(EMR) Electronic Medical Record Software for Breast Cancer Patient

Robert Graham Reporting: Press Release New York City New York August 1, 2012

Announced Today The Release of Electronic Medical Record Software for Breast Cancer Patient.

GenNXeix Medical Software

Announced Today The Release of Electronic Medical Record Software for Breast Cancer P

atient. Use of electronic medical records (EMR) for Breast Cancer Oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data.

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Brought To You By gloStreamgloEMR, we'll provide a full refund for all GenNXeix gloStream software and services. With GenNXeix gloStream you can be confident that your EMR purchase will help your business succeed and won't cause it to falter. Doctors considering EMR software and thinking about whether they will qualify for stimulus funding must be certain that the systems they choose are certified and

stimulus ready. You can be confident that gloEMR from GenNXeix gloStream will qualify for stimulus funding because we guarantee it. In fact, GenNXeix gloStream will refund the software costs for any eligible professional who goes through our exclusive gloDNA process, and then finds themself unable to qualify as a meaningful user and acquire stimulus funding. The unique combination of GenNXeix gloStream certified software and our exclusive gloDNA process ensures that doctors always have the tools and knowledge needed to achieve truly meaningful use - today, and well into the future. Gennxeix after researching breast cancer to assess the utility of using EMR data in

population-based cancer research by comparing a database of EMRs from community oncology clinics against Surveillance Epidemiology and End Results (SEER) cancer registry data and two claims databases (Medicare and commercial claims). Demographic, clinical, and treatment patterns in the EMR, SEER, Medicare, and commercial claims data were compared using six tumor sites: breast, lung/bronchus, head/neck, colorectal, prostate, and non-Hodgkin’s lymphoma (NHL). We identified various challenges in data standardization and selection of appropriate statistical procedures. We describe the patient and clinic inclusion criteria, treatment definitions, and consideration of the administrative and clinical purposes of the EMR, registry, and claims data to address these challenges. Sex and 10-year age distributions of patient populations for each tumor site were generally similar across the data sets. We observed several differences in racial composition and treatment patterns, and modest differences in distribution of tumor site. We calculated treated proportions of patients by dividing the number of patients receiving ambulatory therapy by the total number of patients with the specific cancer diagnosis. Given the large sample sizes from the databases evaluated, traditional tests of significance resulted in statistically significant findings, even for small absolute differences. Therefore, we focused on descriptive comparisons and used Cohen’s w effect size (ES) with a pooled standard deviation to assess the importance of observed differences. This qualitative measure is not based on a rigorous hypothesis-testing framework and does not have a probabilistic interpretation such as a P-value obtained from standard methods. While the ES interpretation depends on the subject matter, Cohen classified the magnitude of the ES as small (w = ~0.1), medium (w = ~0.30), and large (w = ~0.50).25,26 A large proportion of data was missing for race (40%) and tumor stage (~70%) in the EMR records. The largest percentage of missing stage data (97%) was observed for NHL. Given that NHL treatment is determined mainly by subtype and pathology (not stage), this missing data trend was understandable. When these two categories were excluded, the proportion missing for stage was 63%. Text fields were not analyzed to determine whether they contained missing stage information. We selected a hot-deck method to impute missing data,27 and compared this method against two other regression-based imputation procedures.28–30 We also evaluated the model prediction properties of hot-deck imputation by applying it to records with known values. Sociodemographic information (2000 US Census) was incorporated into the imputation models. Pre- and post-imputation marginal distributions were compared to evaluate similarity in data sets and were found to be comparable to distributions of data among records with complete information for race and stage. An evaluation of the performance of the hot-deck procedure under a simple missing data mechanism that compared imputed and observed data was also conducted. Only post-imputed data comparisons are presented. SEER provided the largest number of patient records (331,427). There were 60,255 unique records in Medicare and 32,357 and 16,427 records in the EMR and commercial claims, respectively. Several differences were observed in overall tumor site distributions. Excluding the “other tumors” category, the largest proportion of patients had prostate cancer in Medicare, and the largest proportion of patients had breast cancer in the other three databases. In the oncology EMR data, >25% of the cancer patient records had breast tumors – nearly 7% more than the proportion in SEER – while Medicare had the lowest fraction (8%). The EMR had the highest percentage of lung cancer and NHL patients; proportions of patients with CRC or head/neck tumors were generally comparable across all databases. Prostate cancer was noticeably under-represented in the EMR, likely because prostate cancer patients are treated primarily by urologists.