Evidence-Based Medicine: Is It Relevant to Long-Term Care?

2004 ◽  
Vol 5 (5) ◽  
pp. 328-332 ◽  
Author(s):  
Barbara J. Messinger-Rapport
2020 ◽  
Author(s):  
Kyoung Ja Moon ◽  
Chang-Sik Son ◽  
Jong-Ha Lee ◽  
Mina Park

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none


2018 ◽  
Vol 14 (2) ◽  
pp. 124-126 ◽  
Author(s):  
Janet K. Sluggett ◽  
Ivanka Hendrix ◽  
J. Simon Bell

2006 ◽  
Vol 88 (1) ◽  
pp. 33-36 ◽  
Author(s):  
F Dinah ◽  
A Adhikari

INTRODUCTION Most surgical wounds are closed primarily, but some are allowed to heal by secondary intention. This usually involves repeated packing and dressing of the raw wound surfaces. Although the long-term care of such wounds has devolved to the care of nurses in the community or out-patient setting, the initial wound dressing or cavity packing is done by the surgeon in the operating theatre. Many surgeons are unaware of the growth of the discipline of wound care, and still use traditional soaked gauze for dressing and packing open surgical wounds and cavities. RESULTS This review summarises the some of the modern alternatives available and the evidence – or the lack of it – for their use in both the acute and chronic setting.


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