An approach for the application of statistical process control techniques for quality improvement of treated water

Desalination ◽  
2007 ◽  
Vol 213 (1-3) ◽  
pp. 273-281 ◽  
Author(s):  
E.M. Smeti ◽  
N.C. Thanasoulias ◽  
L.P. Kousouris ◽  
P.C. Tzoumerkas
Keyword(s):  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Treated Water ◽  
Control Techniques

Statistics in quality improvement: Measurement and statistical process control

2021 ◽  
Vol 31 (5) ◽  
pp. 539-547
Author(s):  
Heather A. Wolfe ◽  
April Taylor ◽  
Rajeev Subramanyam
Keyword(s):  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process

Successfully reducing newborn asphyxia in the labour unit in a large academic medical centre: a quality improvement project using statistical process control

2018 ◽  
Vol 27 (8) ◽  
pp. 633-642 ◽  
Author(s):  
Rikke von Benzon Hollesen ◽  
Rie Laurine Rosenthal Johansen ◽  
Christina Rørbye ◽  
Louise Munk ◽  
Pierre Barker ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Clinical Care ◽  
University Hospital ◽  
Vacuum Extraction ◽  
Statistical Process ◽  
Academic Medical Centre ◽  
Improvement Project ◽  
Care Bundles

BackgroundA safe delivery is part of a good start in life, and a continuous focus on preventing harm during delivery is crucial, even in settings with a good safety record. In January 2013, the labour unit at Copenhagen University Hospital, Hvidovre, undertook a quality improvement (QI) project to prevent asphyxia and reduced the percentage of newborns with asphyxia by 48%.MethodsThe change theory consisted of two primary elements: (1) the clinical content, including three clinical bundles of evidence-based care, a ‘delivery bundle’, an ‘oxytocin bundle’ and a ‘vacuum extraction bundle’; (2) an implementation theory, including improving skills in interpretation of cardiotocography, use of QI methods and participation in a national learning network. The Model for Improvement and Deming’s system of profound knowledge were used as a methodological framework. Data on compliance with the care bundles and the number of deliveries between newborns with asphyxia (Apgar <7 after 5 min or pH <7) were analysed using statistical process control.ResultsCompliance with all three clinical care bundles improved to 95% or more, and the percentages of newborns with pH <7 and Apgar <7 after 5 min were reduced by 48% and 31%, respectively. In general, the QI approach strengthened multidisciplinary teamwork, systematised workflow and structured communication around the deliveries. Changes included making a standard memo in the medical record, the use of a bedside whiteboard, bedside handovers, shared decisions with a peer when using an oxytocin infusion and the use of a checklist before vacuum extractions.ConclusionThis QI project illustrates how aspects of patient safety, such as the prevention of asphyxia, can be improved using QI methods to more reliably implement best practice, even in high-performing systems.

scholarly journals Statistical process control part 1: a primer for using statistical process control in health care process improvement

2009 ◽  
Vol 33 (3) ◽  
pp. 408 ◽  
Author(s):  
Tamara G Chetter
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Process Control ◽  
Patient Care ◽  
Statistical Process Control ◽  
Care Process ◽  
Quality Improvement Effort ◽  
Statistical Process ◽  
Measurement Analysis ◽  
Quality Of Patient Care

QUALITY IMPROVEMENT is increasingly important for health care organisations both nation-wide and internationally. There is greater recognition of both the variances in patient care and the gaps between evidence-based research and current practice. At the same time, demand, not only for the quantity of services, but for higher quality services, continues to grow. Realising this, most major hospitals across Australia are initiating the redesign of hospital processes in order to maximise the timeliness and quality of patient care. But changing a process does not always result in an improvement.1,2 For this reason, a key component of any quality improvement effort is the robust measurement, analysis, and interpretation of appropriate clinical outcomes and processes, to ensure beneficial changes occur.

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