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Guided by a continuous quality improvement approach, the program combined standardized sleep assessment, environmental noise and light control (e.g., noise sensors and light-reducing films), staff “Quiet Hour” behavior changes, and patient/family education. After implementation, situational insomnia incidence fell from 66.4% to 35.5%, midday and nighttime noise levels decreased by 21.5% and 28.0%, and satisfaction with noise and temperature management increased by 38.7% and 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nature.com/scientificreports)  \nOPEN  \nMultimodal sleep management reduces perioperative insomnia in general surgery patients through optimal healing environments  \nHongxia Xu1, Xiaoxiao Jiang1, Hongying Pan1􀀍, Yihong Xu1, Jun Chen1, Lili Cheng1, Yijie Qian1 & Xiao Liang2  \nThis quality improvement study analyzed factors influencing situational insomnia among perioperative patients in general surgical wards using a combination of literature review, questionnaire surveys, and group discussions. Guided by the Optimal Healing Environment (OHE) model, which emphasizes creating a healing atmosphere through internal, external, and interpersonal factors, we developed a multi-modal sleep management program. Key components included standardizing sleep assessment protocols, optimizing the environment through noise reduction strategies (e.g., noise sensors) and light control (e.g., light-reducing films), implementing staff behavior modifications such as “Quiet Hour” protocols, and providing patient and family education. Post-implementation, the incidence of situational insomnia decreased significantly from 66.4% to 35.5%, noise levels during midday and nighttime were reduced by 21.5% and 28.0%, respectively, and patient satisfaction with noise and temperature management improved by 38.7% and 20.3%, respectively. These results highlight the effectiveness of the OHE-based program in improving sleep quality, accelerating recovery, and enhancing patient satisfaction in general surgery settings.  \nKeywords Healing environment, Sleep disorders, Enhanced recovery after surgery, General surgery  \nSleep disturbances represent a pervasive yet underaddressed challenge in perioperative care, particularly among general surgery patients. Poor sleep quality during hospitalization exacerbates postoperative pain, prolongs recovery, and increases the risk of complications, undermining the goals of Enhanced Recovery After Surgery (ERAS) protocols1. Epidemiological data indicate that 70–80% of surgical patients experience significant sleep deprivation, with situational insomnia—triggered by acute environmental or psychological stressors—being the most prevalent form2–6. Despite its clinical relevance, systematic interventions to mitigate perioperative sleep disruptions remain limited.  \nEmerging evidence underscores multifactorial contributors to sleep disturbances in surgical wards, including environmental noise, inappropriate lighting, temperature fluctuations, and patient-specific factors such as pain and anxiety2,3,7–9. For instance, Mouch et al. (2020) reported that > 60% of elective surgery patients endure fragmented sleep patterns due to these factors7. Concurrently, studies by Lin et al. (2021) and Sipilä & Kalso (2021) highlight the bidirectional relationship between sleep optimization and improved recovery outcomes, including reduced pain and shorter hospital stays2,3. Environmental modifications, such as noise reduction strategies8 and patient education programs9, have shown promise but lack integration into a cohesive framework.  \nThe Optimal Healing Environment (OHE) model addresses this gap by synthesizing internal (psychological), external (physical/environmental), and interpersonal (caregiver-patient interaction) dimensions to foster holistic recovery10–12. While the OHE framework has been theorized, its empirical application to perioperative sleep management remains underexplored. This study employed continuous quality improvement methodologies to develop a multimodal sleep management program grounded in the OHE model. The primary aim was to reduce situational insomnia and enhance recovery metrics in general surgery settings.  \n1Nursing Department, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, No. 3 East Qingchun Road, Hangzhou, China. 2Department of General Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China. 􀀍 email: [panhy@srrsh.com](p","cbCaihIGVl2o5qY9","https://ap.wps.com/l/cbCaihIGVl2o5qY9","pdf",1432550,"English","# Methodology\n## Problem investigation and cause analysis\n## Document analysis","[{\"question\":\"What was the goal of this quality improvement study?\",\"answer\":\"To reduce situational insomnia and improve recovery metrics for perioperative general surgery patients using an OHE-based multimodal program.\"},{\"question\":\"What interventions were included in the multimodal sleep management program?\",\"answer\":\"Standardized sleep assessment, environmental noise and light optimization (noise sensors and light-reducing films), staff behavior changes such as “Quiet Hour” protocols, and education for patients and families.\"},{\"question\":\"How did the program affect situational insomnia and noise levels after implementation?\",\"answer\":\"Situational insomnia decreased from 66.4% to 35.5%, and noise levels during midday and nighttime were reduced by 21.5% and 28.0% respectively.\"}]","Multimodal sleep management reduces perioperative insomnia in general surgery patients through optimal healing environments | PDF",1790562380,25]