This study aimed to construct and test structural equation modeling of the causal relationship between quality of healthcare, patient satisfaction, and intent to revisit perceived by patients using regional hub public hospitals. In this study, data of 2,951 outpatients and 3,135 inpatients were collected using the “2018 Regional Hub Public Hospital Operational Evaluation.” A structural equation model was used to understand the relationship between patient satisfaction and intent to revisit, and bootstrap analysis was performed. In the direct effect, outpatients were presented in the order of the physician’s practice service, the hospital’s environment, and patient satisfaction. Inpatients were in the order of the physician’s practice service and, medical staff’s kindness and consideration,; patient satisfaction was shown in this order. In the indirect effect, the outpatients were presented in the order of physician’s practice service, medical staff’s kindness and consideration, and hospital’s physical environment. Inpatients were introduced in the order of medical staff’s kindness and consideration, nurse’s practice service, physician’s practice service, and patient satisfaction. Regional hub public hospitals need high-quality medical services and efforts from all departments to treat patients with sincerity to improve patient satisfaction and increase intent to revisit.

Recent changes in the medical paradigm highlight the importance of patient-centered communication. However, because of the lack of awareness of dental clinics and competency of medical personnel, the quality of medical services in terms of the communication between doctors and patients has not improved. This study analyzed the impact of health communication and medical service quality, service value, and patient satisfaction on the intention to revisit dental clinics. The study participants were outpatients treated at 10 dental clinics in Seoul. The research data were collected using a questionnaire during visits to these dental clinics from December 1 to December 30, 2016. A total of 600 questionnaires were distributed (60 copies to each clinics) and 570 valid questionnaires were used for the analysis. The influence of the factors was determined using structural equation modeling. The factors influencing service value were reliability (β = 0.364, p < 0.001), expertise (β = 0.319, p < 0.001), communication by doctors (β = 0.224, p < 0.001), and tangibility (β = 0.136, p < 0.05). In addition, the factors influencing patient satisfaction were reliability (β = 0.258, p < 0.001), tangibility (β = 0.192, p < 0.001), communication by doctors (β = 0.163, p < 0.001), and expertise (β = 0.122, p < 0.01). Further, service value (β = 0.438, p < 0.001) raised patient satisfaction, which was found to influence the intention to revisit dental clinics (β = 0.383, p < 0.001). Providing accurate medical services to inpatients based on smooth communication between doctors and patients improves patient satisfaction. In addition, doctors can build long-term relations with patients by increasing patients’ intention to revisit through patient-oriented communication.

Background: The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) plays a potential role in cardiovascular diseases. However, its association with angina pectoris remains unclear. Herein, we aimed to explore their relationship. Methods: This cross-sectional retrospective study included the 2009–2018 data from 22,562 adults diagnosed with angina pectoris, retrieved from the National Health and Nutrition Examination Survey (NHANES) database. NHHR was estimated from laboratory data, and angina pectoris diagnosis was ascertained from the NHANES questionnaire. Results: Angina pectoris risk was greater in the highest than in the lowest NHHR tertile (odds ratio [OR] = 1.61; 95% confidence interval (CI), 1.15–2.54; P = 0.006). Weighted logistic regression showed a positive association between NHHR and angina pectoris in the fully adjusted model (OR = 1.17; 95% CI, 1.07–1.28; P = 0.001). Restricted cubic spline analysis showed a linear association (P = 0.6572). Subgroup analyses indicated no significant differences across different stratifications (P > 0.05, all). Random forest analyses and Boruta algorithm corroborated that NHHR is a strong predictor of angina pectoris. Among the eight machine-learning models evaluated for predictive capabilities, the logistic regression model demonstrated the strongest predictive capability, with an area under the curve of 0.831. Conclusions: Our study suggests that NHHR is a risk factor for angina pectoris and may be used for risk prediction and to inform future intervention programs to reduce its incidence.

Jung M, et al. J Prev Med Public Health. 2010 Jul;43(4):292-300. http://dx.doi.org/10.3961/jpmph.2010.43.4.292