ChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice.

Artificial intelligence (AI) chatbots, such as ChatGPT, have widely invaded all domains of human life. They have the potential to transform healthcare future. However, their effective implementation hinges on healthcare workers' (HCWs) adoption and perceptions. This study aimed to evaluate HCWs usability of ChatGPT three months post-launch in Saudi Arabia using the System Usability Scale (SUS). A total of 194 HCWs participated in the survey. Forty-seven percent were satisfied with their usage, 57 % expressed moderate to high trust in its ability to generate medical decisions. 58 % expected ChatGPT would improve patients' outcomes, even though 84 % were optimistic of its potential to improve the future of healthcare practice. They expressed possible concerns like recommending harmful medical decisions and medicolegal implications. The overall mean SUS score was 64.52, equivalent to 50 % percentile rank, indicating high marginal acceptability of the system. The strongest positive predictors of high SUS scores were participants' belief in AI chatbot's benefits in medical research, self-rated familiarity with ChatGPT and self-rated computer skills proficiency. Participants' learnability and ease of use score correlated positively but weakly. On the other hand, medical students and interns had significantly high learnability scores compared to others, while ease of use scores correlated very strongly with participants' perception of positive impact of ChatGPT on the future of healthcare practice. Our findings highlight the HCWs' perceived marginal acceptance of ChatGPT at the current stage and their optimism of its potential in supporting them in future practice, especially in the research domain, in addition to humble ambition of its potential to improve patients' outcomes particularly in regard of medical decisions. On the other end, it underscores the need for ongoing efforts to build trust and address ethical and legal concerns of AI implications in healthcare. The study contributes to the growing body of literature on AI chatbots in healthcare, especially addressing its future improvement strategies and provides insights for policymakers and healthcare providers about the potential benefits and challenges of implementing them in their practice.

Predictors and Direct Cost Estimation of Long Stays in Pediatric Intensive Care Units in Saudi Arabia: A Mixed Methods Study.

BACKGROUND: Several factors influence patients' length of stay (LOS) in pediatric intensive care units (PICUs). This study explored the factors and cost associated with long LOS among pediatric patient population across various PICUs and pediatric surgical cardiac intensive care units (SCICUs). METHODS: This is a multi-center concurrent mixed-methods study that was conducted in different PICUs and SCICUs in Riyadh, Saudi Arabia. The units' leaders were qualitatively interviewed to explore the potential factors contributing to long LOSs in PICUs and SCICUs, and deductive content analysis was used. Microcosting was utilized to estimate the inpatient costs for a sample of long-stay patients (LSPs) and the potential cost savings if those patients were transferred to step-down care units (SDUs) for those who stayed ≥60 days. RESULTS: Ten (90%) leaders of PICUs and SCICUs who were invited had participated. Changing the admission criteria, patient transfer to SDUs, family engagement in the decision-making process, and adopting a national do-not-resuscitate (DNR) policy were factors contributing to short LOSs. On the other hand, administrative constraints, staff avoidance of code status decisions, lack of palliative care resources, and complex patient characteristics were factors associated with long LOSs. The mean inpatient cost per patient was SAR 3.63 million (USD 0.97 million), and the total cost for the 48 patients was SAR 172.95 million (USD 46.12 million). CONCLUSION: If the recommendation to transfer LSPs after a 60-day PICU stay to SDUs is taken, the estimated cost savings among this sample of patients alone can be as high as SAR 109.47 million (USD 29.19 million). Therefore, future studies should examine the factors contributing to long LOSs in PICUs and SCICUs as well as the effectiveness of different policies aimed at improving the resource allocation and management using more robust study designs and diverse patient population.

Reporting adverse events related to medical devices: A single center experience from a tertiary academic hospital.

Intensive care units (ICU) rely on multiple technical resources with extensive use of different medical devices, such as ventilators, vital sign monitors, infusion, and injection pumps. This study explored how ICU nurses approach adverse events related to medical devices in a single tertiary center and identify their level of awareness of the national reporting system for adverse events related to medical devices beside their source for risk information updates. Totally, 297 nurses working in the ICU at King Saud University Medical City completed a survey on medical devices and adverse events reporting and 198 reported experiencing an adverse event related to equipment failure. However, 195 nurses were unaware of an official national reporting system for reporting such events. It is important to develop a framework of safe operation of medical devices based on international standards. This reporting system should include the national patients' safety authorities, and should be anonymous, confidential, and non-punitive.

Failure Mode and Effect Analysis (FMEA) may enhance implementation of clinical practice guidelines: An experience from the Middle East.

RATIONALE, AIMS, AND OBJECTIVES: Implementation of clinical practice guidelines (CPGs) has been shown to reduce variation in practice and improve health care quality and patients' safety. There is a limited experience of CPG implementation (CPGI) in the Middle East. The CPG program in our institution was launched in 2009. The Quality Management department conducted a Failure Mode and Effect Analysis (FMEA) for further improvement of CPGI. METHODS: This is a prospective study of a qualitative/quantitative design. Our FMEA included (1) process review and recording of the steps and activities of CPGI; (2) hazard analysis by recording activity-related failure modes and their effects, identification of actions required, assigned severity, occurrence, and detection scores for each failure mode and calculated the risk priority number (RPN) by using an online interactive FMEA tool; (3) planning: RPNs were prioritized, recommendations, and further planning for new interventions were identified; and (4) monitoring: after reduction or elimination of the failure mode. The calculated RPN will be compared with subsequent analysis in post-implementation phase. RESULTS: The data were scrutinized from a feedback of quality team members using a FMEA framework to enhance the implementation of 29 adapted CPGs. The identified potential common failure modes with the highest RPN (≥ 80) included awareness/training activities, accessibility of CPGs, fewer advocates from clinical champions, and CPGs auditing. Actions included (1) organizing regular awareness activities, (2) making CPGs printed and electronic copies accessible, (3) encouraging senior practitioners to get involved in CPGI, and (4) enhancing CPGs auditing as part of the quality sustainability plan. CONCLUSION: In our experience, FMEA could be a useful tool to enhance CPGI. It helped us to identify potential barriers and prepare relevant solutions.