ABSTRACT
INTRODUCTION: Primary healthcare providers play a crucial role in educating their patients on chronic disease self-management (CDSM). This study aims to evaluate CDSM competency and satisfaction in patients receiving their healthcare from public or private healthcare providers. METHODS: A cross-sectional household study was conducted in a public housing estate using a standardised questionnaire to interview Singaporeans and permanent residents aged 40 years and above, who were diagnosed with at least 1 of these chronic diseases: hyperlipidaemia, hypertension or diabetes mellitus. CDSM competency was evaluated with the Partners In Health (PIH) scale and a knowledge based questionnaire. Satisfaction was evaluated using a satisfaction scale. RESULTS: In general, the 420 respondents demonstrated good CDSM competency, with 314 followed up at polyclinics and 106 by general practitioners (GPs). There was no significant difference between patients of polyclinics and GPs in CDSM competency scores (mean PIH score 72.9 vs 75.1, P=0.563), hypertension knowledge scores (90.9 vs 85.4, P=0.16) and diabetes knowledge scores (84.3 vs 79.5, P=0.417), except for hyperlipidaemia knowledge scores (78.6 vs 84.7, P=0.043). However, respondents followed up by GPs had higher satisfaction rates than did those followed up at polyclinics (odds ratio 3.6, confidence interval 2.28-5.78). Favourable personality of the doctors and ideal consultation duration led to higher satisfaction in the GP setting. A longer waiting time led to lower satisfaction in the polyclinic group. CONCLUSION: Polyclinics and GPs provide quality primary care as evidenced by high and comparable levels of CDSM competency. Redistribution of patients from public to private clinics may result in improvements in healthcare service quality.
Subject(s)
Chronic Disease , Self-Management , Adult , Cross-Sectional Studies , Humans , Personal Satisfaction , Primary Health Care , Singapore , Surveys and QuestionnairesABSTRACT
STUDY DESIGN: A cross-sectional magnetic resonance imaging (MRI)-based anatomical study. OBJECTIVES: Instrumentation of the thoracic spine may be challenging due to the unique pedicle morphology and the proximity of vital structures. As prior morphological studies have mostly been done in Caucasians, our study aims to determine the optimal pedicle screw size for transpedicular fixation in an Asian population. METHODS: A retrospective analysis of 400 patients who had undergone MRI of the thoracic spine was performed. A total of 3324 pedicles were included. Pedicle morphology was graded qualitatively based on the size of its cancellous channel, and quantitatively with the following parameters: pedicle transverse diameter, pedicle screw path length, and pedicle angle. Subgroup analysis based on gender was performed. RESULTS: Mean pedicle transverse diameter was the narrowest at the T4 (2.9 ± 1 mm) and T5 (3.1 ± 1.1 mm) level. The mean pedicle screw path length progressively increased from T1 (34 ± 4.6 mm) to T12 (47 ± 4.6 mm). The mean pedicle angle was the largest at T1 (34° ± 7.9°) and decreased caudally, to 9.4° ± 3.8° at the T12 level. Females had significantly lower mean pedicle diameter and screw path length than males at every vertebral level; however, they had a larger pedicle angle at T8 to T10. The most common size of the pedicle cancellous channel was more than 4 mm. CONCLUSION: Morphological differences in the Asian pedicle suggest that caution needs to be taken during thoracic spine instrumentation, particularly in Asian females who have significantly smaller pedicles. In such cases, the use of alternative techniques or intraoperative navigation may be useful.
ABSTRACT
The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.
