Compare Client Satisfaction in the Public Health Posts and Outsourced Health Posts Affiliated to Qom University of Medical Sciences in 2014

Abstract Background: Client satisfaction as an important indicator to assess the quality of provided services has found a special place over the past few decades. The main purpose of this study is to compare the client satisfaction in the public health posts and outsourced health posts affiliated to Qom university of medical Sciences in 2014. Materials and Methods: This was a descriptive analytic (cross- sectional) study.The participants were 216 clients, who had referred to 10 public health posts and outsourced health posts of Qom province. Health posts were selected by cluster sampling from different urban areas and participants were selected by simple sampling methods. A researcher made questionnaire was used to measure the data on a 5-point Likert scale, which it's validity and reliability were confirmed by experts panel and Cronbach's alpha coefficient, respectively. After collection, the data were analyzed by SPSS 20, and descriptive statistical methods, Mann-Whitney test, chi-square, with 0.05 significant level. Results: Among 60 health posts, 20 health posts (33.33%) were outsourced and 40 (66.66%) were managed by the public sector. Results showed that in health centers outsourced , overall satisfaction of the child care and vaccinations and maternity care were respectively, 64.5 and 55.42 and 67.43 percent and in public health posts were respectively, 35.5 and 44.58 and 37.66 percent and this difference was significant. Client satisfaction in the public health posts of the vaccination (57.1%) compared with client satisfaction in the outsourced health posts (47.2%) was higher, also at public health posts, satisfaction of the scientific skill employees (55.1 percent) assigned to the outsourced health posts (44.9%) was even greater, that this difference was significant. Conclusion: Results of the present study showed that, there is a significant difference in satisfaction of clients in public health posts and outsourced health posts It seems necessary to pay special attention to employee training programs by managers and also considering the results of client satisfaction in performance appraisal of personnel in health care posts

Health promoting hospitals: a study on educational hospitals of Isfahan, Iran

Background: The current situation of health promotion (HP) services in hospitals of Iran is unclear. The aim of this study was to assess the status of HP in hospitals in Isfahan, Iran. Methods: This study is a cross-sectional survey in which 9 educational hospitals selected through census sampling. HP self-assessment was used for the data collection. The assessment teams formed, and evidence examined in line with the tools. Results: The results identified five categories of HP activities in the hospitals consisted: patients,staff, environmental, community, and organizational. The mean of total score of HP was 48.8(9.8). In terms of the HP standards scores, 5 hospitals (55.5%) were at the intermediate level;3 hospitals (33.3%) were at the weak level, and 1 hospital (11.1%) was at the good level.About the standards, the highest score was "information and patient interventions" standard 79.8 (13.5), and the lowest was "continuity and cooperation" standard 36.2 (10.8). Conclusion: It seems that some of the health promoting hospitals (HPS) duties carried out by hospitals. So, to improve the quality of health services, it seems useful to encourage policymakers and health service managers to create coherent policies and guidelines in HPS

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt