Total quality management (TQM) movement in public health

被引:7
|
作者
Jarrett, Jeffrey E. [1 ]
机构
[1] Univ Rhode Isl, Management Sci Finance Dept, Kingston, RI 02881 USA
关键词
Auto correlated time series; Average run length (ARL); Common and special causes; Multivariate quality control (MQC); Statistical quality control; Total quality management (TQM);
D O I
10.1108/IJQRM-12-2013-0193
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Purpose - The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services. Design/methodology/approach - The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well. Findings - Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present. Research limitations/implications - Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs. Practical implications - Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques. Social implications - Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes. Originality/value - The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.
引用
收藏
页码:25 / 41
页数:17
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