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提升護理師運用智慧臥床照護系統執行率

Improving the Execution Rate of Nurse Use of Smart Bed Care System

摘要


當病人在離床前,藉由智慧臥床照護系統提早通知護理師前往協助,能有效降低跌倒發生率。本專案目的為提升護理師運用智慧臥床照護系統執行率,以減少病人發生跌倒,經調查2021年1月1日至1月10日護理師運用智慧臥床照護系統執行率僅有45.0%,發現主要原因:缺乏衛教資料、系統異常不會處理、手機設定步驟繁瑣及推床送檢返室後,常遺漏將床墊傳輸線插回床邊警示器。透過製作智慧臥床照護系統衛教說明圖、成立LINE群組即時提供障礙排除、升級智慧臥床照護系統介接住院系統、制訂監測提醒護理師機制,提升護理師運用智慧臥床照護系統執行率至85.6%。照護過程中護理師能確實結合運用資訊技術,方可提升智慧資訊系統之效益。期此專案能提供未來護理師應用智慧臥床照護系統之參考,讓護理師多加運用智慧科技照護病人,增加病人安全。

並列摘要


A smart bed care system can effectively reduce the incidence of falls before patients leave the bed by notifying nurses in advance, who can help. This project aimed to improve nurse use of the smart bed care system. After investigation, from January 1 to January 10, 2021, the rate of nurse implementation of the smart bed care system was 45.0%. The main reasons for this were a lack of health education data, the system not being able to deal with abnormalities, the setup process being inconvenient, and nurses often forgetting to plug the mattress transmission line back into the bedside alarm after removal. Through the provision of instructions for the operation of the smart bed care system, establishment of a messaging (LINE) group to provide real-time troubleshooting, upgrading of the smart bed care system to connect with the inpatient system, and monitoring and reminding of nurses, the rate of nurse implementation of the smart bed care system was increased to 85.6%. The efficiency of smart bed care systems can only be improved through the combined use of information technology and nursing care processes. This project provides future nurses with a reference for the application of smart bed care systems, enabling them to use this technology to provide more effective care to more patients.

並列關鍵字

smart bed care system

參考文獻


林小玲、謝雅宜、溫明寰(2016).使用離床報知機偵測離床降低住院病人跌倒之研 究. 榮 總 護 理,33(2),164-175。
林湘玉、林小玲、溫明寰(2017).提升胃腸科病房離床報知機使用遵從率專案.高雄護理雜誌,34(2),45-51。
Cuttler, S. J., Barr-Walker, J., Cuttler, L. (2017). Reducing medical-surgical inpatient falls and injuries with videos, icons and alarms. BMJ Open Quality, 6(2), Article e000119.
Hubbartt, B., Davis, S. G., & Kautz, D. D. (2011). Fall prediction and prevention systems: Recent trends, challenges, and future research directions. Sensors, Rehabilitation Nursing, 36(5), 196-199.
Mileski, M., Brooks, M., Topinka, J. B., Hamilton, G., Land, C., Mitchell, T., Mosley, B., McClay, R. (2019). Alarming and/or alerting device effectiveness in reducing falls in long-term care (LTC) facilities? A systematic review. Healthcare (Basel), 7(1), 51.

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