A new tool to assess patients’ comfort during hospitalization: The Hospital Discomfort Risk questionnaire

Abstract

Aim:We aimed to develop a tool for the assessment of the risk of patient discomfort in Spanish hospital wards. Background: Several studies described tools to assess comfort but most are long and complex.Methods: Cross-sectional study performed in three phases ((a) initial design; (b) refinement and psychometric testing; and (c) internal validation of the Hospital Discomfort Risk [HDR] questionnaire). Results:A voluntary expert panel proposed the HDR questionnaire. Internal consistency and factorial analysis were investigated in 270 (53.7% men, mean age 57.33 ± 18.7 years) inpatients. Based on the Cronbach's α, three items were removed to the final 8-item version of the questionnaire. The HDR questionnaire showed a good predictive ability for identifying the risk of discomfort (c-index: .897, 95% CI 0.854-0.930; p < .001). Conclusions:The HDR questionnaire could be useful for identifying inpatients at risk of discomfort, but further prospective studies should externally validate these results. Implications in Nursing Management:Nurses are the healthcare professionals with better access to patients and the first in identifying complications of hospitalization.Patients' discomfort could be routinely assessed during hospitalizations using the HDR questionnaire. Nurse managers should play an important role in this accomplishment, by promoting its use and knowledge among the nurse staff. K E Y W O R D S comfort, hospitalization, nursing, risk assessmentReceivers operating characteristic curve confirmed that the HDR score had a good predictive ability for identifying patients at risk of discomfort, with a c-index of .897 (95% CI 0.854-0.930, p < .001; Figure 1).According to the Youden index, a score of 20 showed the best combination of sensitivity and specificity. Thus, we established the cut-off value for "at risk of discomfort" as a score >20. When we performed the ROC curve with the HDR score as categorical still showed a good predictive ability for identifying patients at risk of discomfort, with a c-index of 0.817 (95% CI 0.743-0.891, p < .001; Figure 1). The DCA TA B L E 1 Baseline characteristics N = 270 Male sex, n (%) 145 (53.7) Age (years), mean ± SD 57.33 ± 18.7 Body mass index (k/m 2 ), mean ± SD 26.5 ± 5.0 Marital status, n (%) Single 57 (21.1) Married or partner 150 (55.6) Divorced 37 (13.7) Widowed 26 (9.6) Hospitalization stay (days), mean ± SD 6.9 ± 7.8 Hospital size, n (%) Extended bed occupancy 180 (66.7) Reduced bed occupancy 90 (33.3) Main reason for hospitalization, n (%) Medical condition 169 (62.6) Surgical intervention 101 (37.4) Abbreviation: SD, standard deviation.

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“…Scale in the measurement of transpersonal human care based on the theory of Jean Watson FatemehMohammadipour et al, (2017);Santos et al, (2017);Romero-García et al, 2018;Shin & Park, (2018);Gishu et al, (2019);Vera-Catalán et al, (2019);…”