By: Anatoly Postilnik, First Line Software

Artificial intelligence (AI) for healthcare, while set to change the future of direct patient care, faces a path that is full of complexities. No longer a science fiction trope, the technology can empower medicine in numerous ways. One of the most obvious applications is clinical decision support (CDS), with CDS systems providing clinicians and staff timely information that assists with decisions at the point of care. This information is available in various forms, including actionable alerts, reminders, and diagnostic support.

But while CDS’are built into every modern EHR today and play an essential role in a provider’s daily workflow, these systems suffer from numerous deficiencies. They generate meaningless alerts contributing to provider burnout. The information they supply is frequently too general or not useful within the unique context of a given patient. Important factors – such as comorbidities, mental states and behavioral indicators– may not be taken in consideration, rendering CDS recommendations ineffective or even dangerous.
It is therefore useful to take a look at the underlying technology that powers CDS systems today to understand how and why some of these obstacles occur, and how machine learning algorithms stand to improve and expand their capabilities.

Utilizing clinical decision support
Most CDS systems are based on clinical guidelines, evidence-based research, and best practices, which are translated into rules that guide them in how to respond to different circumstances and scenarios. Various activities can trigger these rules, from a clinician opening a patient chart in an EHR system to a nurse administering medication.

The library of rules coded into a CDS system represents the clinical knowledge that defines alerts and recommendations produced by this system. This library is maintained by teams of trained medical informaticians who continuously change and update the rules in response to new research, changing guidelines, and best practices.

But even this process comes with its share of complexities and deficiencies. For instance, translating guidelines, best practices, and information extracted from research publications into rules is a massive manual undertaking. Team members must stay informed about the latest changes in relevant information and update the rules accordingly. The number of rules inevitably grows over time as additional research is done and greater knowledge is gained. So, the logic in these systems becomes increasingly complicated, leading to potential conflicts of logic and more errors.

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