The application of data mining techniques to improve service delivery in health and aged care

Impact: Economic Impact, Public policy Impact, Quality of life Impact, Social Impact

Impact story summary

Data Mining (DM) is the science of analysing data for discovering knowledge, making sense of data and predicting the future. While there are many machine learning algorithms for future prediction there is a shortage of algorithms for knowledge discovery in community health. To address this need, Charles Sturt University researchers developed a data mining algorithm called SysFor, which was used to improve service quality in regional health and aged care in Australia.

SysFor allowed regional health and aged care organisations to:

1. Improve service quality at Hobart District Nursing Service (HDNS) and reduce the functional decline (e.g. loss of mobility) of older people

2. Develop a patient risk stratification tool for the Murrumbidgee Local Health District (MLHD) that reduced the number of avoidable re-admissions

3. In association with LiveBetter Pty Ltd, SysFor demonstrated the positive impact of Linkers (i.e staff that link aged people to community and services) in improving their overall wellbeing and quality of life
Impact date2011
Category of impactEconomic Impact, Public policy Impact, Quality of life Impact, Social Impact
Impact levelBenefit

Keywords

  • Data mining
  • Knowledge discovery
  • Artificial intelligence
  • Machine learning
  • Healthcare
  • Health informatics
  • Aged care

Countries where impact occurred

  • Australia