Background length of stay (LOS) is the time between hospital admission and discharge. LOS has an impact on hospital management and hospital care functions. Methods A descriptive, retrospective study was designed on about 27,500 inpatients between March 2019 and 2020. Required data were collected from six wards (CCU, ICU, NICU, General, Maternity, and Women) in a teaching hospital. Clinical data such as demographic characteristics (age, sex), type of ward, and duration of hospital stay were analyzed by the R-studio program. Violin plots, bar charts, mosaic plots, and tree-based models were used to demonstrate the results. Results The mean age of the population was 40.8 ± 19.2 years. The LOS of the study population was 2.43 ± 4.13 days. About 60% of patients were discharged after staying one day in the hospital. After staying one day in the hospital, 67% of women were discharged. However, 23% of men were discharged within this time frame. The majority of LOS in the CCU, ICU, and NICU ranged from 5 to 9 days.; In contrast, LOS was one day in General, Maternity, and Woman wards. Due to the tree plot, there was a different LOS pattern between Maternity-Women and the CCU-General-ICU-NICU wards group. Conclusion We observed that patients with more severe diseases hospitalized in critical care wards had a longer LOS than those not admitted to critical care wards. The older patient had longer hospital LOS than the younger. By excluding Maternity and Woman wards, LOS in the hospital was comparable between males and females and demonstrated a similar pattern.

Objectives: Several factors influence medication patterns. The purpose of this study was to look into the role of social determinants in the use of prescribed and non-prescribed medications in a population-based setting of people over 18 in a southern metropolis of Iran (Shiraz) for 2 years. Study Design: Prospective population-based cross-sectional. Methods: This descriptive and cross-sectional survey was done in 2018-2020. A total of 1016 participants were randomly selected based on their postal codes recruited to the study. The demographic characteristics (age, sex, and education), social profiles (insurance, supplementary insurance, health status, and daily exercise plan), and outpatient visits (family/general physician or specialist/subspecialist) were recorded by gathering sheets. Descriptive analyses and multinomial logistic analyses were carried out using SPSS software. Results: The medication use pattern was classified into three categories: non-prescribed type I, non-prescribed type II, and prescribed. The mean age of participants was 45.54 ± 15.82 years. The results indicated that most of them took their medication without prescription (non-prescribed type II). However, people with insurance and referred to a family physician commonly used the prescribed medications. This study also found that patients who visited a family doctor or a general practitioner used fewer prescribed drugs than those who visited a specialist. Conclusion: This study describes social determinants as additional effective factors in health services that influence the use of prescribed and non-prescribed medications in Shiraz. These evidence-based findings can help policymakers to plan the best programs.