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. 2022 Sep 1;21(1):243.
doi: 10.1186/s12912-022-00948-5.

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

Milad Abbasi  1   2 , Saied Yazdanirad  3   4 , Mojtaba Zokaei  2 , Mohsen Falahati  2 , Nazila Eyvazzadeh  5
Affiliations

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

Milad Abbasi et al. BMC Nurs. .

Abstract

Background: Hospital noise can adversely impact nurses' health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care.

Methods: This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses' noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses' individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used.

Results: For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283).

Conclusion: Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.

Keywords: Annoyance; Bayesian network; Noise exposure; Nurse; Quality of patient care; Sensitivity.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The dependencies among the marginal probabilities of the studied variables based on the Bayesian network model
Fig. 2
Fig. 2
Sensitivity analysis on noise exposure: (a) low and (b) high
Fig. 3
Fig. 3
Sensitivity analysis on noise sensitivity: (a) low and (b) high
Fig. 4
Fig. 4
Sensitivity analysis on noise annoyance: (a) low and (b) high
Fig. 5
Fig. 5
Sensitivity analysis on simultaneous noise exposure and noise sensitivity: (a) low and (b) high
Fig. 6
Fig. 6
The ROC curve
See this image and copyright information in PMC

References

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