Awakenings? Patient and Hospital Staff Perceptions of Nighttime Disruptions and Their Effect on Patient Sleep

Grossman, Mila N.; Anderson, Samantha; Worku, Aelaf; Marsack, William; Desai, Nimit; Tuvilleja, Ambrosio; Ramos, Jacqueline; Francisco, Mary Ann; LaFond, Cynthia M.; Balachandran, Jay S.; Mokhlesi, Babak; Farnan, Jeanne M.; Meltzer, David O.; Arora, Vineet M.

doi:10.5664/jcsm.6468

Cited by 52 publications

(50 citation statements)

References 19 publications

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“…To develop SIESTA, patients were surveyed, and focus groups of staff were conducted; overnight vitals, medications, and phlebotomy were identified as major barriers to patient sleep. 9 We found that physicians did not know how to change the default vital signs order "every 4 hours" or how to batch-order morning phlebotomy at a time other than 4:00 am. Nurses reported having to wake patients up at 1:00 am for q8h subcutaneous heparin.…”

Section: Development Of Siestamentioning

confidence: 85%

Effectiveness of SIESTA on Objective and Subjective Metrics of Nighttime Hospital Sleep Disruptors

Arora

Machado²,

Anderson

et al. 2019

Journal of Hospital Medicine

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A lthough sleep is critical to patient recovery in the hospital, hospitalization is not restful, 1,2 and inpatient sleep deprivation has been linked to poor health outcomes. 1-4 The American Academy of Nursing's Choosing Wisely ® campaign recommends nurses reduce unnecessary nocturnal care. 5 However, interventions to improve inpatient sleep are not widely implemented. 6 Targeting routine disruptions, such as overnight vital signs, by changing default settings in the electronic health record (EHR) with "nudges" could be a cost-effective strategy to improve inpatient sleep. 4,7 We created Sleep for Inpatients: Empowering Staff to Act (SI-ESTA), which pairs nudges in the EHR with interprofessional education and empowerment, 8 and tested its effectiveness on objectively and subjectively measured nocturnal sleep disruptors. METHODS Study Design Two 18-room University of Chicago Medicine general-medicine units were used in this prospective study. The SIESTA-enhanced unit underwent the full sleep intervention: nursing education and empowerment, physician education, and EHR changes. The standard unit did not receive nursing interventions but received all other forms of intervention. Because physicians simultaneously cared for patients on both units, all internal medicine residents and hospitalists received the same education. The study population included physicians, nurses, and awake English-speaking patients who were cognitively intact and admitted to these two units. The University of Chicago Institutional Review Board approved this study (12-1766; 16685B).

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Section: Development Of Siestamentioning

confidence: 85%

Effectiveness of SIESTA on Objective and Subjective Metrics of Nighttime Hospital Sleep Disruptors

Arora

Machado²,

Anderson

et al. 2019

Journal of Hospital Medicine

Self Cite

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“…Patients, clinical staff and hospital administrators share a growing awareness that sleep is severely disrupted among hospitalised patients, and this may represent an important target to improve recovery 31–33. Nonetheless, there remains a large gap between this awareness and the implementation of nocturnal sleep promotion protocols 31.…”

Section: Introductionmentioning

confidence: 99%

Pilot study: an intensive care unit sleep promotion protocol

et al. 2019

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PurposeDisturbances, such as in-room activity and sound, are significant sources of sleep disruption among critically ill patients. These factors are potentially modifiable. We tested the impact of an intensive care unit (ICU) sleep promotion protocol on overnight in-room disturbance.MethodsOur protocol restricted non-urgent bedside care from 00:00 to 03:59. Patients were assigned to usual care (n=30) or the sleep protocol (n=26). The primary outcomes were measures of in-room activity, sound and light. These three types of disturbance were compared between arms during a baseline time block (20:00–23:59) and a rest time block (00:00–03:59). We assessed the sleep protocol effect with generalised linear models.ResultsUsual care and sleep protocol patients had equivalent levels of in-room activity, sound and light during the baseline time block (20:00–23:59). In contrast, during the rest time block (00:00–03:59), the sleep protocol arm had 32% fewer room entries (rate ratio (RR) 0.68, p=0.001) and 9.1 fewer minutes of in-room activity (p=0.0002). Also, the length of time between room entrances increased from 26.4 to 45.8 min (p=0.0004). The sleep protocol arm also had lower sound during the rest time block. Mean A-weighted sound was 2.5 decibels lower (p=0.02), and there were 36% fewer peaks (RR 0.64, p=0.02). Light levels were highly variable and not changed by the sleep protocol.ConclusionsSleep promotion protocols can improve in-room activity and sound. This provides a better sleep opportunity and may, therefore, improve ICU sleep.Trial registration number1112009428

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“…VS assessments are the second most common cause of sleep disruption in the hospitalized patient 16 , and safely reducing these interventions overnight can improve patient sleep quality and quantity while leading to better health outcomes. Multiple studies have measured the quality and quantity of sleep in hospitalized patients around the world, showing significant reductions in sleep quality for patients in North America 1,2,7 , Europe 17 , and Australia 18 , among others.…”

Section: Discussionmentioning

confidence: 99%

Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

et al. 2020

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Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

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Awakenings? Patient and Hospital Staff Perceptions of Nighttime Disruptions and Their Effect on Patient Sleep

Abstract: Hospital staff and patients agreed that pain, vital signs and tests were top sleep disrupters. However, pain was associated with the greatest objective sleep loss, highlighting the need for proactive screening and management of patient pain to improve sleep in hospitals.

Cited by 52 publications

References 19 publications

Effectiveness of SIESTA on Objective and Subjective Metrics of Nighttime Hospital Sleep Disruptors

Effectiveness of SIESTA on Objective and Subjective Metrics of Nighttime Hospital Sleep Disruptors

Pilot study: an intensive care unit sleep promotion protocol

Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

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