Today’s clinicians are navigating an overwhelming volume of patient data – lab results, imaging, clinical notes, and more – all while striving to make timely and accurate decisions for their patients. According to Thomas Moran, MD, VP and CMO at Northwestern Medicine Central DuPage Hospital, advancements in clinical AI are turning this challenge into an opportunity:
“We’re using machine learning and NLP in result management for incidental findings in imaging. For example, a patient goes to the ED for a cough, gets a chest X-ray, and a lung nodule is found. We used to struggle with who did follow-up…who was accountable? Now AI sends an in-basket message to the primary recommending follow-up. It’s pre-ordered. The ED doctor gets a message too. And if there’s no primary, we have a human results management team that reaches out to the patient. Redundancy and fail-safes are built in. Everybody’s in the know.”
This example illustrates the power of AI-enabled clinical decision support (CDS) tools: They can help streamline care coordination, reduce the burden on clinicians, and ensure that no patient falls through the cracks.
AI is already reshaping the way care is delivered – from decision support at the bedside to smarter clinical documentation, faster trial recruitment, and more. Download our eBook, AI in healthcare: 6 solutions driving efficiency and innovation, to explore the real-world ways AI is driving progress in clinical settings today.
Keep reading for an excerpt from Chapter 1: Clinical Decision Support.
Clinical decision support at-a-glance
Clinical decision support (CDS) tools and systems help healthcare professionals to make more informed decisions by providing clinical knowledge, patient data, and specific recommendations – within the clinical workflow. While CDS can improve the accuracy of diagnoses and protect patient safety, it also presents challenges including data overload, alert fatigue, and often poor integration with EHRs and other systems. Although some tools do work seamlessly and effectively, others raise the ire of burned-out clinicians who may resist the adoption of yet another tool interrupting their work.
AI in action
A deep-learning model was implemented in emergency departments in southern California to improve the early detection of sepsis, a life-threatening condition affecting millions annually. The AI model continuously analyzes over 150 patient variables, such as lab results, vital signs, and medical histories, to identify those at high risk before symptoms become readily apparent. When a patient is flagged, nursing staff receive an alert through the EHR, prompting a physician review and early intervention. The model was associated with a 17% reduction in sepsis mortality and improved adherence to treatment protocols.
How AI is being applied
AI synthesizes large volumes of data from EHRs, lab results, medical literature, imaging, and more to enhance CDS through:
Evidence-based guidance
AI-enriched tools can integrate clinical research, medical guidelines, and detailed patient histories to deliver recommendations tailored to the individual.
Predictive modeling
Behind the scenes, AI can predict patient risks and outcomes, to inform proactive interventions.
Reduction in cognitive load
AI can help clinicians, who are inundated with data, by filtering and prioritizing the most relevant information so they can focus on what matters.
Real-time insights
AI-powered CDS tools can analyze lab results, vitals, clinical notes and other patient data then deliver actionable recommendations at the point of care.
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