Literature DB >> 30747859

Patient Safety Incidents Describing Patient Falls in Critical Care in North West England Between 2009 and 2017.

Antony N Thomas1, Joanna E Balmforth2.   

Abstract

AIM: The aim of the study was to review reported falls in critical care units to see whether the causes and results were different from those described in a general hospital population.
METHODS: We reviewed and classified patient safety incidents describing falls from critical care units in the North West of England between 2009 and 2017. The classification reviewed patient and staff factors contributing to the fall, the environment of the fall, and the reported consequences. We then calculated and compared rates of falls in different units.
RESULTS: There were 914 falls reported, representing only 2.0% of all reported incidents. The median (interquartile range) unit rate was 1.0 falls per 1000 (0.5-1.2) days, and falls were unrelated to the number of single rooms and were no more common in specialist units. There were 304 (33%) falls in patients transferring (207 to standing, 8 from standing), and there were 259 (28%) falls from bed. Patient factors included attempting tasks without assistance (323 incidents [35%]) and organic confusion (188 incidents [21%]). Staff factors included being away from the patient (375 incidents [41%]). Harm was described in 201 incidents (22%), including removal of medical devices (40 incidents), injury to staff (10 incidents) subdural hematoma, and possible spinal injury (1 incident each).
CONCLUSIONS: There is a low rate of falls and associated harm in critical care units. The variation between units suggests that this rate could be further reduced by the prevention and management of delirium and by educating patients and staff to take care when moving patients to the standing position.
Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Entities:  

Mesh:

Year:  2021        PMID: 30747859     DOI: 10.1097/PTS.0000000000000574

Source DB:  PubMed          Journal:  J Patient Saf        ISSN: 1549-8417            Impact factor:   2.844


  1 in total

Review 1.  The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea.

Authors:  Kyoung Ja Moon; Chang-Sik Son; Jong-Ha Lee; Mina Park
Journal:  BMC Med Inform Decis Mak       Date:  2022-08-17       Impact factor: 3.298

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.