PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15116523

Practical guide for improving performance.

Patrice L Spath
View original source ↗Export metadata ↓

Abstract

Research has established that health care is not error free. The question facing perioperative units, as well as all health care services, is how to minimize the human factors that impact quality and safety. This two-part series on performance improvement in perioperative services was intended to help answer this question. Achieving performance excellence starts with a supportive work culture. The human issues of teamwork, communication, and leadership are crucial to achieving performance excellence. Next, perioperative caregivers must accept that all people make mistakes so systems and processes can be designed to be more "forgiving" of errors. Last, a planned and systematic approach must be used to measure, analyze, and improve performance. Successful implementation of performance improvement calls for strong partnerships between physicians, managers, and staff members. Performance excellence requires that everyone work together to ensure that perioperative care is safe, effective, appropriate, customer focused, and efficient.

Explore related subjects

Efficiency, OrganizationalGuidelines as TopicModels, OrganizationalPlanning TechniquesTotal Quality ManagementUnited StatesJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Patrice L Spath. 2004. Practical guide for improving performance.. https://pubmed.ncbi.nlm.nih.gov/15116523/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2004

Reuse of imputed data in microarray analysis increases imputation efficiency.

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data.

Efficiency, Organizational↗