Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.

Objectives: The increased utilization of Artificial intelligence (AI) in healthcare changes practice and introduces ethical implications for AI adoption in medicine. We assess medical doctors' ethical stance in situations that arise in adopting an AI-enabled Clinical Decision Support System (AI-CDSS) for antibiotic prescribing decision support in a healthcare institution in Singapore. Methods: We conducted in-depth interviews with 30 doctors of varying medical specialties and designations between October 2022 and January 2023. Our interview guide was anchored on the four pillars of medical ethics. We used clinical vignettes with the following hypothetical scenarios: (1) Using an antibiotic AI-enabled CDSS's recommendations for a tourist, (2) Uncertainty about the AI-CDSS's recommendation of a narrow-spectrum antibiotic vs. concerns about antimicrobial resistance, (3) Patient refusing the "best treatment" recommended by the AI-CDSS, (4) Data breach. Results: More than half of the participants only realized that the AI-enabled CDSS could have misrepresented non-local populations after being probed to think about the AI-CDSS's data source. Regarding prescribing a broad- or narrow-spectrum antibiotic, most participants preferred to exercise their clinical judgment over the AI-enabled CDSS's recommendations in their patients' best interest. Two-thirds of participants prioritized beneficence over patient autonomy by convincing patients who refused the best practice treatment to accept it. Many were unaware of the implications of data breaches. Conclusion: The current position on the legal liability concerning the use of AI-enabled CDSS is unclear in relation to doctors, hospitals and CDSS providers. Having a comprehensive ethical legal and regulatory framework, perceived organizational support, and adequate knowledge of AI and ethics are essential for successfully implementing AI in healthcare.

Applying Andersen's healthcare utilization model to assess factors influencing patients' expectations for diagnostic tests at emergency department visits during the COVID-19 pandemic.

Background: The uncertainties surrounding the COVID-19 pandemic led to a surge in non-urgent emergency department (ED) attendance among people presenting with upper respiratory tract infection (URTI) symptoms. These non-urgent visits, often manageable in primary care, exacerbated ED overcrowding, which could compromise the quality of ED services. Understanding patients' expectations and the reasons for these ED visits is imperative to mitigate the problem of ED overcrowding. Hence, we assessed the factors influencing patients' expectations for diagnostic tests during their ED visits for uncomplicated URTI during different phases of the pandemic. Methods: We conducted a cross-sectional study on adults with URTI symptoms seeking care at four public EDs in Singapore between March 2021 and March 2022. We segmented the study period into three COVID-19 pandemic phases-containment, transition, and mitigation. The outcome variables are whether patients expected (1) a COVID-19-specific diagnostic test, (2) a non-COVID-19-specific diagnostic test, (3) both COVID-19-specific and non-COVID-19-specific diagnostic tests, or (4) no diagnostic test. We built a multinomial regression model with backward stepwise selection and classified the findings according to Andersen's healthcare utilization model. Results: The mean age of participants was 34.5 (12.7) years. Factors (adjusted odds ratio [95% confidence interval]) influencing expectations for a COVID-19-specific diagnostic test in the ED include younger age {21-40 years: (2.98 [1.04-8.55])}, no prior clinical consultation (2.10 [1.13-3.89]), adherence to employer's health policy (3.70 [1.79-7.67]), perceived non-severity of illness (2.50 [1.39-4.55]), being worried about contracting COVID-19 (2.29 [1.11-4.69]), and during the transition phase of the pandemic (2.29 [1.15-4.56]). Being non-employed influenced the expectation for non-COVID-19-specific diagnostic tests (3.83 [1.26-11.66]). Factors influencing expectations for both COVID-19-specific and non-COVID-19-specific tests include younger age {21-40 years: (3.61 [1.26-10.38]); 41-60 years: (4.49 [1.43-14.13])}, adherence to employer's health policy (2.94 [1.41-6.14]), being worried about contracting COVID-19 (2.95 [1.45- 5.99]), and during the transition (2.03 [1.02-4.06]) and mitigation (2.02 [1.03-3.97]) phases of the pandemic. Conclusion: Patients' expectations for diagnostic tests during ED visits for uncomplicated URTI were dynamic across the COVID-19 pandemic phases. Expectations for COVID-19-specific diagnostic tests for ED visits for uncomplicated URTI were higher among younger individuals and those worried about contracting COVID-19 during the COVID-19 pandemic. Future studies are required to enhance public communications on the availability of diagnostic services in primary care and public education on self-management of emerging infectious diseases such as COVID-19.