The Use of Control Charts in Health-Care and Public-Health Surveillance

By William H. Woodall. 2006

621 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Medical Laboratory Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a509686cde9f1e5b5091b2641c0f1826a137cef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Medical Laboratory Technology

In Statistics and Probability

Citations in the corpus

Current research on profile monitoring (2007, background): “Finally, it was pointed out by Woodall (2006) that profi le monitoring ideas could be applied in public health surveillance where the interest is in detecting clusters of increased disease rates over time.”
Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014, background): “Woodall (2006) and Tsui et al. (2008) reviewed this area, but the number of papers and the amount of interest has increased considerably.”
Is Average Run Length to False Alarm Always an Informative Criterion? (2008, background): “For recent reviews on new applications, see Fienberg and Shmueli (2005), Tartakovsky et al. (2006), Woodall (2006), and the references therein.”
MDEWMA chart: an efficient and robust alternative to monitor process dispersion (2013, background): “Although first proposed for the manufacturing industry, control charts have now been applied in a wide variety of disciplines, such as in nuclear engineering [1], health care [2], education [3], analytical laboratories [4], etc.”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “Prior work has described how to address differences between health-related control chart applications and industrial applications 58 .”
Likelihood-Based EWMA Charts for Monitoring Poisson Count Data With Time-Varying Sample Sizes (2012, background): “The objective is to detect the change occurring at an unknown time point as early as possible after it has occurred while controlling the rate of false alarms (cf. Woodall 2006).”
EARLY DETECTION OF A CHANGE IN POISSON RATE AFTER ACCOUNTING FOR POPULATION SIZE EFFECTS (2011, background): “For introductory discussions of bio and syndromic surveillance as well as their statistical challenges, see Fienberg and Shmueli (2005), Woodall (2006), and Tsui et al. (2008).”