Search Results - "Does, Ronald J. M. M."

The Search for an Outcome Variable That Measures Both Quality and Processes in Cardiac Surgery: Comparing the Quality Process Index and Mortality by Hoefsmit, Paulien C, Jansen, Evert K, Does, Ronald J M M, Zandbergen, H Reinier

“…The translation of a large quantity of data into valuable insights for daily clinical practice is underexplored. A considerable amount of information is…”
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Quality and process improvement of the multidisciplinary Heart Team meeting using Lean Six Sigma by Hoefsmit, Paulien C, Schretlen, Stijn, Does, Ronald J M M, Verouden, Niels J, Zandbergen, H Reinier

“…IntroductionThe Heart Team is a multidisciplinary meeting for shared decision-making in cardiology and cardiothoracic surgery. A quality improvement project to…”
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Mixed Exponentially Weighted Moving Average-Cumulative Sum Charts for Process Monitoring by Abbas, Nasir, Riaz, Muhammad, Does, Ronald J. M. M.

“…The control chart is a very popular tool of statistical process control. It is used to determine the existence of special cause variation to remove it so that…”
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An EWMA-Type Control Chart for Monitoring the Process Mean Using Auxiliary Information by Abbas, Nasir, Riaz, Muhammad, Does, Ronald J. M. M.

“…Statistical process control (SPC) is an important application of statistics in which the outputs of production processes are monitored. Control charts are an…”
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Mixed Cumulative Sum-Exponentially Weighted Moving Average Control Charts: An Efficient Way of Monitoring Process Location by Zaman, Babar, Riaz, Muhammad, Abbas, Nasir, Does, Ronald J.M.M.

“…Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring…”
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Improving the performance of CUSUM charts by Riaz, Muhammad, Abbas, Nasir, Does, Ronald J. M. M.

“…The control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger…”
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CS-EWMA Chart for Monitoring Process Dispersion by Abbas, Nasir, Riaz, Muhammad, Does, Ronald J. M. M.

“…Control charts are the most extensively used technique to detect the presence of special cause variations in processes. They can be classified into memory and…”
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A Robust Standard Deviation Control Chart by Schoonhoven, Marit, Does, Ronald J.M.M.

“…This article studies the robustness of Phase I estimators for the standard deviation control chart. A Phase I estimator should be efficient in the absence of…”
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Robust CUSUM Control Charting by Nazir, Hafiz Zafar, Riaz, Muhammad, Does, Ronald J. M. M., Abbas, Nasir

“…Cumulative sum (CUSUM) control charts are very effective in detecting special causes. In general, the underlying distribution is supposed to be normal. In…”
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Memory-Type Control Charts for Monitoring the Process Dispersion by Abbas, Nasir, Riaz, Muhammad, Does, Ronald J. M. M.

“…Control charts have been broadly used for monitoring the process mean and dispersion. Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA)…”
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Shewhart control charts for dispersion adjusted for parameter estimation by Goedhart, Rob, da Silva, Michele M, Schoonhoven, Marit, Epprecht, Eugenio K, Chakraborti, Subha, Ronald J M M Does, Veiga, Álvaro

“…Several recent studies have shown that the number of Phase I samples required for a Phase II control chart with estimated parameters to perform properly may be…”
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On the performance of different control charting rules by Riaz, Muhammad, Mehmood, Rashid, Does, Ronald J. M. M.

“…In the literature a number of control charting rules are proposed to decide whether a process is in control or out of control. Some issues with these rules…”
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A Robust X¯ Control Chart by Schoonhoven, Marit, Does, Ronald J. M. M.

“…This article studies alternative standard deviation estimators that serve as a basis to determine the X¯ control chart limits used for real‐time process…”
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Comparison of threshold tuning methods for predictive monitoring by von Stackelberg, Paulina, Goedhart, Rob, Birbil, Ş. İlker, Does, Ronald J. M. M.

“…Predictive monitoring techniques produce signals in case of a high predicted probability of an undesirable event, such as mortality, heart attacks, or machine…”
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Predictive monitoring using machine learning algorithms and a real‐life example on schizophrenia by Huberts, Leo C. E., Does, Ronald J. M. M., Ravesteijn, Bastian, Lokkerbol, Joran

“…Predictive process monitoring aims to produce early warnings of unwanted events. We consider the use of the machine learning method extreme gradient boosting…”
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The usefulness of lean six sigma to the development of a clinical pathway for hip fractures by Niemeijer, Gerard C., Flikweert, Elvira, Trip, Albert, Does, Ronald J. M. M., Ahaus, Kees T. B., Boot, Anja F., Wendt, Klaus W.

“…Aims and objectives The objective of this study was to show the usefulness of lean six sigma (LSS) for the development of a multidisciplinary clinical pathway…”
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Process improvement in healthcare: overall resource efficiency by de Mast, Jeroen, Kemper, Benjamin, Does, Ronald J. M. M., Mandjes, Michel, van der Bijl, Yohan

“…This paper aims to develop a unifying and quantitative conceptual framework for healthcare processes from the viewpoint of process improvement. The work adapts…”
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Robust CUSUM Control Charting for Process Dispersion by Nazir, Hafiz Zafar, Riaz, Muhammad, Does, Ronald J. M. M.

“…Process monitoring through control charts is a quite popular practice in statistical process control. From a statistical point of view, a superior control…”
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A head-to-head comparison of the out-of-control performance of control charts adjusted for parameter estimation by Diko, Mandla D., Goedhart, Rob, Does, Ronald J. M. M.

“…When in-control process parameters are estimated, this can have a substantial effect on the control chart performance. In particular, it may lead to high false…”
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Enhancing the Performance of Exponentially Weighted Moving Average Charts: Discussion by Abbas, Nasir, Riaz, Muhammad, Does, Ronald J. M. M.

“…Abbas et al. (Abbas N, Riaz M, Does RJMM. Enhancing the performance of EWMA charts. Quality and Reliability Engineering International 2011; 27(6):821–833)…”
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