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Kwan Y Lee

Publications and source records attributed to Kwan Y Lee.

3 recordsLinked to original sources

Using comparison charts to assess performance measurement data.

BACKGROUND: In 1997 the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) announced the ORYX initiative, which integrates outcomes and other performance measurement data into the accreditation process. JCAHO uses control and comparison charts to identify performance trends and patterns that are provided to JCAHO surveyors in advance of a health care organization's (HCO's) survey. During the survey, the HCO is asked to explain its rationale for its selection of performance measures, how the ORYX data have been analyzed and used to improve performance, and the outcomes of these activities. CONSTRUCTING COMPARISON CHARTS: A comparison chart, a graphical summary of the comparison analysis, consists of actual (or observed) rates, expected rates, and expected ranges (upper and lower limits) for a given time frame. The expected range describes the degree of certainty that a given point is different from the average score (population). THE USE OF COMPARISON CHARTS: Comparison charts are primarily useful for telling an HCO whether one of its selected performance measures may be evidencing one of the three types of measurement outcomes: exemplary performance, average performance, or substandard performance (indicating an opportunity for improvement). The comparison charts compare an HCO's outcomes to those of its comparison group or to its risk-adjusted data. The charts provide guidance to an HCO about whether it should continue to monitor a process so as to maintain its current level of performance or whether it should try to improve its current performance.

Accreditation↗

Application of attribute control charts to risk-adjusted data for monitoring and improving health care performance.

This article proposes a new class of control charts that may be used for monitoring and improving the quality of care. Unlike conventional control charts that rely on observed performance data, these charts use risk-adjusted data in addition to the observed data. The resulting time-ordered charts are capable of reducing time-to-time variation that may stem from uncontrollable changes in patient mix over time. Depending on how observed and risk-adjusted data are combined, proposed charts are categorized under the framework of either additive or multiplicative models. Risk-adjusted rates are obtained using multivariate logistic regression models. It was found that the risk-adjusted control charts could be effective in reducing biases that arise from variation in patient mix. These charts can potentially achieve higher sensitivity and specificity compared with ordinary control charts.

Cesarean Section↗

Application of variables control charts to risk-adjusted time-ordered healthcare data.

In a previous article (M. K. Hart, Qual Manag Health Care. 2003;12(1):5-19), the authors presented risk-adjusted control charts applicable for attributes data. The present article discusses a similar class of control charts applicable for variables data that are often skewed. The key feature of these charts is their application of risk-adjusted data in addition to actual performance data. The resulting charts should decrease the occurrence of both type I and type II errors as compared to the unadjusted control charts. This article presents several control charts that vary in the data transformation and combination approaches. Data depicting hospital length of stay following coronary artery bypass graft procedures were used to illustrate the use of transformed and risk-adjusted control charts.

Confounding Factors, Epidemiologic↗