Course: Statistical Support for Health Care Quality and Management

Course type: elective
Lecturer: Gaj Vidmar, Ph.D., Associate Professor
 

Study programme and level Study field Academic year Semester
Applied statistics, second level All modules 1st or 2nd 1st or 2nd

For the timeline see Curriculum.

Prerequisites:

  • Inscription to the academic year. 
  • Passing grade on the essay is a prerequisite for oral examination.

Content (Syllabus outline):
Concepts and methodologies of continuous process improvement

  • leading authors (Deming, Shewart, Wheeler)
  • systems and standards (Six Sigma, ISO etc.)

Data-analytic aspects of quality in health care

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

Good data visualization for business reporting and decision support

  • sparklines and other micro-charts
  • dot-plots; sieve and mosaic plots; heat-maps and related charts; combining tables and graphics; dashboards;
  • general principles of graphical design of documents

Key statistical methods for quality control in health care

  • league tables; funnel and double square root (Shewart) plots
  • statistical tests for outlier detection
  • analysis of means (ANOM)
  • CUSUM charts and sequential probability ratio testing (SPRT)

Selected topics in categorical data analysis, assessment scales and method/rater agreement

  • RIDIT analysis; dominance (Cliff's delta)
  • analysis and display of reliability and agreement (Kappa coefficients; Cronbach's Alpha and ICC; Bland-Altman approach; regression methods for agreement analysis; fitting univariate distributions; Bangdiwala's observer agreement chart and concordance plots)

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 use in the field of health care, with the importance and methods of good data visualization for the purpose of reporting and managerial decision support in health care, and with modern statistical and psychometric methods for quality control in health care.

Intended learning outcomes:
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 visualization and being able to apply them to health care data;
  • knowing how to design dashboard;
  • understanding and being able to apply modern statistical methods of quality control in health care;
  • knowing how to analyze metric characteristics of scales used in health care;
  • knowing how to statistically analyze and graphically display agreement between raters or method.

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 field.

Reflexion:

  • awareness of the role of data analysis and visualization in promoting and 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 visualize data for the purpose of business reporting and decision support;
  • ability to apply statistical methods for outlier detection and agreement analysis.

Contact

Main contact:
e-mail: info.stat (at) uni-lj.si

Contact for administrative questions (enrolment, technical questions):
Katarina Erjavec Drešar
University of Ljubljana, Faculty of electrical engineering, Tržaška cesta 25, 1000 Ljubljana.
room num.: AN012C-ŠTU
phone: 01 4768 209
e-mail: katarina.erjavec-dresar (at) fe.uni-lj.si