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This is a review on Lau's article: Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data.<ref name="Lau et al"> Lau, E. C., Mowat, F. S., Kelsh, M. A., Legg, J. C., Engel-Nitz, N. M., Watson, H. N., ... & Whyte, J. L. (2011). Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data. Clinical epidemiology, 3, 259. http://www.ncbi.nlm.nih.gov/pubmed/22135501
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==Introduction==

EMRs have been used for observational research, post-marketing research and pharmacoepidemiologic studies etc. One advantage of EMRs data is its comprehensiveness and timeliness. The availably of data varies across EMRs and depends on their design and completeness of data entry. This paper compared demographic, clinical and treatment factors from a community oncology clinic EMR database with three other common databases: SEER registry, Medicare claims and a commercial health insurance claims database to evaluate the content and utility of EMRs in population-based cancer research.

==Methods==

Data sources: 2006 data were selected for all three comparison databases (SEER, Medicare, commercial claims). EMR database is the Oncology Services Comprehensive Electronic Records (OSCER) data warehouse. SEER collects data from 17 population-based registries representing 26% of the U.S. population. Medicare data provided a nationally representative source of medical treatment data for elderly US residents. Commercial claim data contained the demographic and treatment data among cancer patient <65 years old. The authors applied patient and clinic inclusion criteria for analytical files.

Patient demographic, clinical and treatment characteristics  from the four data sources were compared using six tumor sites: breast, lung/bronchus, head/neck, colorectal, prostate and non-Hodgkin’s lymphoma (NHL). This paper focused on descriptive comparisons and used Cohen’s w effect size (ES) with a pooled standard deviation to assess the importance of observed differences. They used a hot-deck method to impute missing data as appropriate statistical procedures. 


==Results==

Several differences were observed in overall tumor site distributions across data sources. Sex and age distributions of patient populations for each tumor site were generally similar across the data sets. Differences in racial composition were observed. There are small differences in tumor stage by tumor site for EMR and SEER data.  Differences were observed in ambulatory treatment. 

==Discussion==

The authors identified some specific data comparability and methodological challenges arose during their analysis: 1) missing data and data standardization; 2) patient/clinic characterization. Physician specialty or clinic and practice type may influence treatments used and demographic profiles.  They also stated the several limitations of this study such as limitation in accuracy and completeness of EMRs data , its representativeness and exclusion of pharmacy claims data etc.

==Conclusion==

By comparing an oncology EMR database to a large cancer registry and two claim databases, the authors characterize differences between the databases and use these comparisons in estimating characteristics in US cancer population. They identified several factors including the tumor stage, geographic location and specialization of the medical facilities, that must be considered when using EMRs for research purpose or generalizing results to the US cancer population. EMRs can provide detailed or more complete clinical data not found in claims that are extremely important in conducting epidemiologic and outcomes research. 

==My Comments==

This is an interesting study. The databases in comparison are large and representative for cancer population in US. It further provided the evidence of using of EMRs to conduct epidemiologic and outcome research.  The factors that must be considered when using EMRs for research purposes would be useful. I think the challenge identified by the authors and its solutions, i.e. missing data and applying data imputation procedures to missing data to improve the completeness of data for comparison is interesting. 

 == References ==
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