Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Benchmark”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66Linked to original sources
PubMedDate not supplied

Benchmarking physician performance, Part 1.

The performance of 16 primary care physicians in the same medical specialty and university clinic is compared using data envelopment analysis (DEA) efficiency scores. DEA is capable of modeling multiple criteria and automatically determines the relative weights of each performance measure. In this research, the performance measures include physician work relative value units (RVUs) as an input variable and patient satisfaction and total billable charges as the two output variables. The results provide insights into: 1. Who are the best-performing physicians? 2. Who are the underperforming physicians? 3. How can underperforming physicians improve? 4. What are the underperformers' performance targets? 5. How do you deal with full- and part-time physicians in a university setting? This research also provides a preliminary framework for how work measurement and DEA analysis can be used as a basis for a medical team or physician compensation system.

Benchmarking↗
PubMed2006

Benchmarking RCM.

Explore the source record for details and available documents.

Benchmarking↗
PubMedDate not supplied

Benchmarking physician performance, part 2.

Part 1 of this article (January-February 2006) reviewed ways of measuring the work of physicians through methods such as data envelopment analysis (DEA) and relative value units (RVUs). These techniques provide insights into: 1. Who are the best-performing physicians? 2. Who are the underperforming physicians? 3. How can underperforming physicians improve? 4. What are the underperformers' performance targets? 5. How do you deal with full- and part-time physicians in a university setting? Part 2 compares the performance of 16 primary care physicians in the same medical specialty using DEA efficiency scores. DEA is capable of modeling multiple criteria and automatically determines the relative weights of each performance measure. This research also provides a preliminary framework for how work measurement and DEA can be used as a basis for a medical team or physician compensation system.

Benchmarking↗
PubMed2006

GPAC: benchmarking the sensitivity of genome informatics analysis to genome annotation completeness.

In view of the recent explosion in genome sequence data, and the 200 or more complete genome sequences currently available, the importance of genome-scale bioinformatics analysis is increasing rapidly. However, computational genome informatics analyses often lack a statistical assessment of their sensitivity to the completeness of the functional annotation. Therefore, a pre-analysis method to automatically validate the sensitivity of computational genome analyses with regard to genome annotation completeness is useful for this purpose. In this report we developed the Gene Prediction Accuracy Classification (GPAC) test, which provides statistical evidence of sensitivity by repeating the same analysis for five different gene groups (classified according to annotation accuracy level), and for randomly sampled gene groups, with the same number of genes as each of the five classified groups. Variability in these results is then assessed, and if the results vary significantly with different data subsets, the analysis is considered "sensitive" to annotation completeness, and careful selection of data is advised prior to the actual in silico analysis. The GPAC test has been applied to the analyses of Sakai et al., 2001, and Ohno et al., 2001, and it revealed that the analysis of Ohno et al. was more sensitive to annotation completeness. It showed that GPAC could be employed to ascertain the sensitivity of an analysis. The GPAC bendhmarking software is freely available in the latest G-language Genome Analysis Environment package, at http://www.g-language.org/.

Benchmarking↗
PubMed2006

The myths of benchmarking healthcare IT spending.

Healthcare organizations should make those IT investments that they believe can be managed to achieve an acceptable return. They should make investment decisions based on the merits of the IT proposal, not because they have to catch up to another industry, such as banking.

Benchmarking↗
PubMedDate not supplied

Benchmarking retirement: a best practices decision.

Making the decision to retire from active practice is a complex process with very strong psychological overtones. Successful retirement demands preplanning, and financial preplanning must begin very early in the career to achieve the time value of money. Psychological preparation requires recognition that retirement is inevitable, and an avenue of change for yourself and your spouse should be identified, anticipated, and refined over time.

Benchmarking↗