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Statistical Analysis to Identify Factor Related with Patients Satisfaction on General Service in Assosa General Hospital, Western Ethiopia
Author(s) -
Endalkachew Abebe Debelu
Publication year - 2022
Publication title -
international journal of medical and biomedical studies
Language(s) - English
Resource type - Journals
eISSN - 2589-8698
pISSN - 2589-868X
DOI - 10.32553/ijmbs.v6i4.2489
Subject(s) - logistic regression , medicine , patient satisfaction , health care , family medicine , population , odds ratio , service (business) , nursing , environmental health , economy , economics , economic growth
Patient satisfaction is the extent to which patients are happy with their healthcare provided from the hospital/health center. It is considered as one of the desired outcomes of health care and it is directly related with utilization of health services. The aim of the study was to identify the factor related with patient satisfaction on general service in Assosa General hospital. To reach the aim, the data have been collected through questionnaire from 735 patients, selected using simple random sample of total 2509 population, in October 1 to  November 30, 2021. Frequency distribution Table and pie chart were used for data description; binary logistic regression was used to identify the factors that affect patients satisfaction, using R version 4.1.3. The results of this study showed that 43.27% of patients were satisfied and the remaining 56.73% were not satisfied with the service they have received from the hospital. Patients’ satisfaction was associated with age, gender, health care quality of services, waiting time for treatment, fee-for-service, availability of drug, laboratory service, cleanness of hospital, doctor and nurse response. From the result of binary logistic regression it can be concluded that age, health care quality of services, waiting time for treatment, availability of drug, cleanness of hospital, doctor and nurse response has significant effect on the satisfaction of patients.Keywords: Satisfaction of patients, Binary logistic regression, the odds

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