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Word identification accuracy is modulated by many factors including linguistic characteristics of words (frequent vs. infrequent), listening environment (noisy vs. quiet), and listener-related differences (older vs. younger). Nearly, all studies investigating these factors use high-familiarity words and noise signals that are either energetic maskers (e.g., white noise) or informational maskers composed of competing talkers (e.g., multitalker babble). Here, we expand on these findings by examining younger and older listeners' speech-in-noise perception for words varying in both frequency and familiarity within a simulated hospital noise that has important non-speech information. The method was inspired by the real-world challenges aging patients can face in understanding less familiar medical terminology used by healthcare professionals in noisy hospital environments. Word familiarity data from older and young adults were collected for 800 medically related terms. Familiarity ratings were highly correlated between the two age groups. Older adults' transcription accuracy for sentences with medical terminology that vary in their familiarity and frequency was assessed across four listening conditions: hospital noise, speech-shaped noise, amplitude-modulated speech-shaped noise, and quiet. Listeners were less accurate in noise conditions than in a quiet condition and were more impacted by hospital noise than either speech-shaped noise. Sentences with low-familiarity and low-frequency medical words combined with hospital noise were particularly detrimental for older adults compared to younger adults. The results impact our theoretical understanding of speech perception in noise and highlight real-world consequences of older adults' difficulties with speech-in-noise and specifically noise containing competing, non-speech information., (© 2024. The Author(s).)

Hospital noise can be problematic for both patients and staff and consistently is rated poorly on national patient satisfaction surveys. A surge of research in the last two decades highlights the challenges of healthcare acoustic environments. However, existing research commonly relies on conventional noise metrics such as equivalent sound pressure level, which may be insufficient to fully characterize the fluctuating and complex nature of the hospital acoustic environments experienced by occupants. In this study, unsupervised machine learning clustering techniques were used to extract patterns of activity in noise and the relationship to patient perception. Specifically, nine patient rooms in three adult inpatient hospital units were acoustically measured for 24 h and unsupervised machine learning clustering techniques were applied to provide a more detailed statistical analysis of the acoustic environment. Validation results of five different clustering models found two clusters, labeled active and non-active, using k-means. Additional insight from this analysis includes the ability to calculate how often a room is active or non-active during the measurement period. While conventional LAeq was not significantly related to patient perception, novel metrics calculated from clustered data were significant. Specifically, lower patient satisfaction was correlated with higher Active Sound Levels, higher Total Percent Active, and lower Percent Quiet at Night metrics. Overall, applying statistical clustering to the hospital acoustic environment offers new insights into how patterns of background noise over time are relevant to occupant perception., (© 2023 Acoustical Society of America.)

Noise in healthcare settings, such as hospitals, often exceeds levels recommended by health organizations. Although researchers and medical professionals have raised concerns about the effect of these noise levels on spoken communication, objective measures of behavioral intelligibility in hospital noise are lacking. Further, no studies of intelligibility in hospital noise used medically relevant terminology, which may differentially impact intelligibility compared to standard terminology in speech perception research and is essential for ensuring ecological validity. Here, intelligibility was measured using online testing for 69 young adult listeners in three listening conditions (i.e., quiet, speech-shaped noise, and hospital noise: 23 listeners per condition) for four sentence types. Three sentence types included medical terminology with varied lexical frequency and familiarity characteristics. A final sentence set included non-medically related sentences. Results showed that intelligibility was negatively impacted by both noise types with no significant difference between the hospital and speech-shaped noise. Medically related sentences were not less intelligible overall, but word recognition accuracy was significantly positively correlated with both lexical frequency and familiarity. These results support the need for continued research on how noise levels in healthcare settings in concert with less familiar medical terminology impact communications and ultimately health outcomes.

Medical and nursing staff working in hospitals often experience exposure to extreme sound environments, and there is growing evidence of the negative impacts. Previous research highlighted various complexities regarding noise sources in hospitals; however, identifications of intrinsic noise categories that can reveal the complex mixture of existing hospital noise is still limited. The objective of this work was to identify intrinsic categories of the noise sources based on staff perceived annoyance and explore clear associations of these categorized noise sources with psychological perceptions. The staff perceptual responses regarding hospital noise were assessed by conducting surveys at the three pediatric and neonatal care units in two hospitals. Using principle component analysis (PCA), the psychological annoyance responses of 94 participants were used to derive the inherent structural patterns of the existing noise sources. The derived PCA categorization was validated on mixed-model analysis of variances, and employed on regression models to explore potential associations between the categorized noise factors and the staff's psychological perceptions. The results highlighted three intrinsic noise categories and their negative impacts on staff's psychological perceptions including work/rest disturbance and noisiness. Taken as a whole, the findings better reveal problematic noise source categories and establish a framework for hospital noise control that is less source-specific and more broadly generalizable.

The previous hospital acoustic literature has highlighted some important considerations and various complexities regarding objective noise measurements. However, extensive use of conventional acoustical metrics such as logarithmically averaged equivalent sound pressure levels (L eq ) do not sufficiently describe hospital acoustical environments and often lack considerations of the room-based activity status that can significantly influence the soundscape. The goal of this study was to explore utilizing statistical clustering techniques in healthcare settings with a particular aim of identifying room-activity conditions. The acoustic measurements were conducted in the patient rooms of two pediatric hospital units and subsequently classified based on two room-activity conditions-active and non-active conditions-by applying statistical clustering analyses with standard k-means and fuzzy c-means algorithms. The results of this study demonstrate the most probable noise levels and degree of associations of the measured noise levels for the two room-activity conditions. The results were further validated in terms of the clustered levels, the number of conditions, and parameter dependency. The clustering approach allows for a more thorough soundscape characterization than single-number level descriptors alone by providing a method of identifying and describing the noise levels associated with typical, intrinsic activity conditions experienced by occupants.

This retrospective, exploratory study examined 8,366 patient responses to surveys on patient satisfaction and patient room spatial layout in a large academic teaching hospital consisting of 17 nursing units and 382 patient rooms. This study included four spatial measures: average distance to the nurse station, room handedness, location of bed, and location of first encounter-and explored their statistical associations with two types of patient satisfaction surveys (Hospital Consumer Assessment of the Healthcare Provider and Systems and third party). The study had two phases: a preliminary study of 3,751 patient respondents in a limited diagnosis-related group (DRG) over 5 years and a general study of 4,615 patient respondents with a broader range of DRG's over 2 different years from the preliminary study. Findings indicated statistically significant relationships between all four spatial layout measures and specific survey questions pertaining to perception of nursing, physician, individual care, and overall room environment. Results emphasize the importance of hospital design-and spatial layout in particular-on patient satisfaction.

Patient's perception of care-referred to as patient satisfaction-is of great interest in the healthcare industry, as it becomes more directly tied to the revenue of the health system providers. The perception of care has now become important in addition to the actual health outcome of the patient. The known influencers for the patient perception of care are the patient's own characteristics as well as the quality of service received. In patient surveys, the physical environment is noted as important for being clean and quiet but is not considered a critical part of patient satisfaction or other health outcomes. Patient perception of care is currently measured as patient satisfaction, a systematic collection of perceptions of social interactions from an individual person as well as their interaction with the environment. This exploration of the literature intends to explore the rigorous, statistically tested research conducted that has a spatial predictor variable and a health or behavior outcome, with the intent to begin to further test the relationships of these variables in the future studies. This literature review uses the patient satisfaction framework of components of influence and identifies at least 10 known spatial environmental variables that have been shown to have a direct connection to the health and behavior outcome of a patient. The results show that there are certain features of the spatial layout and environmental design in hospital or work settings that influence outcomes and should be noted in the future research., (© The Author(s) 2016.)

Objective: This study investigates the perception of nurses about their lighting environment at medical-surgical hospital units in order to understand areas of improvement for lighting at these units., Background: The bulk of the research about nurses and lighting is focused on nighttime nursing, exploring the disruptions of nurses' circadian rhythm and maintaining alertness. The understanding of nurses' perception about lighting and its impact on nurses' task performance and patient examination remains imprecise., Methods: This study used an online survey to ask a set of questions about lighting in medical-surgical units at five key locations including centralized nurse stations, decentralized nurse stations (DCNS), patient bedsides, patient bathrooms, and corridors from 393 survey participants. It then explored the survey findings in more depth through conducting focus groups with eight volunteer nurses., Results: Lighting conditions at patient besides and DCNSs were significantly less desirable for nurses compared to other locations. A significant relationship between nurses' access to lighting controls (switches and dimmers) and satisfaction about the lighting environment was found. No significant relationship was observed between the individual characteristics of nurses (such as age, years of experience, etc.) and findings of this study., Conclusions: Thoughtful design of the lighting environment can improve nurses' satisfaction and perception about their working environment., (© The Author(s) 2015.)