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.

scholarly journals Statistical process control part 2: using statistical process control to measure process improvement in patient flow

2009 ◽  
Vol 33 (3) ◽  
pp. 412 ◽  
Author(s):  
Tamara G Chetter
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Process Improvement ◽  
Patient Flow ◽  
Care Process ◽  
Statistical Process ◽  
The Impact ◽  
Control Part

THE FIRST ARTICLE in this series, ?Statistical process control part 1: a primer for using statistical process control in health care process improvement? 1 (in this issue of the Journal), introduced the basic concepts of statistical process control (SPC) and its main tool, the control chart. While this set of techniques was originally developed in the manufacturing sector, there is growing realisation in recent years that SPC (and also other quality improvement techniques, such as Six Sigma and lean thinking) can be successfully applied to health care quality improvement.2 The reason for this is that SPC is a potent and powerful, yet simple tool for tracking, and detecting any variation in, process performance over time; which creates the opportunity for health professionals to promptly respond to any improvement or deterioration in the process. Perhaps the most valuable feature of SPC techniques however, is the ability to place a change in the outcome of a process in close temporal proximity to the redesign and improvement of the process. This means SPC can reliably evaluate the effectiveness of quality improvement initiatives implemented at the front line of health service delivery, despite the complexities of the hospital system and the challenges this often poses for health services research (for example, the inability to use robust research designs). The purpose of this companion article is to therefore demonstrate the practical application of SPC in a health care organisation. Specifically, the technique of control charting was used to track the impact of patient flow process improvement interventions in a public hospital, in the hope that this will exemplify to health care professionals the value and simplicity in applying SPC as part of their daily work.

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

scholarly journals Optimum Manpower Allocation in Health Care Using Statistical Process Control

2019 ◽  
pp. 1-9
Author(s):  
William E. Odinikuku ◽  
Jephtah A. Ikimi ◽  
Ikechukwu P. Onwuamaeze
Keyword(s):  
Health Care ◽  
Process Control ◽  
Statistical Process Control ◽  
Optimal Allocation ◽  
Manpower Planning ◽  
Production Costs ◽  
Statistical Control ◽  
Statistical Process ◽  
Planning Models ◽  
Allocation Process

In many countries manpower problems in the field of health care are regular items on the agenda of policy makers. To avoid mismatches between demand of care and supply of care on national and regional levels, manpower planning models and methods are used to determine adequate numbers of medical specialists to fulfill the future demand of care. Inadequate or inefficient allocation of manpower to various departments in an organization or workplace can lead to undesired outcomes which may include: down time, reduced productivity, workers fatigue, increased production costs, etc. As a result of the above stated problem, there is need to devise a statistical model that will ensure optimal allocation of manpower. In this study, the optimum allocation of two hundred and fifty two general nurses to fifteen wards at a hospital code named WCH located in South-South geopolitical zone, Nigeria was achieved using statistical process control. The study involved the analysis of data obtained from our hub of study for a period of two months. The C-chart was used to check if the process of allocation was in control or not. The result obtained from the study showed that the manpower allocation process was out of statistical control as the allocation of the children emergency ward was outside the upper control limit of the c-chart plot.

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.

A Case Study in Quality Improvement of Precision Turned Components

1996 ◽  
Vol 210 (6) ◽  
pp. 589-595 ◽  
Author(s):  
L Carter ◽  
C Butler
Keyword(s):  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Batch Size ◽  
Just In Time ◽  
Statistical Process ◽  
Measurement Capability ◽  
Analysis Measurement ◽  
Engineering Company ◽  
Batch Sizes

The company participating in this study had recently reorganized its factory layout, moving from a traditional layout to machining cells. The company was a light engineering company; the cells consisted of turning centres, each cell catering for a different family grouping of components. An associated change in production methods was the move to ‘just-in-time manufacture’. The consequences of these changes were that the batch sizes had reduced considerably so that now the maximum batch size was 20 and the average only 3 or 4. The company had previously used statistical process control (SPC), but with such small batch sizes the practice had fallen into disuse as the traditional software was no longer appropriate. The general objective of the study was therefore to investigate their current situation and recommend a systematic approach to quality improvement. The study employed non-conformance analysis, measurement capability studies and analysis of variance leading to an appropriate statistical process control methodology. The company initially employed near to 100 per cent inspection; nevertheless, significant quality improvement was achieved.

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