Measuring Quality: Statistical Tools, Qualitative and Quantitative Measures

Running head: MEASURING QUALITY
Measuring Quality: Statistical Tools, Qualitative and Quantitative Measures
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MEASURING QUALITY
Statistical Process Control in patient-centred control home aims not only improve the cost,
quality, provider and patient experience but also helps in analysing the theory of variation
measured over time with high level of performance (Magar & Shinde, 2014). However, in
research the quality stools including SPC for process improvements has 7 tools are often
called as “The Basic Seven” or “The Old Seven” as emphasized by Ishikawa.
1. Pareto Chart – the significant factors are highlighted
2. Control Chart –time series charts often used in health care with control limits for
expected random variation
3. Cause/ effect Diagram/ Fishbone Chart –strategizes and recognizes the causes for a
problem by sorting them in categories
4. Histogram –shows the set of data happens in a time using frequency distribution
5. Check Sheet –for collecting and examining the data
6. Stratification/ Run Chart –the data is separated from sources so that a pattern an be
seen for analysis
7. Scatter Plot –quantitative variables that are plotted on x and y axis to examine for a
relationship between the two
Broadly in healthcare, control charts are used as they detect variations faster than any other
statistical method. Also, as per Medline database found, the hits are for 1951–88, two for
1989–91, 26 for 1992–5, and 71 for 1996–2004 with many recent publications (Leclère, et
al., 2017). For example, in hours a particular lab procedure is followed, ARL (Average run
length) is the common performance metric that is used. Coory, Duckett & Sketcher-Baker
(2007) gathered a comparative analysis of cross sectional analysis to monitor quality of
hospital care with administrative data had seen no outliers for 2 years UCL (3σ and 3-low-
outliers) and LCL (2σ and 1-high-outlier).