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Unlabelled: Electronic medical records (EMRs) are used increasingly for research in clinical oncology, epidemiology, and comparative effectiveness research (CER).
Objective: 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).
Study Design And Setting: 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.
Results: 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.
Conclusion: Our experience with an oncology EMR database identified several factors that must be considered when using EMRs for research purposes or generalizing results to the US cancer population. These factors were related primarily to evaluation of treatment patterns, including evaluation of stage, geographic location, race, and specialization of the medical facilities. While many specialty EMRs may not provide the breadth of data on medical care, as found in comprehensive claims databases and EMR systems, they can provide detailed clinical data not found in claims that are extremely important in conducting epidemiologic and outcomes research.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224632 | PMC |
http://dx.doi.org/10.2147/CLEP.S23690 | DOI Listing |
Abdom Radiol (NY)
December 2024
Brazilian Center for Evidence-Based Research, Federal University of Santa Catarina, Florianópolis, Brazil.
Purpose: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.
Method: Included studies assessed adults diagnosed with PDAC, confirmed by histopathology. Metrics of tests were interpreted by ML algorithms.
Ochsner J
January 2024
The University of Queensland Medical School, Ochsner Clinical School, New Orleans, LA.
Despite the substantial expenditures on health care in the United States, persistent underperformance in health system metrics necessitates innovative approaches to address complex patient needs. The MedVantage Clinic in New Orleans, Louisiana, offers a regionally tailored, value-based primary care model targeting patients with high social and medical needs. This study provides an evaluation of the efficacy of the MedVantage Clinic in improving the cost of care and service utilization for this population.
View Article and Find Full Text PDFLancet Reg Health West Pac
December 2024
School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, South Korea.
Background: Little is known about the impact of PM on people with disabilities. We aimed to explore the association between PM and hospitalization via the emergency department (ED admission) among people with disabilities, together with the attributable ED admission cases and costs.
Methods: We applied a time-stratified case-crossover design adjusting ozone, holiday, and temperature using seven years (2015-2021) of claim-based data on ED admissions from the Korean National Health Insurance Database.
Cureus
November 2024
Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, CHN.
Background Cardiovascular diseases (CVD), including coronary artery disease, ischemic heart disease, stroke, cardiomyopathy, and atrial fibrillation and flutter, are the leading cause of mortality worldwide, resulting in significant economic and health costs. Recognizing trends and geographical differences in the global burden of CVD facilitates health authorities in particular nations to assess the disease burden and forecast future epidemiological trends. Public health authorities in each country can better understand the differences in disease data and, by learning from the experiences and practices of successful countries and considering the characteristics of their diseases, allocate health resources more rationally and formulate more targeted healthcare strategies to reduce the disease burden.
View Article and Find Full Text PDFJ Comput Graph Stat
March 2024
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint.
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