The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Predicting the impact of new health technologies on average length of stay: development of a prediction framework

Predicting the impact of new health technologies on average length of stay: development of a prediction framework
Predicting the impact of new health technologies on average length of stay: development of a prediction framework
Objectives: The aim of this study was to develop a framework to predict the impact of new health technologies on average length of hospital stay.
Methods: A literature search of EMBASE, MEDLINE, Web of Science, and the Health Management Information Consortium databases was conducted to identify papers that discuss the impact of new technology on length of stay or report the impact with a proposed mechanism of impact of specific technologies on length of stay. The mechanisms of impact were categorized into those relating to patients, the technology, or the organization of health care and clinical practice.
Results: New health technologies have a variable impact on length of stay. Technologies that lead to an increase in the proportion of sicker patients or increase the average age of patients remaining in the hospital lead to an increase in individual and average length of stay. Technologies that do not affect or improve the inpatient case mix, or reduce adverse effects and complications, or speed up the diagnostic or treatment process should lead to a reduction in individual length of stay and, if applied to all patients with the condition, will reduce average length of stay.
Conclusions: The prediction framework we have developed will ensure that the characteristics of a new technology that may influence length of stay can be consistently taken into consideration by assessment agencies. It is recognized that the influence of technology on length of stay will change as a technology diffuses and that length of stay is highly sensitive to changes in admission policies and organization of care.
forecasting, length of stay, health-care technology
0266-4623
487-491
Simpson, Sue
017968b4-3b6a-4811-b834-a4d6389278e8
Packer, Claire
bf327e86-b7c5-43e4-a7da-f260c3a6ec1f
Stevens, Andrew
5bc9b6cc-97cf-44b7-9d23-cb41f1600dc0
Raftery, James
de9dd294-3a0e-4756-8427-a4f2382495db
Simpson, Sue
017968b4-3b6a-4811-b834-a4d6389278e8
Packer, Claire
bf327e86-b7c5-43e4-a7da-f260c3a6ec1f
Stevens, Andrew
5bc9b6cc-97cf-44b7-9d23-cb41f1600dc0
Raftery, James
de9dd294-3a0e-4756-8427-a4f2382495db

Simpson, Sue, Packer, Claire, Stevens, Andrew and Raftery, James (2005) Predicting the impact of new health technologies on average length of stay: development of a prediction framework. International Journal of Technology Assessment in Health Care, 21 (4), 487-491. (doi:10.1017/S0266462305050671).

Record type: Article

Abstract

Objectives: The aim of this study was to develop a framework to predict the impact of new health technologies on average length of hospital stay.
Methods: A literature search of EMBASE, MEDLINE, Web of Science, and the Health Management Information Consortium databases was conducted to identify papers that discuss the impact of new technology on length of stay or report the impact with a proposed mechanism of impact of specific technologies on length of stay. The mechanisms of impact were categorized into those relating to patients, the technology, or the organization of health care and clinical practice.
Results: New health technologies have a variable impact on length of stay. Technologies that lead to an increase in the proportion of sicker patients or increase the average age of patients remaining in the hospital lead to an increase in individual and average length of stay. Technologies that do not affect or improve the inpatient case mix, or reduce adverse effects and complications, or speed up the diagnostic or treatment process should lead to a reduction in individual length of stay and, if applied to all patients with the condition, will reduce average length of stay.
Conclusions: The prediction framework we have developed will ensure that the characteristics of a new technology that may influence length of stay can be consistently taken into consideration by assessment agencies. It is recognized that the influence of technology on length of stay will change as a technology diffuses and that length of stay is highly sensitive to changes in admission policies and organization of care.

This record has no associated files available for download.

More information

Published date: 2005
Keywords: forecasting, length of stay, health-care technology

Identifiers

Local EPrints ID: 24504
URI: http://eprints.soton.ac.uk/id/eprint/24504
ISSN: 0266-4623
PURE UUID: 6e194124-4cd5-4f2a-918e-dd0ea5257a91

Catalogue record

Date deposited: 31 Mar 2006
Last modified: 08 Jan 2022 01:02

Export record

Altmetrics

Contributors

Author: Sue Simpson
Author: Claire Packer
Author: Andrew Stevens
Author: James Raftery

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×