Mohamad-Hani Temsah

Compassion fatigue (CF) poses significant challenges to healthcare workers’ (HCWs) well-being. This study aimed to estimate the prevalence of CF and identify its predictive factors among HCWs in all regions of Saudi Arabia (SA). As such, all HCWs from different disciplines in different centers were allowed to participate, resulting in 678 participants. The study tool, distributed between October 2022 and January 2023, consisted of a questionnaire created by the authors based on the Professional Quality of Life Scale (ProQOL). The ProQOL measures the positive (compassion satisfaction [CS]) and negative (CF) effects of helping those who have suffered, noting that burnout (BO) and secondary traumatic stress (STS) are the two subscales that constitute CF. Our findings revealed that 63.9% of HCWs experienced average STS, while 57.2% reported average BO levels. HCWs in the southern and northern regions exhibited higher STS (p-value = 0.003 and 0.010, respectively). Physicians displayed higher BO levels (p-value = 0.024). Higher levels of CS were found among older HCWs (p-value = 0.001) and lower levels among those with more years of experience (p-value = 0.004). Support at work and job, life, and financial income satisfaction were significantly and positively correlated with CS and negatively correlated with BO and STS. These findings highlight the need for tailored awareness campaigns targeting HCWs, particularly physicians, to promote well-being, enhance coping skills, and foster problem-solving techniques. Keywords: burnout; compassion fatigue; compassion satisfaction; healthcare workers; professional quality of life; Saudi Arabia; secondary traumatic stress; medical trainees’ well-being.

This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs’ familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.

Background: COVID-19 is a respiratory disease that eventually became a pandemic, with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently been reported to help in early prediction and managing severe cases, which might improve outcomes. Our study aimed to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in COVID-19 patients and its effect on the outcome. Methods: We have collected retrospective data on socio-demographics, medical history, biomarkers, and disease outcomes from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID-19 in our cohort. The presence of abnormal inflammatory biomarkers (D-dimer, CRP, troponin, LDH, ferritin, and t white blood cells) was significantly associated with unstable COVID-19 disease. In addition, patients with evidence of severe respiratory disease, particularly those who required mechanical ventilation, had higher biomarkers when compared to those with stable respiratory conditions (p < 0.001). Conclusion: Identifying biomarkers predicts outcomes for COVID-19 patients and may significantly help in their management.