A data-driven framework for clinical decision support applied to pneumonia management

Clinical challenge of diagnosing non-ventilator hospital-acquired pneumonia and identifying causative pathogens: a narrative review

Non-ventilated hospital-acquired pneumonia (NV-HAP) is associated with a significant healthcare burden, arising from high incidence and associated morbidity and mortality. However, accurate identification of cases remains challenging. At present, there is no gold-standard test for the diagnosis of NV-HAP, requiring instead the blending of non-specific signs and investigations. Causative organisms are only identified in a minority of cases. This has significant implications for surveillance, patient outcomes and antimicrobial stewardship. Much of the existing research in HAP has been conducted among ventilated patients. The paucity of dedicated NV-HAP research means that conclusions regarding diagnostic methods, pathology and interventions must largely be extrapolated from work in other settings. Progress is also limited by the lack of a widely agreed definition for NV-HAP. The diagnosis of NV-HAP has large scope for improvement. Consensus regarding a case definition will allow meaningful research to improve understanding of its aetiology and the heterogeneity of outcomes experienced by patients. There is potential to optimize the role of imaging and to incorporate novel techniques to identify likely causative pathogens. This would facilitate both antimicrobial stewardship and surveillance of an important healthcare-associated infection. This narrative review considers the utility of existing methods to diagnose NV-HAP, with a focus on the significance and challenge of identifying pathogens. It discusses the limitations in current techniques, and explores the potential of emergent molecular techniques to improve microbiological diagnosis and outcomes for patients.

Artificial intelligence for the optimal management of community-acquired pneumonia

PURPOSE OF REVIEW

This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes.

RECENT FINDINGS

Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP.

SUMMARY

The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.