Hospital Practices and Research

Abstract Background: Different studies have shown that despite the expanding number of antifungal drugs, the death rate caused by Candida species has increased during the recent decades due to drug resistance occurrence.
Objectives: The present study aims to identify molecular structure and evaluate drug susceptibility in Candida species isolated from bronchoalveolar lavage fluid in patients diagnosed with hematological malignancies.
Methods: In this cross-sectional study, 54 clinical specimens were taken from the bronchoalveolar lavage of patients. The suspected colonies were investigated by microscopic examination and subsequent passages were evaluated according to standard operating procedures and specification of the type of colony color prescribed by CHROMagar to isolate the yeast. The sequencing method (ITS1, ITS4) was used to approve Candida species. Finally, susceptibility test was carried out according to M27S-3 and M38-A2 micro-dilution methods.
Results: Among 54 samples investigated with culture and PCR methods, 33 Candida species were identified in patients with hematological malignancies. Candida albicans (75.7%) was the most common fungal isolate. Results of drug susceptibility tests showed that the isolated C. albicans (n = 2), C. glabrata (n = 1), and C. tropicalis (n = 1) from patients with hematological malignancies were resistant to fluconazole. The present study showed that the prevalence of C. albicans was higher than other fungal species among patients with hematological malignancies.
Conclusion: Candida species are more susceptible to voriconazole, amphotericin B and Caspofungin. Therefore, identification of candida species along with their antifungal susceptibility pattern can help clinicians to better treat patients.

Abstract Background: Smart hospitals today use Artificial Intelligence to improve the quality of their services. In this sense, optimizing the patient's electronic medical record is one of the most significant issues that these hospitals face.
Objectives: This study aimed to determine the role of AI in patient electronic records in a smart hospital.
Methods: This study was a systematic review, with keywords searched in PubMed, Scopus, Google Scholar, and SID databases. In Persian and English, the keywords were artificial intelligence algorithms, electronic medical records, service quality, and hospital. The inclusion criteria included publication in Persian or English, full-text papers, current publications, and a focus on the use of AI in electronic medical records. Finally, about 57 papers related to the investigation were picked.
Results: After reviewing previous related studies, it was discovered that AI can play a role in various aspects of electronic patient records, such as disease diagnosis, predicting relapse and recovery periods, improving treatment accuracy and reducing medical errors, digital care, and decision-support systems. This can result in a 20-30% improvement in resource planning, a 30% decrease in wait times, better resource use, and more accurate predictions.
Conclusion: Leveraging AI in electronic patient records is critical for maximizing benefits while minimizing hazards. Despite the limitations, AI has the potential to become a critical tool for smart hospitals in improving healthcare delivery and efficiency. Accordingly, healthcare leaders that incorporate AI algorithms into their systems can give more effective and up-to-date care to their patients.