the state registry. This 3-way data linkage is offers a practical approach and provides additional information regarding the variability in cancer care and outcome data.

==Materials and Methods==
Breast cancer diagnosis test and treatment data from January 1, 2000 through January 1, 2010 was extracted from EMR's at Stanford University Medical Center (university patients) and the Palo Alto Medical Foundation (community patients). The authors requested the California Cancer Registry (CCR), a SEER component. CCR and EMR records to be linked using names, social security numbers, medical records numbers and birth dates. They generated two separate sets of data for both university and community patient cohorts. Then, the authors linked the 2 EMR-CCR cohorts. 

==Results==
The authors initially identified 8210 university analytical patients and 5770 community patients, linking records identifying 12,109 records. The combined analytical cohort contained 1902 overlapping records (16%). This group of patients was treated in both facilities. (defined as “Both” patients). Increasing the percentage of community patients, but not the percentage of university patients who fell into “Both” patients category. Patient were found to have similar prognostic factors such as decreasing age, increasing stage of disease, increasing grade, and less favorable receptor sub-type worsened. Before linking the data sets, community patients appeared to receive less interventions than university patients (mastectomy: 37.6% vs. 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked data sets revealed that patients treated at both facilities received more interventions than those treated at only one facility (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 54%; and genetic testing: 10.9%).

==Conclusions==
The 3-way data linkage revealed a 16% patient overlap between the two health care systems. Identifying these patients reduced missing data and generated unique insights.  The “Both” patents received more intensive treatment than others despite comparable prognostic factors. Integrating cancer data from EMRs and population-based registries broadened the understanding of cancer care beyond what could be achieved from just one or two data sources. This approach can advance comparative effectiveness and outcome oncology research. 

==My comment==
This study design is interesting to me. I have been interested in how database linkage works. They did a thorough analysis and came to the conclusion that linking EMR data and cancer registry data can be a solution for issues regarding data fragmentation and missing data. The authors demonstrate a practical approach for improving the methodology of collecting cancer research outcome data.

== References ==
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[[Category: Reviews]]
[[Category: HI5313-2015-FALL]]
[[Category: EHR]]
[[Category: Databases, Big Data, and Data Quality]]