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Objective. Develop an improved method for auditing hospital cost and quality. Data Sources/Setting. Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, Texas, and New York between 2004 and 2006. Study Design. A template of 300 representative patients was constructed and then used to match 300 patients at hospitals that had a minimum of 500 patients over a 3-year study period. Data Collection/Extraction Methods. From each of 217 hospitals we chose 300 patients most resembling the template using multivariate matching. Principal Findings. The matching algorithm found close matches on procedures and patient characteristics, far more balanced than measured covariates would be in a randomized clinical trial. These matched samples displayed little to no differences across hospitals in common patient characteristics yet found large and statistically significant hospital variation in mortality, complications, failure-to-rescue, readmissions, length of stay, ICU days, cost, and surgical procedure length. Similar patients at different hospitals had substantially different outcomes. Conclusion. The template-matched sample can produce fair, directly standardized audits that evaluate hospitals on patients with similar characteristics, thereby making benchmarking more believable. Through examining matched samples of individual patients, administrators can better detect poor performance at their hospitals and better understand why these problems are occurring. Key Words. Quality of care, outcomes research, health care research, costThere are reasons to audit hospitals with respect to cost and quality. An insurer, such as Medicare, Medicaid, or a private insurance company, wants to ensure efficient and safe practices. A hospital's Chief Medical Officer wants to benchmark efficiency and quality in comparison to performance at other hospitals.

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Over the past two decades, live kidney donation by older individuals (≥55 years) has become more common. Given strong associations of older age with cardiovascular disease, nephrectomy could make older donors vulnerable to death and cardiovascular events. We performed a cohort study among older live kidney donors who were matched to healthy older individuals in the Health and Retirement Study. The primary outcome was mortality ascertained through national death registries. Secondary outcomes ascertained among pairs with Medicare coverage included death or cardiovascular disease ascertained through Medicare claims data. During the period from 1996 – 2006, there were 5717 older donors in the United States. We matched 3368 donors 1:1 to older healthy non-donors. Among donors and matched pairs, the mean age was 59 years; 41% were male and 7% were black race. In median follow-up of 7.8 years, mortality was not different between donors and matched pairs (p=0.21). Among donors with Medicare, the combined outcome of death/CVD (p=0.70) was also not different between donors and non-donors. In summary, carefully selected older kidney donors do not face a higher risk of death or CVD. These findings should be provided to older individuals considering live kidney donation.

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Objective. To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public. Data Sources/Setting. Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011. Study Design. Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals. Data Collection/Extraction Methods. Medicare fee-for-service claims. Principal Findings. Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing. Conclusion. To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing.

The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers belonging to a specific target audience. This task has immense importance in the field of lexical simplification. Lexical simplification helps in improving the readability of texts consisting of challenging words. As a participant of the SemEval-2016: Task 11 shared task, we developed two systems using various lexical and semantic features to identify complex words, one using Naïve Bayes and another based on Random Forest Classifiers. The Naïve Bayes classifier based system achieves the maximum G-score of 76.7% after incorporating rule based post-processing techniques.

Traditional on-disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world. Oracle 12c Database In-memory, the industry's first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other. In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific formatrow major or columnarthat will be scanned during query execution. With enhancements in several areasstatistics, cost model, query transformation, access path and join optimization, parallelism, and cluster-awarenessthe query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory. This section provides a brief introduction to Oracle DBIM; more details are in [13] and [18]. Oracle 12c Database In-Memory (DBIM) is a dual-format database where data from a table can reside in both columnar format in an in-memory column store and in row major format on disk. The in-memory columnar format speeds up analytic queries and the row major format is well-suited for answering OLTP queries. Note that scanning on-disk tables does not necessarily mean disk I/O; some or all of the blocks of the table may be cached in the row major buffer cache [4]. A dedicated in-memory column store called the In-Memory Area acts as the storage for columnar data. The in-memory area is a subset of the database shared global area (SGA).