[Comparative quality measurements part 1: run charts].
Quality assessment may be based on data of quality indicators. There are two main approaches for comparative quality measurements: comparison of data of the same service at different points over time or comparison of data of different services at the same time. Risk adjustment and standardisation must be performed and random variation must be adequately taken into account. In Statistical Process Control (SPC) theory common cause and special cause variation are distinguished. Processes in statistical control are stable and predictable. If processes exhibit special cause variation the management should investigate the reasons for this and manage the causes. Run charts as simple tools to display and analyse data of processes and outcomes over time are discussed in this article. They can be used for self-comparison. The following two parts of this three-part series explain control charts and funnel plots.
Clinical Research Center for Hair and Skin Science, Klinik für Dermatologie, Venerologie und Allergologie, Berlin, Germany. jan.kottner@email.de
Jan
J
Kottner
D000704:Analysis of Variance; D003625:Data Collection / Q000706:statistics & numerical data; D005858:Germany; D006801:Humans; D009732:Nursing Care / Q000592:standards* / Q000706:statistics & numerical data*; D010043:Outcome and Process Assessment, Health Care / Q000592:standards / Q000706:statistics & numerical data*; D017060:Patient Satisfaction / Q000706:statistics & numerical data; D011785:Quality Assurance, Health Care / Q000458:organization & administration* / Q000706:statistics & numerical data*; D011786:Quality Control; D058996:Quality Improvement / Q000592:standards / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care / Q000458:organization & administration* / Q000706:statistics & numerical data*; D020379:Risk Adjustment / Q000592:standards / Q000706:statistics & numerical data