Using statistical process control to improve the quality of health care

Mohammed, M A

doi:10.1136/qhc.13.4.243

Cited by 60 publications

(53 citation statements)

References 14 publications

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“…The exponential distribution is the most commonly used distribution to deal with the highly skewed distributions particularly to model the time for the occurrence of the events in regular intervals. The use of exponential distribution for modeling the time‐between‐events in the manufacturing process has attracted the attention of the quality control researchers for improving the product quality and such charts are known as the t‐charts . Ahmad et al developed a new monitoring scheme for the double moving average chart using the exponential distribution under the exponentially weighted moving average (EWMA) technique.…”

Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

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In this article, a new t-chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t-chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in-control average run lengths. The proposed t-chart is compared with the existing chart by using the out-of-control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.

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Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

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“…We wanted to find the significant outlier among the surgeons to use as a benchmark. Based on some less statistically powerful charts, such as histogram or even box plots, the performance of an outlier physician may have been misinterpreted as a normal performance and vice versa [4]. However, using SPC according to the above five steps proved surgeon A is an excellent outlier and benchmark.…”

Section: Discussionmentioning

confidence: 99%

“…The challenge is thus to select a simple but useful statistical tool for accurately and confidently evaluating an individual physician who may be an excellent outlier. Although easily applied, box plots and histograms lack the statistical power necessary to detect actual differences or non-random variations [4].…”

Section: Introductionmentioning

confidence: 99%

Statistical process control as a tool for controlling operating room performance: retrospective analysis and benchmarking

Chen

Chang

et al. 2010

Evaluation Clinical Practice

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“…The statistical significance of changes is supported by mathematical rules that indicate when the data are not representing a random occurrence. The rules on chart performance have been widely described previously [25], [30], [31], [32], [33], [38], [39], [40]. A brief explanation of this rules are shown at the legend of figure 1.…”

Section: Methodsmentioning

confidence: 99%

“The 3/3 Strategy”: A Successful Multifaceted Hospital Wide Hand Hygiene Intervention Based on WHO and Continuous Quality Improvement Methodology

et al. 2012

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BackgroundOnly multifaceted hospital wide interventions have been successful in achieving sustained improvements in hand hygiene (HH) compliance.Methodology/Principal FindingsPre-post intervention study of HH performance at baseline (October 2007– December 2009) and during intervention, which included two phases. Phase 1 (2010) included multimodal WHO approach. Phase 2 (2011) added Continuous Quality Improvement (CQI) tools and was based on: a) Increase of alcohol hand rub (AHR) solution placement (from 0.57 dispensers/bed to 1.56); b) Increase in frequency of audits (three days every three weeks: “3/3 strategy”); c) Implementation of a standardized register form of HH corrective actions; d) Statistical Process Control (SPC) as time series analysis methodology through appropriate control charts. During the intervention period we performed 819 scheduled direct observation audits which provided data from 11,714 HH opportunities. The most remarkable findings were: a) significant improvements in HH compliance with respect to baseline (25% mean increase); b) sustained high level (82%) of HH compliance during intervention; c) significant increase in AHRs consumption over time; c) significant decrease in the rate of healthcare-acquired MRSA; d) small but significant improvements in HH compliance when comparing phase 2 to phase 1 [79.5% (95% CI: 78.2–80.7) vs 84.6% (95% CI:83.8–85.4), p<0.05]; e) successful use of control charts to identify significant negative and positive deviations (special causes) related to the HH compliance process over time (“positive”: 90.1% as highest HH compliance coinciding with the “World hygiene day”; and “negative”:73.7% as lowest HH compliance coinciding with a statutory lay-off proceeding).Conclusions/SignificanceCQI tools may be a key addition to WHO strategy to maintain a good HH performance over time. In addition, SPC has shown to be a powerful methodology to detect special causes in HH performance (positive and negative) and to help establishing adequate feedback to healthcare workers.

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Using statistical process control to improve the quality of health care

Cited by 60 publications

References 14 publications

Design of a t‐chart using generalized multiple dependent state sampling

Design of a t‐chart using generalized multiple dependent state sampling

Statistical process control as a tool for controlling operating room performance: retrospective analysis and benchmarking

“The 3/3 Strategy”: A Successful Multifaceted Hospital Wide Hand Hygiene Intervention Based on WHO and Continuous Quality Improvement Methodology

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