Simulation modelling in mental health

A systematic review

Research output: Contribution to journalReview ArticleResearchpeer-review

2 Citations (Scopus)

Abstract

While mental illness is one of the leading causes of international disease burden, within fifteen identified reviews of healthcare simulation only three included papers are in mental health. Here we systematically review the use of simulation modelling in mental healthcare, identifying 160 papers. There were widespread and innovative applications of simulation in the areas of medical decision making and epidemiology, with health system planning and optimisation relatively underrepresented. Markov modelling was the preferred method across area and illness. However, the literature is currently undermined by a lack of coherence and evidence of implementation, and we acknowledge an ongoing issue of accessing unpublished models from healthcare and government organisations. To advance, the field requires a shared knowledge base. We propose that this may be facilitated by the use of existing epidemiological models as the basis of modelling in all other areas.
Original languageEnglish
Pages (from-to)76-85
Number of pages10
JournalJournal of Simulation
Volume12
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • healthcare
  • mental health
  • simulation modelling
  • systematic review

Cite this

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Simulation modelling in mental health : A systematic review. / Long, Katrina M.; Meadows, G. N.

In: Journal of Simulation, Vol. 12, No. 1, 2018, p. 76-85.

Research output: Contribution to journalReview ArticleResearchpeer-review

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T2 - A systematic review

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AB - While mental illness is one of the leading causes of international disease burden, within fifteen identified reviews of healthcare simulation only three included papers are in mental health. Here we systematically review the use of simulation modelling in mental healthcare, identifying 160 papers. There were widespread and innovative applications of simulation in the areas of medical decision making and epidemiology, with health system planning and optimisation relatively underrepresented. Markov modelling was the preferred method across area and illness. However, the literature is currently undermined by a lack of coherence and evidence of implementation, and we acknowledge an ongoing issue of accessing unpublished models from healthcare and government organisations. To advance, the field requires a shared knowledge base. We propose that this may be facilitated by the use of existing epidemiological models as the basis of modelling in all other areas.

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