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INTRODUCTION: ChatGPT (OpenAI, San Francisco, CA, USA) is a novel artificial intelligence (AI) application that is used by millions of people, and the numbers are growing by the day. Because it has the potential to be a source of patient information, the study aimed to evaluate the ability of ChatGPT to answer frequently asked questions (FAQs) about asthma with consistent reliability, acceptability, and easy readability. METHODS: We collected 30 FAQs about asthma from the Global Initiative for Asthma website. ChatGPT was asked each question twice, by two different users, to assess for consistency. The responses were evaluated by five board-certified internal medicine physicians for reliability and acceptability. The consistency of responses was determined by the differences in evaluation between the two answers to the same question. The readability of all responses was measured using the Flesch Reading Ease Scale (FRES), the Flesch-Kincaid Grade Level (FKGL), and the Simple Measure of Gobbledygook (SMOG). RESULTS: Sixty responses were collected for evaluation. Fifty-six (93.33%) of the responses were of good reliability. The average rating of the responses was 3.65 out of 4 total points. 78.3% (n=47) of the responses were found acceptable by the evaluators to be the only answer for an asthmatic patient. Only two (6.67%) of the 30 questions had inconsistent answers. The average readability of all responses was determined to be 33.50±14.37 on the FRES, 12.79±2.89 on the FKGL, and 13.47±2.38 on the SMOG. CONCLUSION: Compared to online websites, we found that ChatGPT can be a reliable and acceptable source of information for asthma patients in terms of information quality. However, all responses were of difficult readability, and none followed the recommended readability levels. Therefore, the readability of this AI application requires improvement to be more suitable for patients.
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The economic impact of the COVID-19 pandemic on global health systems is a major concern. To plan and allocate resources to treat COVID-19 patients and provide insights into the financial sustainability of healthcare systems in fighting the future pandemic, measuring the costs to treat COVID-19 patients is deemed necessary. As such, we conducted a retrospective, real-world observational study to measure the direct medical cost of treating COVID-19 patients at a tertiary care hospital in Saudi Arabia. The analysis was conducted using primary data and a mixed methodology of micro and macro-costing. Between July 2020 and July 2021, 287 patients with confirmed COVID-19 were admitted and their data were analyzed. COVID-19 infection was confirmed by RT-PCR or serologic tests in all the included patients. There were 60 cases of mild to moderate disease, 148 cases of severe disease, and 79 critically ill patients. The cost per case for mild to moderate disease, severe disease, and critically ill was 2003 USD, 14,545 USD, and 20,188 USD, respectively. There was a statistically significant difference in the cost between patients with comorbidities and patients without comorbidities (P-value 0.008). Across patients with and without comorbidities, there was a significant difference in the cost of the bed, laboratory work, treatment medications, and non-pharmaceutical equipment. The cost of treating COVID-19 patients is considered a burden for many countries. More studies from different private and governmental hospitals are needed to compare different study findings for better preparation for the current COVID-19 as well as future pandemics.