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Title Health Information - Making Sense of It All
Author Bryan Hall
AAMS Webmaster
Publication Status 1. Published: January 2002
Review Status GR
Copyright Copyright of this article is vested in the author. Permissions for reprints or republications must be obtained in writing from the copyright holder. This article has been republished here with permission from the copyright holder.

Health Information - Making Sense of it all

Some Facts

Multiple Observations & Evidence

Data Attributes & Generalisation

Health Care Policy must be seen in the broader economic, cultural and political context. The complex nature of health policy and administration has inevitably led to a reliance on theories of mathematical statistics for setting spending priorities.

To formulate information in a scientific manner it is essential to have basic facts stated in numerical terms. However, it is not necessary to enumerate each unit in the universe in order to arrive at an acceptable estimate for the total. A carefully designed sample may provide the necessary information (Raj; 1968). Statistical methods can be relied upon on only in so far as a rigorous and competent analysis is conducted on a carefully designed sample space that is representative of the characteristics of either the total population or a specific subset of the population for which indicative information is sought. The theory of Mathematical Statistics is concerned with obtaining all and only those conclusions for which multiple observations are evidence. Mathematical statistics is not merely the handling of facts stated in numerical terms (Kaplan; 1961).

The purpose of inductive statistics is to provide methods for making statistical inferences about a population based on a collection of sampled individuals. Statistical inferences are necessarily probabilistic in nature. The testing of an appropriate hypothesis relating to measurable characteristics is central to statistical decision making and consequently, when applying statistical methods, it is essential to carefully and precisely define the problem to be solved.

To change a casual observation into useful information or data requires the detailed reporting of at least the following attributes of the observation:

At least three important types of data generalisation commonly take place:

These procedures of abstraction and generalisation significantly affect the utility of data for analytic purposes. Certain types of detail present in the original data may be lost. It is important to establish the extent and characteristics of the detail lost in the process of generalisation as this affects the nature of the thematic content of the information (Sinton; 1978).

Information Systems

The US Congress Office of Technology Assessment report Protecting Privacy in Computerised Medical Information advises that in addition to patient data confidentiality and security considerations, according to the Institute of Medicine, content, data-exchange, and vocabulary standards must be developed in order to implement a computerized system for health care information. Such standards are necessary for transmitting records and aggregating information from many sources, either for longitudinal records for individual patients or for databases of secondary records to be used for research or epidemiologic purposes.

The Report A National Model for the Collection and Analysis of a Minimum Data Set with Outcome Measures for Private, Hospital-based, Psychiatric Services contains a most insightful analysis concerning the development of new information systems. Section 8.2 from this report is paraphrased below:

Any attempt to develop complete and comprehensive information systems ... is likely to entail considerable risk of failure. Gilb has discussed alternative development methodologies in detail and presents a convincing argument that in such circumstances an evolutionary approach is the only way to proceed. By this, Gilb means that we should identify key stake-holders core information requirements and the critical performance attributes of the system that, were they not to be met, could render the system unacceptable. A system which meets those requirements should be developed and implemented first and then used as the basis for further developments.

The recommended systems development strategy is therefore based on Gilb's model of evolutionary software development. Under this approach, the initial prototype applications are built on the basis of the most well understood components of the stake-holders' requirements. This provides a solid architectural foundation for building the software tools as the full requirements become clearly defined.

The development and implementation of information systems is in any case, an inherently iterative process. The initial specification of an information model and functional requirements is essentially an hypothesis about what might be required. The implementation of a system based on those requirements is to some extent an experiment. By using an evolutionary model of software development, with close attention being paid to users' experience with the new system, developers are better able to build effective systems which meet user's needs. Any expectations that a system can be built and implemented "once and for all" must be abandoned for, once implemented, use of the system by the key stake-holders will change their understanding of their functional requirements. This is the principal reason why the implementation of the MMS will not be complete at the end of the first phase. At that point, the system the key stake-holders, through their representatives on the Working Group, thought was required will have been implemented. The key task in the second phase is to refine that first version of the system on the basis of Hospitals' Funds and other stake-holders' experience of using an actual system.

International Diseases and Health Classifications

The United Nations Statistical Commission maintains and develops economic and social classifications across a broad range of areas, including economics, demographics, labour, health, education, social welfare, geography, environment and tourism. The classifications are partitioned into Reference, Derived and Related classifications.

Relevant classifications include:

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Australian Bureau of Statistics

In Australia the Australian Bureau of Statistics (ABS) are responsible for Demography Statistics, which the Macquarie Dictionary defines as, the science of vital and social statistics, as the births, deaths, diseases, marriages, etc of populations. The ABS conducts a national census every five years which collects a range of personal, family and household information about the Australian population. The ABS recognise that classification is one of the cornerstones of statistics and that without the accurate and systematic arrangement of data according to common properties, statistical output can not be comparable. Consequently the ABS have published standardised classification systems and methods in many disparate fields.

Australian Standard Research Classification

The Australian Standard Research Classification (ASRC) is the collective name for a set of three related classifications developed for use in the measurement and analysis of research and experimental development (R & D) undertaken in Australia, both in the public and private sectors. Use of these classifications ensures that R & D statistics and statistics collected from higher education institutions are useful to governments, educational institutions, and other organisations such as scientific, professional business and community groups and private individuals.

Australian Standard Geographical Classification

The Australian Standard Geographical Classification (ASGC) provides a common framework of statistical geography and thereby enables the production of statistics which are comparable and can be spatially integrated. In practice, statistical units such as households and businesses are first classified or assigned to a geographical area in one of a number of ASGC structures. Data collected from these statistical units are then compiled into ASGC defined geographic aggregations which, subject to confidentiality restrictions, are then available for publication. The classification structures used by the ASGC are:

The various geographical areas, or spatial units, which build the different classification structures are as follows:

Australian Standard Classification of Drugs of Concern

The ABS have also developed the Australian Standard Classification of Drugs of Concern (ASCDC) for use with data relating to drugs of concern. Generally, the ASCDC is designed to classify chemical substances which are of concern because they alter physiological processes to produce a psychoactive effect, to enhance performance or image, or to act as a detoxifying agent or antidote. Drugs of concern are defined as:

Any chemical substances for which policies and programs aimed at reducing drug related harm or reducing the availability of drugs have been developed, or which have otherwise been identified by key stake-holders in the health, welfare, and crime and justice sectors to be of current concern in the Australian context.

This definition clearly includes not only drugs subject to legal restrictions but also legally obtainable drugs for which there may or may not be harm reduction strategies are in place.

National Health Surveys

The ABS periodically publish a review of statistical information arising from the National Health Survey. The most recent review provides an overview of results from the 1995 National Health Survey (NHS) it includes very broad indicators of

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Australian Institute of Health and Welfare

The Australian Institute of Health and Welfare were set up in 1987 under the Australian Institute of Health and Welfare Act. AIHW generate data and publish reports and discussion papers concerning the health and welfare of Australians.

Data capture collection and processing procedures vary among the States and Territories. The AIHW manage a number of projects to reduce data variation and ensure the collection of uniform information.

National Health Data Dictionary

The NHDD contains the data definitions currently formally approved by the National Health Information Management Group (NHIMG). Under the National Health Information Agreement (NHIA), the NHDD is the authoritative source of health data definitions used in Australia where National consistency is required. It is designed to improve the comparability of data across the health arena.

National Minimum Data Set

The NMDS is a core set of data definitions agreed by the relevant national information management group for collection and reporting at a national level. A NMDS is contingent upon a national agreement to collect uniform data and to supply it as part of the national collection, but does not preclude agencies and service providers from collecting additional data to meet their own specific needs.

The AIHW run a number of information portals dealing with:

Aged Care, Burden of Disease, Cancer, Cardiovascular Health, Children and Youth, Collaborating Units , Data Development, Data Standards, Dental Health, Disability, Drugs and Alcohol, Expenditure, General Practice, Hospital Data, Housing & Homelessness, Indicators, Immunisation Research, Indigenous People, Injuries, Knowledgebase, Labour Force, Mental Health, NHPA - Health Priorities, Perinatal Health, Population Health, Rural Health

The Knowledgebase

The Knowledgebase is an electronic register of Australian health, community services, housing and related data definitions and standards maintained by the AIHW. The Knowledgebase contains both the definitions themselves and tools for searching and grouping data definitions with the National Health Information Model and National Minimum Data Sets.

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Australian Institute of Health and Welfare Data Online

National Cardiovascular Disease Database

The NCDD provides access to the data held by the National Centre for Monitoring Cardiovascular Disease at the Australian Institute of Health and Welfare

Related Links:

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National Hospital Morbidity Data Cubes

Related Links:

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Other Health Databases

Australian Drug Information Network

The Australian Drug Information Network (ADIN) was funded for four years from May 1999 to May 2003 by the Commonwealth Government as part of its National Illicit Drug Strategy. ADIN provides a central point of access to quality Internet-based alcohol and drug information provided by prominent organisations in Australia and Internationally.

Related Links:

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Health Wiz

HealthWIZ is a Social Health Database application package distributed on CD-ROM. It combines social health data with graphing and mapping tools into a single package. Tables can be constructed based on a large number of constituent data variables such as age, sex, birthplace, cause of death, etc). Most of these variables have national coverage. HealthWIZ is used for analysing statistical / health patterns in its constituent data sets which include or are derived from data sets originating from:
  • Population Censuses
  • Medicare Claims
  • Medicare Cancer Screening
  • Immunisation status
  • Deaths
  • Hospital Use
  • DSS-Centrelink
  • Veterans’ Affairs
  • Aged Care
  • Child Care
  • National Cancer
  • State Cancer
  • Dementia
  • Hospital Capacity (Establishments)
  • Social Health Atlas
  • Standardisation data

Health Wiz

Health Promotion

The Health Promotion Projects website contains a database listing of Australian and New Zealand health promotion projects. The database contains over 6,000 entries covering a wide range of health promotion activities including programs for:
  • Indigenous Australians;
  • Rural and Regional Australia;
  • Youth;
  • Ethnic Communities;
  • Women's Health; and
  • General Practice.

GO TO HEAPS Database

Adolescent Health Promotion Database

This database provides access to articles relating to adolescent health promotion.

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Health & Ageing

The Australian Government Department of Health of Ageing Website provides an extensive list of publications, reports and reviews. Some of these reports are related to data modeling and statistical reviews. Publications of particular relevance to the current topic include:

Related Links:

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Quality in Australian Health Care

This report advances a number of obvious arguments of principle and makes a number of assertions concerning proposed benefits deriving from computerised information systems including:

There are a number of parrallels between the Quality in Australian Health Care Study and the Harvard Medical Practice Study, which also drew attention to medical errors. The Harvard Medical Practice Study was first published in 1991 and was based on 1984 case records. The researchers have subsequently written a number of articles and a book, and popular discussion of "the Harvard study" has come to refer to these collective works 1, 2, 3, 4, 5, 6. Both the Harvard study and the Quality in Australian Health Care study examined medical records to detect evidence of adverse events. By extrapolating from the adverse event analysis both the studies drew extraordinary conclusions concerning the frequency and totality of hosptial treatment derived injuries and fatalities. Richard E. Anderson, a specialist medical oncologist and professor of medicine at the University of California San Diego, offers valuable critiques of the Harvard Medical Practice Study:

There are a number of conflicting points of view concerning Quality in Health Care Studies. Hayward and Hofer have recently released a book titled: "Estimating Hospital Deaths Due to Medical Errors: Preventability Is in the Eye of the Reviewer," the full text of which available, to registered users, on the JAMA Internet Site (

Rand have also produced a number of discussion papers concerning measuring Quality in Health Care:

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Some References

Numbered Refererences for the Harvard Medical Practice Study

  1. Brennan RA, Leape LL, Laird MM, Hebert L, Localio AR, Lawthers AG, et al. Incidence of Adverse Events and Negligence in Hospitalized Patients: Results of the Harvard Medical Practice Study. New England Journal of Medicine. 1991; 324: 370-6.
  2. Localio AR, Lawthers AG, Brennan TA, Laird NM, Hebert LE, Peterson LM, et al. Relation Between Malpractice Claims and Adverse Events Due to Negligence. New England Journal of Medicine. 1991; 325: 245-51.
  3. Leape LL, Brennan TA, Laird N, Lawthers AG, Localio AR, Barnes BA, et al. The Nature of Adverse Events in Hospitalized Patients. New England Journal of Medicine. 1991; 324: 377-84.
  4. Weiler PC, Hiatt HH, Newhouse JP, Johnson WG, Brennan TA, Leape LL. A Measure of Malpractice. Cambridge: Harvard University Press; 1993: 175.
  5. Weiler PC, Newhouse JP, Hiatt HH. Proposal for Medical Liability Reform. Journal of the American Medical Association. 1992; 267: 2355-8.
  6. Weiler PC, Brennan TA, Newhouse JP, Leape LL, Lawthers AG, Hiatt HH, et al. The Economic Consequences of Medical Injuries. Journal of the American Medical Association. 1992; 267: 2487-92.
    (these references cited from the articles attributed to Richard E. Anderson referenced in the text)

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