Literature DB >> 21367330

Predictive risk modelling in health: options for New Zealand and Australia.

Laura E Panattoni1, Rhema Vaithianathan, Toni Ashton, Geraint H Lewis.   

Abstract

Predictive risk models (PRMs) are case-finding tools that enable health care systems to identify patients at risk of expensive and potentially avoidable events such as emergency hospitalisation. Examples include the PARR (Patients-at-Risk-of-Rehospitalisation) tool and Combined Predictive Model used by the National Health Service in England. When such models are coupled with an appropriate preventive intervention designed to avert the adverse event, they represent a useful strategy for improving the cost-effectiveness of preventive health care. This article reviews the current knowledge about PRMs and explores some of the issues surrounding the potential introduction of a PRM to a public health system. We make a particular case for New Zealand, but also consider issues that are relevant to Australia.

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Year:  2011        PMID: 21367330     DOI: 10.1071/AH09845

Source DB:  PubMed          Journal:  Aust Health Rev        ISSN: 0156-5788            Impact factor:   1.990


  6 in total

1.  Systematic review of predictive risk models for adverse drug events in hospitalized patients.

Authors:  Nazanin Falconer; Michael Barras; Neil Cottrell
Journal:  Br J Clin Pharmacol       Date:  2018-02-22       Impact factor: 4.335

2.  Predictive risk modelling in the Spanish population: a cross-sectional study.

Authors:  Juan F Orueta; Roberto Nuño-Solinis; Maider Mateos; Itziar Vergara; Gonzalo Grandes; Santiago Esnaola
Journal:  BMC Health Serv Res       Date:  2013-07-09       Impact factor: 2.655

3.  Global Health and Visa Policy Reform to Address Dangers of Hajj during Summer Seasons.

Authors:  Mohanad Aleeban; Tim K Mackey
Journal:  Front Public Health       Date:  2016-12-22

Review 4.  Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study.

Authors:  Joana Mora; Miren David Iturralde; Lucía Prieto; Cristina Domingo; Marie-Pierre Gagnon; Catalina Martínez-Carazo; Anna Giné March; Daniele De Massari; Tino Martí; Marco Nalin; Francesca Avolio; Jean Bousquet; Esteban de Manuel Keenoy
Journal:  BMC Health Serv Res       Date:  2017-05-05       Impact factor: 2.655

5.  Using family network data in child protection services.

Authors:  Alex James; Jeanette McLeod; Shaun Hendy; Kip Marks; Delia Rusu; Syen Nik; Michael J Plank
Journal:  PLoS One       Date:  2019-10-29       Impact factor: 3.240

6.  Association of plasma trace element levels with neovascular age-related macular degeneration.

Authors:  Thomas J Heesterbeek; Mansour Rouhi-Parkouhi; Stephanie J Church; Yara T Lechanteur; Laura Lorés-Motta; Nikolaos Kouvatsos; Simon J Clark; Paul N Bishop; Carel B Hoyng; Anneke I den Hollander; Richard D Unwin; Anthony J Day
Journal:  Exp Eye Res       Date:  2020-10-21       Impact factor: 3.467

  6 in total

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