Satisfaction Domains Differ between the Patient and Their Family in Adult Intensive Care Units.
Amartya MukhopadhyayGe SongPei Zhen SimKit Cheng TingJeffrey Kwang Sui YooQing Li WangRaudhah Binte Haji Mohamad MascuriVenetia Hui Ling OngJason PhuaYanika KowitlawakulPublished in: BioMed research international (2016)
Background. Patients' and family's satisfaction data from the Asian intensive care units (ICUs) is lacking. Objective. Domains between patient and family satisfaction and contribution of each domain to the general satisfaction were studied. Method. Over 3 months, adult patients across 4 ICUs staying for more than 48 hours with abbreviated mental test score of 7 or above and able to understand English and immediate family members were surveyed by separate validated satisfaction questionnaires. Results. Two hundred patients and 194 families were included in the final analysis. Significant difference in the satisfaction scores was observed between the ICUs. Patients were most and least satisfied in the communication (4.2 out of 5) and decision-making (2.9 out of 5) domains, respectively. Families were most and least satisfied in the relationship with doctors (3.9 out of 5) and family's involvement domains (3.3 out of 5), respectively. Domains contributing most to the general satisfaction were the illness management domain for patients (β coefficient = 0.44) and characteristics of doctors and nurses domain for family (β coefficient = 0.45). Discussion. In an Asian ICU community, patients and families differ in their expectations and valuations of health care processes. Health care providers have difficult tasks in attending to these different domains.
Keyphrases
- end stage renal disease
- healthcare
- ejection fraction
- newly diagnosed
- intensive care unit
- chronic kidney disease
- peritoneal dialysis
- mental health
- prognostic factors
- magnetic resonance imaging
- decision making
- computed tomography
- patient reported outcomes
- magnetic resonance
- electronic health record
- machine learning
- medical students
- health information
- artificial intelligence