AIMC Topic: Decision Support Systems, Clinical

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Developing an AI-Based clinical decision support system for basal insulin titration in type 2 diabetes in primary Care: A Mixed-Methods evaluation using heuristic Analysis, user Feedback, and eye tracking.

International journal of medical informatics
BACKGROUND AND AIM: The progressive nature of type 2 diabetes often, in time, necessitates basal insulin therapy to achieve glycemic targets. However, despite standardized titration algorithms, many people remain poorly controlled after initiating in...

The role of artificial intelligence in the diagnosis, imaging, and treatment of thoracic empyema.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: The management of thoracic empyema is often complicated by diagnostic delays, recurrence, treatment failures and infections with antibiotic resistant bacteria. The emergence of artificial intelligence (AI) in healthcare, particular...

Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals' perspectives.

BMC medical ethics
BACKGROUND: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision...

Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network.

F1000Research
BACKGROUND: The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This ca...

Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study.

JMIR aging
BACKGROUND: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and deman...

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must ...

Clouds on the horizon: clinical decision support systems, the control problem, and physician-patient dialogue.

Medicine, health care, and philosophy
Artificial intelligence-based clinical decision support systems have a potential to improve clinical practice, but they may have a negative impact on the physician-patient dialogue, because of the control problem. Physician-patient dialogue depends o...

AI-enabled clinical decision support tools for mental healthcare: A product review.

Artificial intelligence in medicine
The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorize...