Statistical Support for Health Care Quality and Management

Subject description

Concepts and methodologies of continuous process improvement

history and "philosophy" (Deming, Shewhart, Wheeler)
systems and standards (Six Sigma, ISO)

Official data sources in health care management

quality and safety indicators
health-related classifications (ICD, ICF)

Good data visualisation for business reporting and decision support

selected graphical displays (dot plots, mosaic plots, heatmaps, sparklines)
combining tables and graphics, dashboards
principles of graphical design and typesetting of documents

Fundamental statistical methods for quality control in health care

league tables, funnel plots and control charts
outlier detection, univariate distribution fitting
analysis of means
CUSUM charts and sequential probability ratio testing
risk estimation and adjustment (overview of concepts and approaches)

Selected topics from categorical data analysis, assessment scales and agreement analysis

RIDIT analysis and similar methods
analysis and display of reliability and agreement (Kappa coefficients, Cronbach's Alpha, ICC, Bland-Altman approach, Bangdiwala's observer agreement chart, concordance coefficients and plots)

Introduction to efficiency analysis

basics of stochastic frontier analysis

basics of data envelopment analysis

The subject is taught in programs

Objectives and competences

The objective of the course is to make the students familiar with the systems and methodologies of continuous quality improvement and data-analytic indicators that are used in the field of health care, with the importance and methods of good data visualisation for the purpose of reporting and managerial decision support in health care, and with statistical models and psychometric methods for quality management in health care.

Teaching and learning methods

Lectures, tutorials, essays, consultations

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Knowledge and understanding:

being familiar with the major systems and methodologies of continuous quality improvement and their role in health care;
being familiar with the fundamental quality and safety indicators in health care;
being familiar with the concept and structure of the ICD-10 and the ICF;
knowing the methods and principles of good data visualisation and being able to apply them to health care data;
knowing how to design useful dashboard;
understanding and being able to apply appropriate statistical methods of quality control in health care;
knowing how to analyse metric characteristics of scales used in health care;
knowing how to statistically analyse and graphically display agreement between raters or methods;
being familiar with the basic concepts and methods of hospital efficiency analysis.

Application:

introduction and implementation of quality control in health care and other public services;
business reporting and decision support in health care and other public services;
research in health care quality, epidemiology, health policy, public health and related fields.

Reflexion:

awareness of the role of data analysis and visualisation in managing quality in health care and other public services;
awareness of the importance of statistics as decision support tool at all levels within health care and other public services;
awareness of the importance of adequate use and further development of statistical and graphical methods for further progress of health care.

Transferable skills:

in-depth understanding of systems and methodologies of continuous quality improvement;
familiarity with both universal health-related classifications and their use;
ability to properly visualise data for the purpose of business reporting and decision support;
ability to apply statistical methods for outlier detection and agreement analysis;
familiarity with the basics of economic efficiency analysis-

Basic sources and literature

Knjige / Monographs:

Wheeler D.J. (2000). Understanding Variation (2nd ed.). Knoxville, TX: SPC Press.
Few S. (2009): Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press.
Nelson P.R., Wludyka P.S., Copeland K.A.F. (2005): The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions. Philadelphia, PA: SIAM.
Winkel P., Zhang N.F. (2007): Statistical Development of Quality in Medicine. Chichester: John Wiley.
von Eye A., Mun E.Y. (2005): Analyzing Rater Agreement: Manifest Variable Methods. Mahwah, NJ: Lawrence Erlbaum.
Jacobs R., Smith P.C., Street A. (2006): Measuring Efficiency in Health Care: Analytic Techniques and Health Policy. New York: Cambridge University Press.

Članki / Articles:

de Koning H., Verver J.P.S., van den Heuvel J., Bisgaard S., Does R.J.M.M. (2006): Lean Six Sigma in healthcare. Journal for Healthcare Quality, 28(2): 4-11.
Guthrie B., Love T., Fahey T., Morris A., Sullivan F. (2005): Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators. Quality and Safety in Health Care, 14: 450-454.
Mohammed M.A., Cheng K.K., Rouse A., Marshall T. (2001): Bristol, Shipman, and clinical governance: Shewhart’s forgotten lessons. Lancet, 357: 463-467.
Spiegelhalter D., Grigg O., Kinsman R., (2003): Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery. International Journal of Quality in Health Care, 15: 7-13
Sermeus W., Delesie L. (1996): Ridit analysis on ordinal data. Western Journal of Nursing, 18(3): 351-359.