Bringing Change by Measuring Impact

Health Information Management and Applied Epidemiology for Health Care Workers in South Africa

sa-data

Module 5

Health Information Systems and Data Management

Learning Objectives

At the end of Module 5, you will be able to:

  • Describe what a health information system is, what its components are, and how it can be used.
  • Describe the tools and methods used in the four steps of the data cycle in South Africa’s health information system.
  • Discuss the challenges that can occur at each step of the data cycle.
  • Describe data-management procedures and practices that help us to ensure security of health data.
  • Define validity, reliability, and other attributes of data quality.
  • Explain the importance of data quality, and the implications of poor data quality.
  • Outline and give examples of techniques for improving data quality.

In this module, we delve further into data and information systems, and the importance of good data to public health practice. We learn about the components of a health information system (HIS), tools and methods we can use in the data cycle, and ways to ensure reliable, valid, high-quality data. We also discuss what can happen when our data are poor, and how data are generated and used in South Africa.

Pre-test (10 minutes)

Part 1: Overview of Health Information Systems (15 minutes)

Part 2: Revisiting the Data Cycle: South Africa’s Health Information System (45 minutes)

Part 3: Challenges in the Data Cycle in South Africa (15 minutes)

Part 4: Data Management: Ensuring Security of Health Data (15 minutes)

Part 5: Data Quality (30 minutes)

Part 6: Data Quality Assessment and Assurance (45 minutes)

  • Summary

    In this module, we conducted an in-depth exploration of health information systems, and how they relate to data management.

    We started in general terms by learning in Part 1 what a health information system is, what its components are, and how it can be used.

    We then moved on to more specific information; in Part 2, we revisited the data cycle that we learnt about in Module 1, and looked at how South Africa’s health information system fits into that. We reviewed each step of the data cycle—collection and storage; processing; reporting; and analysis, interpretation, and use—specifically looking at how each would be carried out in South Africa’s health information system, and with South African forms and tools.

    In Part 3, we looked at some of the challenges that can occur at each step of the data cycle.

    In Part 4, we discussed ways to keep data safe and secure. Our discussion included learning about the three pillars of a secure health system: integrity, availability, and confidentiality.

    This led us to Part 5, in which we explored further issues around data quality, including validity and reliability, as well as completeness, precision, timeliness, and integrity. We then looked at the implications of poor data quality: How does poor data quality affect our work?

    Finally, in Part 6, we discussed ways to ensure and improve data quality, starting with assessment. We talked about ways to prevent errors from occurring in the first place, as well as several accuracy-enhancing principles: training; logistics and preparation; user-friendly, standardised tools; feedback; use of information; and personalised contact.

    This module has helped us to learn more about data management and quality in the context of health information systems. We will apply this knowledge in later modules, when we discuss data presentation.

Post-test (10 minutes)

  • References

    AbouZahr C, Adjei S, Kanchanachitra C, 2007 From data to policy: good practices and cautionary tales Lancet 369: 1039–46

    Beaumont, R. Types of Health Information Systems (IS). 2008.
    http://www.fhi.rcsed.ac.uk/rbeaumont/virtualclassroom/chap12/s2/systems_new.pdf

    Data Quality and Management Training – for Data Captures. CDC, PEPFAR, and the Aurum Institute. (Needs more citation)

    Denzin, N. K. (1970). The Research Act in Sociology. Chicago: Aldine. Found in http://www.referenceworld.com/sage/socialscience/triangulation.pdf

    Kenya Electronic Medical Record Systems: An Introduction for EMR System Users (I-TECH Training). July 2012. Session 10: EMR System and Data Security, Privacy, and Confidentiality

    Kenya Electronic Medical Record Systems: An Introduction for EMR System Users (I-TECH Training). July 2012. Session 3: Overview of Data Quality

    Kenya Electronic Medical Record Systems: An Introduction for EMR System Users (I-TECH Training). July 2012. Session 7: EMR Systems and Data Quality Assurance and Control

    Kenya Electronic Medical Record Systems: An Introduction for EMR System Users (I-TECH Training). July 2012. Session 1: Health Information Systems and Structures

    Osler, M and Boulle, A. Three Interlinked Electronic Registers (TIER.Net) Project: A Working Paper. September 2010.

    Principles of Public Health Surveillance. Dr. Lazarus Kuonza. Basic Applied Epidemiology for Health Professionals, Week 1 of 2, East London: 3 – 7 August 2015 (FELTP Materials)