Noise in the ICU

Sleep during and following critical illness: A narrative review

Sleep is a complex process influenced by biological and environmental factors. Disturbances of sleep quantity and quality occur frequently in the critically ill and remain prevalent in survivors for at least 12 mo. Sleep disturbances are associated with adverse outcomes across multiple organ systems but are most strongly linked to delirium and cognitive impairment. This review will outline the predisposing and precipitating factors for sleep disturbance, categorised into patient, environmental and treatment-related factors. The objective and subjective methodologies used to quantify sleep during critical illness will be reviewed. While polysomnography remains the gold-standard, its use in the critical care setting still presents many barriers. Other methodologies are needed to better understand the pathophysiology, epidemiology and treatment of sleep disturbance in this population. Subjective outcome measures, including the Richards-Campbell Sleep Questionnaire, are still required for trials involving a greater number of patients and provide valuable insight into patients’ experiences of disturbed sleep. Finally, sleep optimisation strategies are reviewed, including intervention bundles, ambient noise and light reduction, quiet time, and the use of ear plugs and eye masks. While drugs to improve sleep are frequently prescribed to patients in the ICU, evidence supporting their effectiveness is lacking.

Remote Monitoring of Sustained Low-Efficiency Dialysis (SLED) Machines in Intensive Care Unit

The Henry Ford Health System provides patients with a safe, improved system of continuous kidney replacement therapy using a proprietary, 24-hour sustained low-efficiency dialysis (SLED). The SLED system utilizes regional citrate anticoagulation (RCA) in conventional hemodialysis machines that have been configured to provide slow dialytic therapy. Within our hospital complex, SLED-RCA systems are deployed in intensive care units distributed over 4 floors in 2 buildings. This widespread footprint represents a spatial challenge for hemodialysis technicians. Fifteen SLED-RCA machines may be running at one time, and each deployed unit may signal an alarm for multiple reasons. Previously, audible alarms prompted intensive care unit nurses to identify the alarming machine and manually notify technicians by telephone. Technicians would then travel to resolve the alarm. To improve the process of addressing SLED-RCA machine alarms, we developed a remote alert alarm system that wirelessly notifies hemodialysis technicians of specific machine alarms. A quality improvement analysis of nearly 1,000 SLED-RCA alarms over a 1-week period revealed that the average time for alarm correction with a remote alert alarm system was approximately 5 minutes. Reducing alarm resolution time may free technicians and nurses for other critical duties.

Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

BACKGROUND

Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos.

METHODS

We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices.

RESULTS

A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical.

CONCLUSIONS

The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

Impact of Sleep Deprivation in the Neurological Intensive Care Unit: A Narrative Review

Sleep is fundamental for everyday functioning, yet it is often negatively impacted in critically ill patients by the intensive care setting. With a focus on the neurological intensive care unit (NeuroICU), this narrative review summarizes methods of measuring sleep and addresses common causes of sleep disturbance in the hospital including environmental, pharmacological, and patient-related factors. The effects of sleep deprivation on the cardiovascular, pulmonary, immune, endocrine, and neuropsychological systems are discussed, with a focus on short-term deprivation in critically ill populations. Where evidence is lacking in the literature, long-term sleep deprivation studies and the effects of sleep deprivation in healthy individuals are also referenced. Lastly, strategies for the promotion of sleep in the NeuroICU are presented.

Reducing medical device alarms by an order of magnitude: A human factors approach

The intensive care unit (ICU) is one of the most technically advanced environments in healthcare, using a multitude of medical devices for drug administration, mechanical ventilation and patient monitoring. However, these technologies currently come with disadvantages, namely noise pollution, information overload and alarm fatigue—all caused by too many alarms. Individual medical devices currently generate alarms independently, without any coordination or prioritisation with other devices, leading to a cacophony where important alarms can be lost amongst trivial ones, occasionally with serious or even fatal consequences for patients. We have called this approach to the design of medical devices the single-device paradigm, and believe it is obsolete in modern hospitals where patients are typically connected to several devices simultaneously. Alarm rates of one alarm every four minutes for only the physiological monitors (as recorded in the ICUs of two hospitals contributing to this paper) degrades the quality of the patient’s healing environment and threatens patient safety by constantly distracting healthcare professionals. We outline a new approach to medical device design involving the application of human factors principles which have been successful in eliminating alarm fatigue in commercial aviation. Our approach comprises the networked-device paradigm, comprehensive alarms and humaniform information displays. Instead of each medical device alarming separately at the patient’s bedside, our proposed approach will integrate, prioritise and optimise alarms across all devices attached to each patient, display information more intuitively and hence increase alarm quality while reducing the number of alarms by an order of magnitude below current levels.

Indian Society of Critical Care Medicine Experts Committee Consensus Statement on ICU Planning and Designing, 2020

BACKGROUND

Indian Society of Critical Care Medicine (ISCCM) guidelines on Planning and Designing Intensive care (ICU) were first developed in 2001 and later updated in 2007. These guidelines were adopted in India, many developing Nations and major Institutions including NABH. Various international professional bodies in critical care have their own position papers and guidelines on planning and designing of ICUs; being the professional body of intensivists in India ISCCM therefore addresses the subject in contemporary context relevant to our clinical practice, its variability according to specialty and subspecialty, quality, resource limitation, size and location of the institution. Aim: To have a consensus document reflecting the philosophy of ISCCM to deliver safe & quality Critical Care in India, taking into consideration the requirement of regulatory agencies (national & international) and need of people at large, including promotion of training, education and skill upgradation. It also aiming to promote leadership and development and managerial skill among the critical care team. Material and Methods: Extensive review of literature including search of databases in English language, resources of regulatory bodies, guidelines and recommendations of international critical care societies. National Survey of ISCCM members and experts to understand their viewpoints on respective issues. Visiting of different types and levels of ICUs by team members to understand prevailing practices, aspiration and Challenges. Several face to face meetings of the expert committee members in big and small groups with extensive discussions, presentations, brain storming and development of initial consensus draft. Discussion on draft through video conferencing, phone calls, Emails circulations, one to one discussion Result: Based upon extensive review, survey and input of experts' ICUs were categorized in to three levels suitable in Indian setting. Level III ICUs further divided into sub category A and B. Recommendations were grouped in to structure, equipment and services of ICU with consideration of variation in level of ICU of different category of hospitals. Conclusion: This paper summarizes consensus statement of various aspect of ICU planning and design. Defined mandatory and desirable standards of all level of ICUs and made recommendations regarding structure and layout of ICUs. Definition of intensive care and intensivist, planning for strength of ICU and requirement of manpower were also described.

HOW TO CITE THIS ARTICLE

Rungta N, Zirpe KG, Dixit SB, Mehta Y, Chaudhry D, Govil D, et al. Indian Society of Critical Care Medicine Experts Committee Consensus Statement on ICU Planning and Designing, 2020. Indian J Crit Care Med 2020;24(Suppl 1):S43-S60.

Reduction of false alarms in the intensive care unit using an optimized machine learning based approach

This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric "score" from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

Evaluation of stressors in intensive care units

Objective

Physical and psychological stressors adversely affect the treatment and length of stay of patients in intensive care units. In this study, we aimed to describe environmental and psychological stressors affecting intensive care unit patients and to determine their priorities.

Material and Methods

In this study, the 40-item Intensive Care Unit Environmental Stressor Scale was administered to patients in the General Surgery Intensive Care Unit and the Anesthesiology and Reanimation Intensive Care Unit. The patients' age, gender, marital status, educational status, cause of hospitalization, and intensive care unit length of stay were questioned and recorded. Acute Physiology And Chronic Health Evaluation II scores were determined for intensive care unit patients.

Results

A total of 98 patients, 80 in the General Surgery Intensive Care Unit and 18 in the Anesthesiology and Reanimation Intensive Care Unit, were included in the study between May 1, 2015 and October 31, 2015. Fifty-six of the patients were male (57.1%) and 42 were female (42.9%). The mean age of the patients was 55.1±15.1 years. The mean intensive care unit length of stay was 3.4±1.6 days. The median Acute Physiology And Chronic Health Evaluation II score of the patients was 6 (0 to 17). The patients were most affected by thirst (mean 2.44). The second most stressful stress factor was the presence of tubes in the mouth and nose (mean 2.25). The least stressful factor for the patients was the presence of nurses constantly performing activities around the bed. Although 51% of the patients were postoperative, pain was ranked 5th among stress factors.

Conclusion

The environmental and psychological factors affecting intensive care unit patients varied according to age, sex, and educational and surgical status. These factors had adverse effects on the patients. The elimination or modification of these factors would contribute positively to the treatment of intensive care unit patients and shorten their length of stay in the intensive care unit.

The nocturnal acoustical intensity of the intensive care environment: an observational study

BACKGROUND

The intensive care unit (ICU) environment exposes patients to noise levels that may result in substantial sleep disruption. There is a need to accurately describe the intensity pattern and source of noise in the ICU in order to develop effective sound abatement strategies. The objectives of this study were to determine nocturnal noise levels and their variability and the related sources of noise within an Australian tertiary ICU.

METHODS

An observational cross-sectional study was conducted in a 24-bed open-plan ICU. Sound levels were recorded overnight during three nights at 5-s epochs using Extech (SDL 600) sound monitors. Noise sources were concurrently logged by two research assistants.

RESULTS

The mean recorded ambient noise level in the ICU was 52.85 decibels (dB) (standard deviation (SD) 5.89), with a maximum noise recording at 98.3 dB (A). All recorded measurements exceeded the WHO recommendations. Noise variability per minute ranged from 9.9 to 44 dB (A), with peak noise levels >70 dB (A) occurring 10 times/hour (SD 11.4). Staff were identified as the most common source accounting for 35% of all noise. Mean noise levels in single-patient rooms compared with open-bed areas were 53.5 vs 53 dB (p = 0.37), respectively.

CONCLUSION

Mean noise levels exceeded those recommended by the WHO resulting in an acoustical intensity of 193 times greater than the recommended and demonstrated a high degree of unpredictable variability, with the primary noise sources coming from staff conversations. The lack of protective effects of single rooms and the contributing effects that staffs have on noise levels are important factors when considering sound abatement strategies.

Speech intelligibility assessment of protective facemasks and air-purifying respirators

Speech Intelligibility (SI) is the perceived quality of sound transmission. In healthcare settings, the ability to communicate clearly with coworkers, patients, etc., is crucial to quality patient care and safety. The objectives of this study were to: (1) assess the suitability of the Speech Transmission Index (STI) methods for testing reusable and disposable facial and respiratory personal protective equipment (protective facemasks [PF], N95 filtering facepiece respirators [N95 FFR], and elastomeric half-mask air-purifying respirators [EAPR]) commonly worn by healthcare workers; (2) quantify STI levels of these devices; and (3) contribute to the scientific body of knowledge in the area of SI. SI was assessed using the STI under two experimental conditions: (1) a modified version of the National Fire Protection Association 1981 Supplementary Voice Communications System Performance Test at a Signal to Noise Ratio (SNR) of -15 (66 dBA) and (2) STI measurements utilizing a range of modified pink noise levels (52.5 dBA (-2 SNR) - 72.5 dBA (+7 SNR)) in 5.0 dBA increments. The PF models (Kimberly Clark 49214 and 3 M 1818) had the least effect on SI interference, typically deviating from the STI baseline (no-mask condition) by 3% and 4% STI, respectively. The N95FFR (3 M 1870, 3 M 1860) had more effect on SI interference, typically differing from baseline by 13% and 17%, respectively, for models tested. The EAPR models (Scott Xcel and North 5500) had the most significant impact on SI, differing from baseline by 42% for models tested. This data offers insight into the performance of these apparatus with respect to STI and may serve as a reference point for future respirator design considerations, standards development, testing and certification activities.

Assessment of noise pollution in and around a sensitive zone in North India and its non-auditory impacts

Noise pollution in hospitals is recognized as a serious health hazard. Considering this, the current study aimed to map the noise pollution levels and to explore the self reported non-auditory effects of noise in a tertiary medical institute. The study was conducted in an 1800-bedded tertiary hospital where 27 sites (outdoor, indoor, road side and residential areas) were monitored for exposure to noise using Sound Level Meter for 24h. A detailed noise survey was also conducted around the sampling sites using a structured questionnaire to understand the opinion of the public regarding the impact of noise on their daily lives. The equivalent sound pressure level (Leq) was found higher than the permissible limits at all the sites both during daytime and night. The maximum equivalent sound pressure level (Lmax) during the day was observed higher (>80dB) at the emergency and around the main entrance of the hospital campus. Almost all the respondents (97%) regarded traffic as the major source of noise. About three-fourths (74%) reported irritation with loud noise whereas 40% of respondents reported headache due to noise. Less than one-third of respondents (29%) reported loss of sleep due to noise and 8% reported hypertension, which could be related to the disturbance caused due to noise. Noise levels in and around the hospital was well above the permissible standards. The recent Global Burden of Disease highlights the increasing risk of non communicable diseases. The non-auditory effects studied in the current work add to the risk factors associated with non communicable diseases. Hence, there is need to address the issue of noise pollution and associated health risks specially for vulnerable population.

Reduction of false arrhythmia alarms using signal selection and machine learning

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced. The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on [Formula: see text]-score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.

Comparing average levels and peak occurrence of overnight sound in the medical intensive care unit on A-weighted and C-weighted decibel scales

PURPOSE

Sound levels in the intensive care unit (ICU) are universally elevated and are believed to contribute to sleep and circadian disruption. The purpose of this study is to compare overnight ICU sound levels and peak occurrence on A- vs C-weighted scales.

MATERIALS AND METHODS

This was a prospective observational study of overnight sound levels in 59 medical ICU patient rooms. Sound level was recorded every 10 seconds on A- and C-weighted decibel scales. Equivalent sound level (Leq) and sound peaks were reported for full and partial night periods.

RESULTS

The overnight A-weighted Leq of 53.6 dBA was well above World Health Organization recommendations; overnight C-weighted Leq was 63.1 dBC (no World Health Organization recommendations). Peak sound occurrence ranged from 1.8 to 23.3 times per hour. Illness severity, mechanical ventilation, and delirium were not associated with Leq or peak occurrence. Equivalent sound level and peak measures for A- and C-weighted decibel scales were significantly different from each other.

CONCLUSIONS

Sound levels in the medical ICU are high throughout the night. Patient factors were not associated with Leq or peak occurrence. Significant discordance between A- and C-weighted values suggests that low-frequency sound is a meaningful factor in the medical ICU environment.

Role of a service corridor in ICU noise control, staff stress, and staff satisfaction: environmental research of an academic medical center

OBJECTIVE

To investigate the role of a dedicated service corridor in intensive care unit (ICU) noise control and staff stress and satisfaction.

BACKGROUND

Shared corridors immediately adjacent to patient rooms are generally noisy due to a variety of activities, including service deliveries and pickups. The strategy of providing a dedicated service corridor is thought to reduce noise for patient care, but the extent to which it actually contributes to noise reduction in the patient care environment and in turn improves staff performance has not been previously documented.

METHODS

A before-and-after comparison was conducted in an adult cardiac ICU. The ICU was relocated from a traditional hospital environment to a new addition with a dedicated service corridor. A total of 118 nursing staff participated in the surveys regarding pre-move and post-move environmental comfort, stress, and satisfaction in the previous and new units. Acoustical measures of noise within the new ICU and a control environment of the previous unit were collected during four work days, along with on-site observations of corridor traffic.

RESULTS

Independent and paired sample t-tests of survey data showed that the perceived noise level was lower and staff reported less stress and more satisfaction in the new ICU (p < 0.01). Analyses of acoustical data confirmed that the new ICU was significantly quieter (p < 0.02). Observations revealed how the service corridor impacted patient care services and traffic.

CONCLUSIONS

The addition of a dedicated service corridor works in the new unit for improving noise control and staff stress and satisfaction.

KEYWORDS

Critical care/intensive care, noise, satisfaction, staff, work environment.

Sleeping on a problem: the impact of sleep disturbance on intensive care patients - a clinical review

Sleep disturbance is commonly encountered amongst intensive care patients and has significant psychophysiological effects, which protract recovery and increases mortality. Bio-physiological monitoring of intensive care patients reveal alterations in sleep architecture, with reduced sleep quality and continuity. The etiological causes of sleep disturbance are considered to be multifactorial, although environmental stressors namely, noise, light and clinical care interactions have been frequently cited in both subjective and objective studies. As a result, interventions are targeted towards modifiable factors to ameliorate their impact. This paper reviews normal sleep physiology and the impact that sleep disturbance has on patient psychophysiological recovery, and the contribution that the clinical environment has on intensive care patients' sleep.

Reliability and limitations of automated arrhythmia detection in telemetric monitoring after stroke

BACKGROUND AND PURPOSE

Guidelines recommend continuous ECG monitoring in patients with cerebrovascular events. Studies on intensive care units (ICU) demonstrated high sensitivity but high rates of false alarms of monitoring systems resulting in desensitization of medical personnel potentially endangering patient safety. Data on patients with acute stroke are lacking.

METHODS

One-hundred fifty-one consecutive patients with acute cerebrovascular events were prospectively included. Automatically identified arrhythmia events were analyzed by manual ECG analysis. Muting of alarms was registered. Sensitivity was evaluated by beat-to-beat analysis of the entire recorded ECG data in a subset of patients. Ethics approval was obtained by University of Erlangen-Nuremberg.

RESULTS

A total of 4809.5 hours of ECG registration and 22 509 alarms were analyzed. The automated detection algorithm missed no events but the overall rate of false alarms was 27.4%. Only 0.6% of all alarms indicated acute life-threatening events and 91.4% of these alarms were incorrect. Transient muting of acoustic alarms was observed in 20.5% patients.

CONCLUSIONS

Continuous ECG monitoring using automated arrhythmia detection is highly sensitive in acute stroke. However, high rates of false alarms and alarms without direct therapeutic consequence cause desensitization of personnel. Therefore, acoustic alarms may be limited to life-threatening events but standardized manual evaluation of all alarms should complement automated systems to identify clinically relevant arrhythmias.

Sleep in the Intensive Care Unit

Patients in the intensive care unit (ICU) are susceptible to sleep deprivation. Disrupted sleep is associated with increased morbidity and mortality in the critically ill patients. The etiology of sleep disruption is multifactorial. The article reviews the literature on sleep in the ICU, the effects of sleep deprivation, and strategies to promote sleep in the ICU. Until the impact of disrupted sleep is better explained, it is appropriate to provide critically ill patients with consolidated, restorative sleep.

Human Factors in Critical Care Medical Environments

The purpose of this chapter on human factors in critical care medical environments is to provide a systematic review of the human factors and ergonomics contributions that led to significant improvements in patient safety over the last five decades. The review will focus on issues that contributed to patient injury and fatalities and how human factors and ergonomics can improve performance of providers in critical care. Given the complexity of critical care delivery, a review needs to cover a wide range of subjects. In this review, I take a sociotechnical systems perspective on critical care and discuss the people, their technical and nontechnical skills, the importance of teamwork, technology, and ergonomics in this complex environment. After a description of the importance of a safety climate, the chapter will conclude with a summary on how human factors and ergonomics can improve quality in critical care delivery.

Evaluation of Noise Levels in Intensive Care Units in Two Large Teaching Hospitals – A Prospective Observational Study

Critically ill patients do not sleep well. One of the major environmental factors influencing sleep is noise. We prospectively measured noise levels and their relation to the time of day and location in different parts of two separate intensive care units (ICUs). Maximum, minimum and average noise levels were collected over 24 hour periods on five random days in both ICUs using digital sound meters, measured by the A-weighted decibel scale (dB (A)). The World Health Organisation (WHO) recommends that the average background noise in hospitals should not exceed 35 dB (A), and that peaks during the night should be less than 40 dB (A). The measured noise levels in both ICUs were well above the WHO standards. We recommend that various aspects, including staff education and modification of ICU design, must be carefully considered to provide an optimum environment for critically ill patients.

Connecting the dots: rule-based decision support systems in the modern EMR era

The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and "sniffers" within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.

Effects of familiar voices on brain activity

This study aimed to examine the extent to which a familiar voice influences brain activity. Participants were nine healthy female volunteers aged 21-34 years old (with a mean age of 25.78 ± 4.04 years). Brain activity was recorded during periods of silence, familiar and unfamiliar voices. Electroencephalographic data were collected and analyzed using a frequency rate set at 5 min. To account for emotional influences imbedded into the contents of the voice stimuli, both the voice of a familiar family member and the voice of a stranger were used to record a well-known Japanese fairy tale, 'Momotaro'. Results revealed that listening to familiar voices increased the rate of the β band (13-30 Hz) in all four brain areas (F3, F4, C3 and C4). In particular, increased activity was observed at F4 and C4. Findings revealed that in study, participants' familiar voices activated cerebral functioning more than unfamiliar voices.

Reducing hospital noise: a review of medical device alarm management

Increasing noise in hospital environments, especially in intensive care units (ICUs) and operating rooms (ORs), has created a formidable challenge for both patients and hospital staff. A major contributing factor for the increasing noise levels in these environments is the number of false alarms generated by medical devices. This study focuses on discovering best practices for reducing the number of false clinical alarms in order to increase patient safety and provide a quiet environment for both work and healing. The researchers reviewed Pub Med, Web of Knowledge and Google Scholar sources to obtain original journal research and review articles published through January 2012. This review includes 27 critically important journal articles that address different aspects of medical device alarms management, including the audibility, identification, urgency mapping, and response time of nursing staff and different solutions to such problems. With current technology, the easiest and most direct method for reducing false alarms is to individualize alarm settings for each patient's condition. Promoting an institutional culture change that emphasizes the importance of individualization of alarms is therefore an important goal. Future research should also focus on the development of smart alarms.

State-of-the-art sensor technology in Spain: invasive and non-invasive techniques for monitoring respiratory variables

The interest in measuring physiological parameters (especially arterial blood gases) has grown progressively in parallel to the development of new technologies. Physiological parameters were first measured invasively and at discrete time points; however, it was clearly desirable to measure them continuously and non-invasively. The development of intensive care units promoted the use of ventilators via oral intubation ventilators via oral intubation and mechanical respiratory variables were progressively studied. Later, the knowledge gained in the hospital was applied to out-of-hospital management. In the present paper we review the invasive and non-invasive techniques for monitoring respiratory variables.

Intensive care unit alarms--how many do we need?

OBJECTIVE

To validate cardiovascular alarms in critically ill patients in an experimental setting by generating a database of physiologic data and clinical alarm annotations, and report the current rate of alarms and their clinical validity. Currently, monitoring of physiologic parameters in critically ill patients is performed by alarm systems with high sensitivity, but low specificity. As a consequence, a multitude of alarms with potentially negative impact on the quality of care is generated.

DESIGN

Prospective, observational, clinical study.

SETTING

Medical intensive care unit of a university hospital.

DATA SOURCE

Data from different medical intensive care unit patients were collected between January 2006 and May 2007.

MEASUREMENTS AND MAIN RESULTS

Physiologic data at 1-sec intervals, monitor alarms, and alarm settings were extracted from the surveillance network. Video recordings were annotated with respect to alarm relevance and technical validity by an experienced physician. During 982 hrs of observation, 5934 alarms were annotated, corresponding to six alarms per hour. About 40% of all alarms did not correctly describe the patient condition and were classified as technically false; 68% of those were caused by manipulation. Only 885 (15%) of all alarms were considered clinically relevant. Most of the generated alarms were threshold alarms (70%) and were related to arterial blood pressure (45%).

CONCLUSION

This study used a new approach of off-line, video-based physician annotations, showing that even with modern monitoring systems most alarms are not clinically relevant. As the majority of alarms are simple threshold alarms, statistical methods may be suitable to help reduce the number of false-positive alarms. Our study is also intended to develop a reference database of annotated monitoring alarms for further application to alarm algorithm research.

Associations of exposure to noise with physiological and psychological outcomes among post-cardiac surgery patients in ICUs

OBJECTIVES

This study sought to study the associations of noise with heart rate, blood pressure, and perceived psychological and physiological responses among post-cardiac surgery patients in ICUs.

METHODS

Forty patients participated in this study after recovering from anesthesia. A sound-level meter was placed at bedsides to measure noise level for 42 hours, and patients' heart rate and blood pressure were recorded every 5 minutes. Patients were also interviewed for their perceived psychological/physiological responses.

RESULTS

The average noise level was between 59.0 and 60.8 dB(A) at the study site. Annoyance and insomnia were the respective psychological and physiological responses reported most often among the patients. Although noise level, irrespective of measures, was not observed to be significantly associated with the self-assessed psychological and physiological responses, it was significantly associated with both heart rate and blood pressure.

CONCLUSIONS

Our study demonstrated that the noise in ICUs may adversely affect the heart rate and blood pressure of patients, which warrants the attention of hospital administrators and health care workers.

[Telemedicine: Improving the quality of care for critical patients from the pre-hospital phase to the intensive care unit]

The Health System is in crisis and critical care (from transport systems to the ICU) cannot escape from that. Lack of integration between ambulances and reference Hospitals, a deep shortage of critical care specialists and assigned economical resources that increase less than critical care demand are the cornerstones of the problem. Moreover, the analysis of the situation anticipated that the problem will be worse in the future. "Closed" ICUs in which critical care specialists direct patient care outperform "open" ones in which primary admitting physicians direct patient care in consultation with critical care specialists. However, the current paradigm in which a critical care specialist is close to the patient is in the edge of the trouble so, only a new paradigm could help to increase the number of patients under intensivist care. Current information technology and networking capabilities should be fully exploited to improve both the extent and quality of intensivist coverage. Far to be a replacement of the existing model Telemedicine might be a complimentary tool. In fact, to centralize medical data into servers has many additional advantages that could even improve the way in which critical care physicians take care of their patients under the traditional system.

Telemedicine in critical care

Critical care medicine is the specialty that cares for patients with acute life-threatening illnesses where intensivists look after all aspects of patient care. Nevertheless, shortage of physicians and nurses, the relationship between high costs and economic restrictions, and the fact that critical care knowledge is only available at big hospitals puts the system on the edge. In this scenario, telemedicine might provide solutions to improve availability of critical care knowledge where the patient is located, improve relationship between attendants in different institutions and education material for future specialist training. Current information technologies and networking capabilities should be exploited to improve intensivist coverage, advanced alarm systems and to have large critical care databases of critical care signals.

Collection of annotated data in a clinical validation study for alarm algorithms in intensive care--a methodologic framework

INTRODUCTION

Monitoring of physiologic parameters in critically ill patients is currently performed by threshold alarm systems with high sensitivity but low specificity. As a consequence, a multitude of alarms are generated, leading to an impaired clinical value of these alarms due to reduced alertness of the intensive care unit (ICU) staff. To evaluate a new alarm procedure, we currently generate a database of physiologic data and clinical alarm annotations.

METHODS

Data collection is taking place at a 12-bed medical ICU. Patients with monitoring of at least heart rate, invasive arterial blood pressure, and oxygen saturation are included in the study. Numerical physiologic data at 1-second intervals, monitor alarms, and alarm settings are extracted from the surveillance network. Bedside video recordings are performed with network surveillance cameras.

RESULTS

Based on the extracted data and the video recordings, alarms are clinically annotated by an experienced physician. The alarms are categorized according to their technical validity and clinical relevance by a taxonomy system that can be broadly applicable. Preliminary results showed that only 17% of the alarms were classified as relevant, and 44% were technically false.

DISCUSSION

The presented system for collecting real-time bedside monitoring data in conjunction with video-assisted annotations of clinically relevant events is the first allowing the assessment of 24-hour periods and reduces the bias usually created by bedside observers in comparable studies. It constitutes the basis for the development and evaluation of "smart" alarm algorithms, which may help to reduce the number of alarms at the ICU, thereby improving patient safety.

Adverse effects of sleep deprivation in the ICU

The hospital is not conducive to sleep. Patients in the ICU are particularly susceptible to sleep disruption secondary to environmental and medical issues. Despite the frequency of sleep disruption in the ICU, the quality of critically ill patients' sleep is often overlooked. This article discusses the following issues essential to understanding the factors associated with sleep loss in the ICU: (1) core elements to consider from the baseline sleep history, (2) impact of the ICU environment on the ICU patient's sleep pattern, and (3) overall systematic impact of sleep deprivation on the ICU patient.

A Review of the Research Literature on Evidence-Based Healthcare Design

Objective:

This report surveys and evaluates the scientific research on evidence-based healthcare design and extracts its implications for designing better and safer hospitals.

Background:

It builds on a literature review conducted by researchers in 2004.

Methods:

Research teams conducted a new and more exhaustive search for rigorous empirical studies that link the design of hospital physical environments with healthcare outcomes. The review followed a two-step process, including an extensive search for existing literature and a screening of each identified study for the relevance and quality of evidence.

Results:

This review found a growing body of rigorous studies to guide healthcare design, especially with respect to reducing the frequency of hospital-acquired infections. Results are organized according to three general types of outcomes: patient safety, other patient outcomes, and staff outcomes. The findings further support the importance of improving outcomes for a range of design characteristics or interventions, including single-bed rooms rather than multibed rooms, effective ventilation systems, a good acoustic environment, nature distractions and daylight, appropriate lighting, better ergonomic design, acuity-adaptable rooms, and improved floor layouts and work settings. Directions for future research are also identified.

Conclusions:

The state of knowledge of evidence-based healthcare design has grown rapidly in recent years. The evidence indicates that well-designed physical settings play an important role in making hospitals safer and more healing for patients, and better places for staff to work.

Characterizing noise and perceived work environment in a neurological intensive care unit

The hospital sound environment is complex. Alarms, medical equipment, activities, and ventilation generate noise that may present occupational problems as well as hinder recovery among patients. In this study, sound measurements and occupant evaluations were conducted in a neurological intensive care unit. Staff completed questionnaires regarding psychological and physiological reactions to the sound environment. A-weighted equivalent, minimum, and maximum (L(Aeq),L(AFMin),L(AFMax)) and C-weighted peak (L(CPeak)) sound pressure levels were measured over five days at patient and staff locations. Acoustical descriptors that may be explored further were investigated, including level distributions, restorative periods, and spectral content. Measurements near the patients showed average L(Aeq) values of 53-58 dB. The mean length of restorative periods (L(Aeq) below 50 dB for more than 5 min) was 9 and 13 min for day and night, respectively. Ninety percent of the time, the L(AFMax) levels exceeded 50 dB and L(CPeak) exceeded 70 dB. Dosimeters worn by the staff revealed higher noise levels. Personnel perceived the noise as contributing to stress symptoms. Compared to the majority of previous studies, this study provides a more thorough description of intensive care noise and aids in understanding how the sound environment may be disruptive to occupants.

Noise levels in a general intensive care unit: a descriptive study

The aim of this small-scale study was to measure, analyse and compare levels of acoustic noise, in a nine-bedded general intensive care unit (ICU). Measurements were undertaken using the Norsonic 116 sound level meter recording noise levels in the internationally agreed 'A' weighted scale. Noise level data were obtained and recorded at 5 min over 3 consecutive days. Results of noise level analysis indicated that mean noise levels within this clinical area was 56.42 dB(A), with acute spikes reaching 80 dB(A). The quietest noise level attained was that of 50 dB(A) during sporadic intervals throughout the 24-h period. Parametric testing using analysis of variance found a positive relationship (p <or= 0.001) between the nursing shifts and the day of the week. However, Scheffe multiple range testing showed significant differences between the morning shift, and the afternoon and night shifts combined (p <or= 0.05). There was no statistical difference between the afternoon and night shifts (p >or= 0.05). While the results of this study may seem self-evident in many respects, what it has highlighted is that the problem of excessive noise exposure within the ICU continues to go unabated. More concerning is that the prolonged effects of excessive noise exposure on patients and staff alike can have deleterious effect on the health and well-being of these individuals.

Clinical practice guidelines for support of the family in the patient-centered intensive care unit: American College of Critical Care Medicine Task Force 2004–2005

OBJECTIVE

To develop clinical practice guidelines for the support of the patient and family in the adult, pediatric, or neonatal patient-centered ICU.

PARTICIPANTS

A multidisciplinary task force of experts in critical care practice was convened from the membership of the American College of Critical Care Medicine (ACCM) and the Society of Critical Care Medicine (SCCM) to include representation from adult, pediatric, and neonatal intensive care units.

EVIDENCE

The task force members reviewed the published literature. The Cochrane library, Cinahl, and MedLine were queried for articles published between 1980 and 2003. Studies were scored according to Cochrane methodology. Where evidence did not exist or was of a low level, consensus was derived from expert opinion.

CONSENSUS PROCESS

The topic was divided into subheadings: decision making, family coping, staff stress related to family interactions, cultural support, spiritual/religious support, family visitation, family presence on rounds, family presence at resuscitation, family environment of care, and palliative care. Each section was led by one task force member. Each section draft was reviewed by the group and debated until consensus was achieved. The draft document was reviewed by a committee of the Board of Regents of the ACCM. After steering committee approval, the draft was approved by the SCCM Council and was again subjected to peer review by this journal.

CONCLUSIONS

More than 300 related studies were reviewed. However, the level of evidence in most cases is at Cochrane level 4 or 5, indicating the need for further research. Forty-three recommendations are presented that include, but are not limited to, endorsement of a shared decision-making model, early and repeated care conferencing to reduce family stress and improve consistency in communication, honoring culturally appropriate requests for truth-telling and informed refusal, spiritual support, staff education and debriefing to minimize the impact of family interactions on staff health, family presence at both rounds and resuscitation, open flexible visitation, way-finding and family-friendly signage, and family support before, during, and after a death.