Today’s physicians and nurses are being asked to provide more care and process more information than ever before and need support. The time has come to realize the potential of clinical decision support systems and tools.

The era of AI-powered predictive CDSS is here
With the dramatic gains made in artificial intelligence, deep learning, analytics and even highly flexible platforms that enable non-technical users to create powerful machine learning applications, predictive CDS is now not only a reality but one that continues to grow even more effective with use as algorithms are refined and the data that informs them grows in volume, scope and specificity.
Such systems are light years ahead of the standalone systems they replace. Whereas most legacy CDSS rely on established knowledge bases and defined, linear and often rigid rules to lookup recommended treatments for specific conditions, drug and allergy indications and other important factors in patient care, today’s AI-powered systems are predictive and proactive.

Tightly integrated with the electronic health record (EHR), these systems ensure complete access not only to the individual patient’s existing information, but also additions to it from external diagnostic tests or even insights from experts and specialists consulted from across the globe. Most importantly, these next-gen CDS tools provide optimized workflow alerts and actionable insights physicians, nurses and other clinicians can use that reflect detailed analysis of evidence-based clinical information.

The use cases associated with these systems are many and varied. Some of many examples include:

● Diagnostic Imaging: Incorporated within the CDSS, AI-powered imaging analysis can help radiologists detect abnormalities that require treatments faster and more accurately, particularly as imaging technology results in scans that are increasingly complex, multi-layered and more difficult for the human eye to discern. The technology shows great promise, with a researcher at Tulane University recently finding that AI can accurately identify and diagnose colorectal cancer as well or better than pathologists.

● Chronic Disease Management: Predictive CDS tools help identify patients who are at high risk of adverse events as a result of comorbidities, while also providing the recommendations for an intelligent and evidence-based intervention. And because the system is integrated directly with the EHR and appropriate external stakeholders – for example pharmacies and labs – clinicians can also be alerted when patients fail to follow recommended treatment plans. Notably, such systems can also be integrated with feeds that deliver information on social determinants of health and other data to influence patients’ outcomes as well as value-based care and other risk-adjusted care models.

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