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Open Access Research article

Predicting recovery at home after Ambulatory Surgery

Juan Viñoles1, Maía V Ibáñez2 and Guillermo Ayala3*

Author Affiliations

1 Ambulatory Surgery Unit. Hospital Universitario Dr. Peset, Avda Gaspar Aguilar 90, 46017 Valencia, Spain

2 Department of Mathematics. Universitat Jaume I, 12071 Castellón, Spain

3 Department of Statistics and Operational Research. Universidad de Valencia. Avda Vicent Andrés Estellés, 1, 46100 Burjassot, Spain

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BMC Health Services Research 2011, 11:269  doi:10.1186/1472-6963-11-269

Published: 13 October 2011

Abstract

The correct implementation of Ambulatory Surgery must be accompanied by an accurate monitoring of the patient post-discharge state. We fit different statistical models to predict the first hours postoperative status of a discharged patient. We will also be able to predict, for any discharged patient, the probability of needing a closer follow-up, or of having a normal progress at home.

Background

The status of a discharged patient is predicted during the first 48 hours after discharge by using variables routinely used in Ambulatory Surgery. The models fitted will provide the physician with an insight into the post-discharge progress. These models will provide valuable information to assist in educating the patient and their carers about what to expect after discharge as well as to improve their overall level of satisfaction.

Methods

A total of 922 patients from the Ambulatory Surgery Unit of the Dr. Peset University Hospital (Valencia, Spain) were selected for this study. Their post-discharge status was evaluated through a phone questionnaire. We pretend to predict four variables which were self-reported via phone interviews with the discharged patient: sleep, pain, oral tolerance of fluid/food and bleeding status. A fifth variable called phone score will be built as the sum of these four ordinal variables. The number of phone interviews varies between patients, depending on the evolution. The proportional odds model was used. The predictors were age, sex, ASA status, surgical time, discharge time, type of anaesthesia, surgical specialty and ambulatory surgical incapacity (ASI). This last variable reflects, before the operation, the state of incapacity and severity of symptoms in the discharged patient.

Results

Age, ambulatory surgical incapacity and the surgical specialty are significant to explain the level of pain at the first call. For the first two phone calls, ambulatory surgical incapacity is significant as a predictor for all responses except for sleep at the first call.

Conclusions

The variable ambulatory surgical incapacity proved to be a good predictor of the patient's status at home. These predictions could be used to assist in educating patients and their carers about what to expect after discharge, as well as to improve their overall level of satisfaction.