Clinicians face critical decisions daily, often under pressure. Clinical Decision Support Systems (CDSS) integrate patient data with medical knowledge, offering evidence-based recommendations to improve care quality and efficiency. At Mayo Clinic, CDSS, including AI for lung cancer diagnosis, enhances treatment plans, reduces unnecessary procedures, and allows doctors more time with patients.
Imagine watching a movie. The first scene: it's night, and we're inside the Emergency Room of a major city hospital. Cut. What comes next?
Probably, you would expect a young boy or girl who is rushed into the ER on a stretcher, with paramedics shouting the patient's vital signs. Soon, a doctor will have to make quick, critical decisions.
Clinicians constantly make decisions that significantly impact their patients. Regardless of how long their shift was, how tired they were, or how limited and confusing the available information might be, technological advancements now provide support through Clinical Decision Support Systems (CDSS), like the one currently used at the Mayo Clinic Hospital System.
A CDSS is an intelligent health IT system that gathers diverse patient-related inputs and combines them with clinical knowledge to create a digital patient profile with a personalised patient trajectory. This supports clinicians in evaluating the patient's health status, determining a diagnosis, and selecting a therapy. Increasingly, these systems are powered by in silico technologies such as Artifical Intelligence (AI)-based systems.
One such decision support system used in the Mayo Clinic helps clinicians identify optimal treatment plans by considering a patient's health data, medical history, drug interactions, recommendations, and contraindications. CDSS also helps physicians access the latest healthcare guidelines and best practices and supports them in monitoring and complying with these procedures.
For instance, the CDSS at Mayo Clinic includes AI capabilities to help doctors diagnose lung cancer from low-dose CT scans. This decision-support system analyses image patterns to detect potential tumours, reducing unnecessary biopsies and surgeries.
The CDSS extends healthcare providers' knowledge and expertise through predictive computer models, leading to faster and more precise diagnosis. Patients benefit by reaching the therapeutic stage faster, with less stress and possibly better outcomes. Doctors can then navigate the diagnostic process more efficiently, reducing workload and burnout. Thus sparing them more time to spend with patients to offer care and engagement, such as explaining the pathology and treatment process.
In conclusion, the future of healthcare, enhanced by computer-based predictive models, including AI, fosters bringing back the practice of medicine to empathy and human contact between doctors and patients.
Further resources:
https://www.linkedin.com/pulse/real-world-examples-ai-clinical-decision-making-/
https://www.linkedin.com/pulse/examples-clinical-decision-support-systems-application-spsoft-com/
