Carter, EM;
Potts, HW;
(2014)
Predicting length of stay from an electronic patient record system: a primary total knee replacement example.
BMC Med Inform Decis Mak
, 14
(1)
, Article 26. 10.1186/1472-6947-14-26.
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Abstract
To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay.
| Type: | Article |
|---|---|
| Title: | Predicting length of stay from an electronic patient record system: a primary total knee replacement example. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/1472-6947-14-26 |
| Publisher version: | http://dx.doi.org/10.1186/1472-6947-14-26 |
| Language: | English |
| Additional information: | © 2014 Carter and Potts; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| Keywords: | Length of stay; Regression analysis; Models, statistical; negative binomial; Total knee replacement; Computerized Medical Records; Hospital planning; |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > CHIME |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1426131 |
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