Using control charts to assess performance measurement data Export

@article{Lee:2002:Jt-Comm-J-Qual-Improv:11838300, abstract = {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 the organization's survey. During its survey, the health care organization (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. WHAT DO CONTROL CHARTS DO? Control charts indicate whether an HCO's process is in statistical control (that is, stable insofar as only common cause variation exists) or out of statistical control (that is, unstable insofar as special cause variation exists). With the presence of special cause variation, the HCO should not make any change in its processes until the special cause is identified and eliminated. CHOOSING THE CORRECT CONTROL CHART: An HCO can use many different control charts. Selecting the correct control chart type for the type of data collected makes interpretation more sensitive for detecting special cause variation. The ORYX measures are calculated as proportions (rates), ratios, and means (continuous variables data, such as average length of stay), and this information forms the basis for selecting the correct type of control chart. In addition, the average rate (especially for rare event measures) and the average number of cases need to be considered when selecting the control chart type for small population measures.}, author = {Lee, K. and Mcgreevey, C.}, citeulike-article-id = {2758450}, citeulike-linkout-0 = {http://www.hubmed.org/display.cgi?uids=11838300}, journal = {Jt Comm J Qual Improv}, keywords = {process\_control}, month = {Feb}, number = {2}, pages = {90--101}, posted-at = {2008-05-05 18:28:08}, priority = {2}, title = {Using control charts to assess performance measurement data}, url = {http://www.hubmed.org/display.cgi?uids=11838300}, volume = {28}, year = {2002} }

TY - JOUR ID - Lee:2002:Jt-Comm-J-Qual-Improv:11838300 L3 - citeulike-article-id:2758450 N2 - 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 the organization's survey. During its survey, the health care organization (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. WHAT DO CONTROL CHARTS DO? Control charts indicate whether an HCO's process is in statistical control (that is, stable insofar as only common cause variation exists) or out of statistical control (that is, unstable insofar as special cause variation exists). With the presence of special cause variation, the HCO should not make any change in its processes until the special cause is identified and eliminated. CHOOSING THE CORRECT CONTROL CHART: An HCO can use many different control charts. Selecting the correct control chart type for the type of data collected makes interpretation more sensitive for detecting special cause variation. The ORYX measures are calculated as proportions (rates), ratios, and means (continuous variables data, such as average length of stay), and this information forms the basis for selecting the correct type of control chart. In addition, the average rate (especially for rare event measures) and the average number of cases need to be considered when selecting the control chart type for small population measures. IS - 2 JF - Jt Comm J Qual Improv EP - 101 TI - Using control charts to assess performance measurement data VL - 28 SP - 90 KW - process_control AU - Lee, K AU - Mcgreevey, C PY - 2002/Feb// UR - http://www.hubmed.org/display.cgi?uids=11838300 ER -