Literature DB >> 8608418

Using prediction models and cost-effectiveness analysis to improve clinical decisions: emergency department patients with acute chest pain.

L Goldman1.   

Abstract

Prediction models and cost-effectiveness analysis are two of the methodologies included in the broad definition of outcomes research. These methodologies are designed to improve physicians' abilities to identify clinical risks and to choose appropriate management strategies based on these risks. For the evaluation and management of patients with acute chest pain, prediction models have markedly improved our ability to estimate risk, and cost-effectiveness analyses have helped guide the development of new paradigms and the incorporation of new technologies. In the past decade, the management of patients who come to emergency departments with acute chest pain has fundamentally changed, with far fewer patients being admitted to coronary intensive care units and an increasing majority being admitted to nonintensive, observation units for shorter and shorter periods of time. These changes in management approaches actually allow more patients to be admitted, hence reducing the risk of inappropriate discharge, while still reducing the utilization of resources.

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Year:  1995        PMID: 8608418

Source DB:  PubMed          Journal:  Proc Assoc Am Physicians        ISSN: 1081-650X


  3 in total

1.  Poor performance of the modified early warning score for predicting mortality in critically ill patients presenting to an emergency department.

Authors:  Le Onn Ho; Huihua Li; Nur Shahidah; Zhi Xiong Koh; Papia Sultana; Marcus Eng Hock Ong
Journal:  World J Emerg Med       Date:  2013

Review 2.  Cost reduction strategies for emergency services: insurance role, practice changes and patients accountability.

Authors:  Daniel Simonet
Journal:  Health Care Anal       Date:  2008-02-28

3.  Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score.

Authors:  Marcus Eng Hock Ong; Christina Hui Lee Ng; Ken Goh; Nan Liu; Zhi Xiong Koh; Nur Shahidah; Tong Tong Zhang; Stephanie Fook-Chong; Zhiping Lin
Journal:  Crit Care       Date:  2012-06-21       Impact factor: 9.097

  3 in total

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