Introducing artificial intelligence training in medical education

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI

Timeliness of antibiotics for patients with sepsis and septic shock

For many years, sepsis guidelines have focused on early administration of antibiotics. While this practice may benefit some patients, for others it might have detrimental consequences. The increasingly shortened timeframes in which administration of antibiotics is recommended, have forced physicians to sacrifice diagnostic accuracy for speed, encouraging the overuse of antibiotics. The evidence supporting this practice is based on retrospective data, with all the limitations attached, while the only randomized trial on this subject does not show a mortality benefit from early administration of antibiotics in a population of patients with sepsis as often seen in the emergency department (ED). Physicians are challenged to treat patients suspected of having sepsis within a short period of time, while the real challenge should be to identify patients who would not be harmed by withholding treatment with antibiotics until the diagnosis of infection with a bacterial origin is confirmed and the appropriateness of a course of antibiotics can be evaluated more adequately. Therefore, in the general population of patients with sepsis, taking the time to gather additional data to confirm the diagnosis should be encouraged without a specific timeframe, although physicians should be encouraged to perform an adequate work-up as soon as possible. Patients with suspected sepsis and signs of shock should immediately be treated with antibiotics, as there is no margin for error

An overview of positive cultures and clinical outcomes in septic patients: a sub-analysis of the Prehospital Antibiotics Against Sepsis (PHANTASi) trial

Background: Sepsis remains one of the most important causes of morbidity and mortality worldwide. In approximately 30-50% of cases of suspected sepsis, no pathogen is isolated, disabling the clinician to treat the patient with targeted antimicrobial therapy. Studies investigating the differences in the patient outcomes between culture-positive and culture-negative sepsis patients have only been conducted in subgroups of sepsis patients and results are ambiguous. Methods: This is a sub-analysis of the PHANTASi (Prehospital Antibiotics Against Sepsis trial), a randomized controlled trial that focused on the effect of prehospital antibiotics in sepsis patients. We evaluated the outcome of cultures from different sources and determined what the clinical implications of having a positive culture compared to negative cultures were for patient outcomes. Furthermore, we looked at the effect of antibiotics on culture outcomes. Results: 1133 patients (42.6%) with culture-positive sepsis were identified, compared to 1526 (56.4%) patients with culture-negative sepsis. 28-day mortality (RR 1.43 [95% CI 1.11-1.83]) and 90-day mortality (RR 1.41 [95% CI 1.15-1.71]) were significantly higher in culture-positive patients compared to culture-negative patients. Culture-positive sepsis was also associated with ≥ 3 organ systems affected during the sepsis episode (RR 4.27 [95% CI 2.78-6.60]). Patients who received antibiotics at home more often had negative blood cultures (85.9% vs. 78%) than those who did not (p < 0.001). Conclusions: Our results show that culture-positive sepsis is associated with a higher mortality rate and culture-positive patients more often have multiple organ systems affected during the sepsis episode

Towards Understanding the Effective Use of Antibiotics for Sepsis

BACKGROUND: The benefits of early antibiotics for sepsis have recently been questioned. Evidence for this mainly comes from observational studies. The only randomized trial on this subject, the Prehospital Antibiotics Against Sepsis (PHANTASi) trial, did not find significant mortality benefits from early antibiotics. That subgroups of patients benefit from this practice is still plausible, given the heterogeneous nature of sepsis. RESEARCH QUESTION: Do subgroups of sepsis patients experience 28-day mortality benefits from early administration of antibiotics in a prehospital setting? And what key traits drive these benefits? STUDY DESIGN AND METHODS: We used machine learning to conduct exploratory partitioning cluster analysis to identify possible subgroups of sepsis patients who may benefit from early antibiotics. We further tested the influence of several traits within these subgroups, using a logistic regression model. RESULTS: We found a significant interaction between age and benefits of early antibiotics (P = .03). When we adjusted for this interaction and several other confounders, there was a significant benefit of early antibiotic treatment (OR, 0.07; 95% CI, 0.01-0.79; P = .03). INTERPRETATION: An interaction between age and benefits of early antibiotics for sepsis has not been reported before. When validated, it can have major implications for clinical practice. This new insight into benefits of early antibiotic treatment for younger sepsis patients may enable more effective care

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slow to adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (the end-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested. Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicable to a wide variety of AI-tool implementations in clinical practice. Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and a nationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included. We performed deductive direct content analysis, using the Consolidated Framework for Implementation Research (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations for Implementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data was descriptively analyzed. Results: We performed ten individual interviews, and one focus group with five physicians. The most prominent constructs identified during the qualitative interim analyses were incorporated in the nationwide survey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation: evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change, compatibility, access to knowledge and information, and knowledge and beliefs about the intervention. Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conduct educational meetings, promote adaptability, develop educational materials, and distribute educational materials. Conclusions: The potential value of AI in healthcare is acknowledged by end-users, however, the current tension for change needs to be sparked to facilitate sustainable implementation. Strategies that should be used are: increasing the access to knowledge and information through educational meetings and materials with committed local leaders. A trial phase for end-users to test and compare AI algorithms. Lastly, algorithms should be tailored to be adaptable to the local context and existing workflows. Applying these implementation strategies will bring us one step closer to realizing the value of AI in healthcare

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slow to adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (the end-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested. Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicable to a wide variety of AI-tool implementations in clinical practice. Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and a nationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included. We performed deductive direct content analysis, using the Consolidated Framework for Implementation Research (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations for Implementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data was descriptively analyzed. Results: We performed ten individual interviews, and one focus group with five physicians. The most prominent constructs identified during the qualitative interim analyses were incorporated in the nationwide survey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation: evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change, compatibility, access to knowledge and information, and knowledge and beliefs about the intervention. Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conduct educational meetings, promote adaptability, develop educational materials, and distribute educational materials. Conclusions: The potential value of AI in healthcare is acknowledged by end-users, however, the current tension for change needs to be sparked to facilitate sustainable implementation. Strategies that should be used are: increasing the access to knowledge and information through educational meetings and materials with committed local leaders. A trial phase for end-users to test and compare AI algorithms. Lastly, algorithms should be tailored to be adaptable to the local context and existing workflows. Applying these implementation strategies will bring us one step closer to realizing the value of AI in healthcare

Outcome of Immediate Versus Early Antibiotics in Severe Sepsis and Septic Shock: A Systematic Review and Meta-analysis

Study objective: Debate exists about the mortality benefit of administering antibiotics within either 1 or 3 hours of sepsis onset. We performed this meta-analysis to analyze the effect of immediate (0 to 1 hour after onset) versus early (1 to 3 hours after onset) antibiotics on mortality in patients with severe sepsis or septic shock. Methods: This review was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Searched databases included PubMed, EMBASE, Web of Science, and Cochrane Library, as well as gray literature. Included studies were conducted with consecutive adults with severe sepsis or septic shock who received antibiotics within each period and provided mortality data. Data were extracted by 2 independent reviewers and pooled with random effects. Two authors independently assessed quality of evidence across all studies with Cochrane's Grading of Recommendations Assessment, Development and Evaluation methodology and risk of bias within each study, using the Newcastle-Ottawa Scale. Results: Thirteen studies were included: 5 prospective longitudinal and 8 retrospective cohort ones. Three studies (23%) had a high risk of bias (Newcastle-Ottawa Scale). Overall, quality of evidence across all studies (Grading of Recommendations Assessment, Development and Evaluation) was low. Pooling of data (33,863 subjects) showed no difference in mortality between patients receiving antibiotics in immediate versus early periods (odds ratio 1.09; 95% confidence interval 0.98 to 1.21). Analysis of severe sepsis studies (8,595 subjects) found higher mortality in immediate versus early periods (odds ratio 1.29; 95% confidence interval 1.09 to 1.53). Conclusion: We found no difference in mortality between immediate and early antibiotics across all patients. Although the quality of evidence across studies was low, these findings do not support a mortality benefit for immediate compared with early antibiotics across all patients with sepsis

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P <. 001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P <. 001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow