Patients' Perceptions of Virtual Live Music in the Intensive Care Unit.

BACKGROUND: Implementing music in the intensive care unit has increased in popularity because the environment can be stressful and anxiety inducing for many patients. In hospital settings, therapeutic music can be beneficial for patients' well-being and recovery. Although live music typically involves a face-to-face encounter between the musician and patient, the COVID-19 pandemic has prompted a change to virtual live therapeutic music, using technology to present music in real time (eg, with a tablet computer). OBJECTIVE: To generate novel findings regarding patients' perceptions of virtual live therapeutic music, which has been little studied compared with live or recorded music.. METHODS: Fifty patients in Vanderbilt University Medical Center intensive care units listened to virtual live music played by a volunteer musician via an online video communication platform. Patients' responses to 5 survey questions were transcribed and analyzed qualitatively and quantitatively using data analysis software. RESULTS: Seven major themes describing the familiarity and significance of music for patients were identified. Forty-seven patients (94%) experienced positive emotions from the music, 46 (92%) indicated that music was a significant part of their lives, 28 (56%) accessed a cherished memory, and 45 (90%) indicated that they would not change anything. CONCLUSIONS: Therapeutic virtual music was well received and provided tangible benefits to patients. Additional research would provide information on patients' outcomes and differences between live and virtual live music.

A Spatiotemporal and Multisensory Approach to Designing Wearable Clinical ICU Alarms.

In health care, auditory alarms are an important aspect of an informatics system that monitors patients and alerts clinicians attending to multiple concurrent tasks. However, the volume, design, and pervasiveness of existing Intensive Care Unit (ICU) alarms can make it difficult to quickly distinguish their meaning and importance. In this study, we evaluated the effectiveness of two design approaches not yet explored in a smartwatch-based alarm system designed for ICU use: (1) using audiovisual spatial colocalization and (2) adding haptic (i.e., touch) information. We compared the performance of 30 study participants using ICU smartwatch alarms containing auditory icons in two implementations of the audio modality: colocalized with the visual cue on the smartwatch's low-quality speaker versus delivered from a higher quality speaker located two feet away from participants (like a stationary alarm bay situated near patients in the ICU). Additionally, we compared participant performance using alarms with two sensory modalities (visual and audio) against alarms with three sensory modalities (adding haptic cues). Participants were 10.1% (0.24s) faster at responding to alarms when auditory information was delivered from the smartwatch instead of the higher quality external speaker. Meanwhile, adding haptic information to alarms improved response times to alarms by 12.2% (0.23s) and response times on their primary task by 10.3% (0.08s). Participants rated learnability and ease of use higher for alarms with haptic information. These small but statistically significant improvements demonstrate that audiovisual colocalization and multisensory alarm design can improve user response times.

Auditory roughness: a delicate balance.

Auditory roughness in medical alarm sounds is an important design attribute, and has been shown to impact user performance and perception. While roughness can assist in decreased signal-to-noise ratios (perceived loudness) and communicate urgency, it might also impact patient recovery. Therefore, considerations of neuroscience correlates, music theory, and patient impact are critical aspects to investigate in order to optimise alarm design.

Ten Years Later, Alarm Fatigue Is Still a Safety Concern.

Ten years after the publication of a landmark article in AACN Advanced Critical Care, alarm fatigue continues to be an issue that researchers, clinicians, and organizations aim to remediate. Alarm fatigue contributes to missed alarms and medical errors that result in patient death, increased clinical workload and burnout, and interference with patient recovery. Led by the American Association of Critical-Care Nurses, national patient safety organizations continue to prioritize efforts to battle alarm fatigue and have proposed alarm management strategies to mitigate the effects of alarm fatigue. Similarly, clinical efforts now use simulation studies, individualized alarm thresholds, and interdisciplinary teams to optimize alarm use. Finally, engineering research efforts have innovated the standard alarm to convey information more effectively for medical users. By focusing on patient and provider safety, clinical workflow, and alarm technology, efforts to reduce alarm fatigue over the past 10 years have been grounded in an evidence-based and personnel-focused approach.

Multisensory alarm to benefit alarm identification and decrease workload: a feasibility study.

The poor design of conventional auditory medical alarms has contributed to alarm desensitization, and eventually, alarm fatigue in medical personnel. This study tested a novel multisensory alarm system which aims to help medical personnel better interpret and respond to alarm annunciation during periods of high cognitive load such as those found within intensive care units. We tested a multisensory alarm that combined auditory and vibrotactile cues to convey alarm type, alarm priority, and patient identity. Testing was done in three phases: control (conventional auditory), Half (limited multisensory alarm), and Full (complete multisensory alarm). Participants (N = 19, undergraduates) identified alarm type, priority, and patient identity (patient 1 or 2) using conventional and multisensory alarms, while simultaneously completing a cognitively demanding task. Performance was based on reaction time (RT) and identification accuracy of alarm type and priority. Participants also reported their perceived workload. RT was significantly faster for the Control phase (p < 0.05). Participant performance in identifying alarm type, priority, and patient did not differ significantly between the three phase conditions (p = 0.87, 0.37, and 0.14 respectively). The Half multisensory phase produced the lowest mental demand, temporal demand, and overall perceived workload score. These data suggest that implementation of a multisensory alarm with alarm and patient information may decrease perceived workload without significant changes in alarm identification performance. Additionally, a ceiling effect may exist for multisensory stimuli, with only part of an alarm benefitting from multisensory integration.

Improving detectability of auditory interfaces for medical alarms through temporal variation in amplitude envelope.

BACKGROUND: Auditory interfaces play a vital role in many applications, informing users about both urgent and routine information critical to safety. Unfortunately, problems related to high alarm rates, low reliability, and sound annoyance create barriers to optimising the quality of patient care in perioperative medicine and critical care. Here, we explore how to reduce annoyance and improve detection by manipulating a sound's temporal envelope or the way its energy changes over time. METHODS: In the first experiment, participants were asked to detect a series of percussive and flat tones presented at six signal-to-noise ratios while performing a concurrent speech comprehension task. In the second experiment, different participants were asked to evaluate the relative annoyance of these same sounds. RESULTS: Relative to industry-standard flat tones, percussive tones were significantly less annoying and more detectable. Although more detectable, percussive tones did not impair concurrent speech comprehension. CONCLUSIONS: Temporal variation in amplitude envelope represents a promising path towards improving auditory interfaces for patient monitoring. By using temporally variable sounds, auditory interfaces can be more effective in alerting users. This is important for safety-critical areas, such as medical alarms, where annoyance often limits efficacy. As this manipulation can preserve the pitch and rhythm of tone sequences, it is compatible with users' pre-existing knowledge of current alarms.

Improved Patient Monitoring with a Novel Multisensory Smartwatch Application.

The design of medical alarms has been heavily criticized in the past decade. Auditory medical alarms have poor learnability, discernibility, and relevance, leading to poor patient outcomes, and alarm fatigue, and overall poor informatic system design. We developed a novel trimodal patient monitoring smartwatch application for patient monitoring. Participants completed two phases: (1) control and (2) our novel trimodal system while identifying alarms (heart rate, oxygenation, and blood pressure) and completing a cognitively demanding task. Alarms were auditory icons presented as either solo or co-alarms. Participant performance was assessed by accuracy and response time (RT) of alarm identification. Using the novel system, accuracy was significantly improved overall (p < 0.01) and in co-alarm situations (p < 0.01), but not for solo alarms (p = 0.484). RT was also significantly faster (p < 0.01) while using the novel system for all alarm types. Participants reported decreased mental workload using the novel system. This feasibility study shows that our novel alarm system performs better than current standards. Improvements in accuracy, RT and perceived mental workload indicate the potential of this system to have a positive impact on medical informatic systems and clinical monitoring, for both the patient and the clinician.

New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record.

OBJECTIVE: To develop and test an accurate deep learning model for predicting new onset delirium in hospitalized adult patients. METHODS: Using electronic health record (EHR) data extracted from a large academic medical center, we developed a model combining long short-term memory (LSTM) and machine learning to predict new onset delirium and compared its performance with machine-learning-only models (logistic regression, random forest, support vector machine, neural network, and LightGBM). The labels of models were confusion assessment method (CAM) assessments. We evaluated models on a hold-out dataset. We calculated Shapley additive explanations (SHAP) measures to gauge the feature impact on the model. RESULTS: A total of 331 489 CAM assessments with 896 features from 34 035 patients were included. The LightGBM model achieved the best performance (AUC 0.927 [0.924, 0.929] and F1 0.626 [0.618, 0.634]) among the machine learning models. When combined with the LSTM model, the final model's performance improved significantly (P = .001) with AUC 0.952 [0.950, 0.955] and F1 0.759 [0.755, 0.765]. The precision value of the combined model improved from 0.497 to 0.751 with a fixed recall of 0.8. Using the mean absolute SHAP values, we identified the top 20 features, including age, heart rate, Richmond Agitation-Sedation Scale score, Morse fall risk score, pulse, respiratory rate, and level of care. CONCLUSION: Leveraging LSTM to capture temporal trends and combining it with the LightGBM model can significantly improve the prediction of new onset delirium, providing an algorithmic basis for the subsequent development of clinical decision support tools for proactive delirium interventions.

The Influence of Audible Alarm Loudness and Type on Clinical Multitasking.

In high-consequence industries such as health care, auditory alarms are an important aspect of an informatics system that monitors patients and alerts providers attending to multiple concurrent tasks. Alarms levels are unnecessarily high and alarm signals are uninformative. In a laboratory-based task setting, we studied 25 anesthesiology residents' responses to auditory alarms in a multitasking paradigm comprised of three tasks: patient monitoring, speech perception/intelligibility, and visual vigilance. These tasks were in the presence of background noise plus/minus music, which served as an attention-diverting stimulus. Alarms signified clinical decompensation and were either conventional alarms or a novel informative auditory icon alarm. Both alarms were presented at four different levels. Task performance (accuracy and response times) were analyzed using logistic and linear mixed-effects regression. Salient findings were 1), the icon alarm had similar performance to the conventional alarm at a +2 dB signal-to-noise-ratio (SNR) (accuracy: OR 1.21 (95% CI 0.88, 1.67), response time: 0.04 s at 2 dB (95% CI: -0.16, 0.24), which is a much lower level than current clinical environments; 2) the icon alarm was associated with 27% greater odds (95% CI: 18%, 37%) of correctly addressing the vigilance task, regardless of alarm SNR, suggesting crossmodal/multisensory multitasking benefits; and 3) compared to the conventional alarm, the icon alarm was associated with an absolute improvement in speech perception of 4% in the presence of an attention-diverting auditory stimulus (p = 0.031). These findings suggest that auditory icons can provide multitasking benefits in cognitively demanding clinical environments.

Delirium Variability is Influenced by the Sound Environment (DEVISE Study): How Changes in the Intensive Care Unit soundscape affect delirium incidence.

Quantitative data on the sensory environment of intensive care unit (ICU) patients and its potential link to increased risk of delirium is limited. We examined whether higher average sound and light levels in ICU environments are associated with delirium incidence. Over 111 million sound and light measurements from 143 patient stays in the surgical and trauma ICUs were collected using Quietyme® (Neshkoro, Wisconsin) sensors from May to July 2018 and analyzed. Sensory data were grouped into time of day, then normalized against their ICU environments, with Confusion Assessment Method (CAM-ICU) scores measured each shift. We then performed logistic regression analysis, adjusting for possible confounding variables. Lower morning sound averages (8 am-12 pm) (OR = 0.835, 95% OR CI = [0.746, 0.934], p = 0.002) and higher daytime sound averages (12 pm-6 pm) (OR = 1.157, 95% OR CI = [1.036, 1.292], p = 0.011) were associated with an increased odds of delirium incidence, while nighttime sound averages (10 pm-8 am) (OR = 0.990, 95% OR CI = [0.804, 1.221], p = 0.928) and the ICU light environment did not show statistical significance. Our results suggest an association between the ICU soundscape and the odds of developing delirium. This creates a future paradigm for studies of the ICU soundscape and lightscape.

Decaying amplitude envelopes reduce alarm annoyance: Exploring new approaches to improving auditory interfaces.

Auditory alarms offer great potential for facilitating human-computer interactions in complex, rapidly changing environments. They are particularly useful in medical settings, where in theory they should afford communication in emergency rooms, operating theatres, and hospitals around the world. Unfortunately, the sounds typically used in these devices are problematic, and researchers have documented numerous shortcomings. Their ubiquity means that even incremental improvements can have significant benefits for patient care. However, solutions have proven challenging for multiple reasons-including issues of backward compatibility inherent in changing any standard. Here we present a series of three experiments showing that manipulations to one specific, understudied property can significantly lower alarm annoyance without harming learning or memory-while preserving an alarm's melodic and rhythmic structure. These results suggest promising new directions for improving the hospital's soundscape, where evidence of problems related to sound are increasingly recognized as affecting medical outcomes as well as physician well-being.

Evaluation of Noninvasive Respiratory Volume Monitoring in the PACU of a Low Resource Kenyan Hospital.

This research aims to evaluate the use of the noninvasive respiratory volume monitor (RVM) compared to the standard of care (SOC) in the Post-Anesthesia Care Unit (PACU) of Kijabe Hospital, Kenya. The RVM provides real-time measurements for quantitative monitoring of non-intubated patients. Our evaluation was focused on the incidence of postoperative opioid-induced respiratory depression (OIRD). The RVM cohort (N = 50) received quantitative OIRD assessment via the RVM, which included respiratory rate, minute ventilation, and tidal volume. The SOC cohort (N = 46) received qualitative OIRD assessment via patient monitoring with oxygenation measurements (SpO2) and physical examination. All diagnosed cases of OIRD were in the RVM cohort (9/50). In the RVM cohort, participants stayed longer in the PACU and required more frequent airway maneuvers and supplemental oxygen, compared to SOC (all p < 0.05). The SOC cohort may have had fewer diagnoses of OIRD due to the challenging task of distinguishing hypoventilation versus OIRD in the absence of quantitative data. To account for the higher OIRD risk with general anesthesia (GA), a subgroup analysis was performed for only participants who underwent GA, which showed similar results. The use of RVM for respiratory monitoring of OIRD may allow for more proactive care.

New-Onset Refractory Status Epilepticus with Underlying Autoimmune Etiology: a Case Report.

Management of new-onset refractory status epilepticus and the approach to burst suppression variable is often challenging. We present the unusual case of a previously healthy 18-year-old male with new-onset status epilepticus admitted to the neurologic intensive care unit for 70 days. Despite treatment with multiple anti-epileptic drugs in addition to IV anesthetics, burst suppression was initially unsustainable and the patient remained in super-refractory status epilepticus. Extensive evaluation revealed an underlying autoimmune-mediated etiology with positivity for glutamic acid decarboxylase-65 antibody. Clinical response with a goal of 1-2 bursts per screen on EEG monitor was eventually achieved after a course of rituximab and plasma exchange therapy as well as a 7-day barbiturate coma with a regimen of clobazam, lacosamide, Keppra, and oxcarbazepine followed by a slow taper of phenobarbital and the addition of fosphenytoin. Remarkably, the patient was subsequently discharged to a rehabilitation facility with complete neurologic recovery. We discuss treatment strategies for new-onset refractory status epilepticus and highlight the role of rapid initiation of burst suppression with high-dose IV anesthetics to ensure neuroprotection while the underlying etiology is addressed with immune-modulating therapy.