Artificial intelligence in clinical decision support

• Clinical decision support systems (CDSS) are essential tools that are used in healthcare to improve clinical decision-making and deliver better patient care
• The integration of artificial intelligence in clinical decision support has opened up several opportunities across healthcare, but especially within medical diagnostics
• The use of artificial intelligence-powered CDSS can improve diagnostic accuracy, optimize treatment plans, reduce healthcare costs, and ultimately, enhance patient safety

Recent technological advances seem to be impacting every facet of the healthcare industry, and the integration of artificial intelligence (AI) in clinical decision support is no exception. 

From improving predictive analytics and streamlining administrative tasks to enabling personalized treatment plans, AI is significantly reshaping clinical decision-support systems (CDSS) and tools.¹

In this article, we place a spotlight on the use of AI in CDSS, examining some of the key benefits that AI brings to clinical decision-making processes

Clinical decision support tools are essential tools that are used in healthcare to improve clinical decision-making and deliver better patient care.^1,2 In essence, they aim to provide clinicians with a virtual assistant, armed with the latest medical knowledge, to make the best decisions for patients.¹

By themselves, CDSS are designed to provide healthcare professionals with actionable insights, evidence-based guidance, and patient-specific recommendations delivered at the point of care.¹ In doing so, they help improve diagnostic accuracy, optimize treatment plans, enhance patient safety, and deliver value-based healthcare.¹

Since their first use in the 1970s, when Stanford University introduced a system called MYCIN, CDSS have undergone a rapid evolution.^3,4

For example, the traditional CDSS were comprised of software designed to aid clinical decision-making by matching the characteristics of a patient to a computerized knowledge base.⁴ Over time, these systems have become increasingly advanced and are now developed with a greater ability to “leverage data” and make observations that would be “otherwise unobtainable or uninterpretable by humans.”⁴

CDSS can also be broken down into different types and are often classified as either “knowledge-based” or “non-knowledge-based.”⁴

• Knowledge-based CDSS: These systems use a set of rules, known as IF-THEN statements, which are created from various sources, including medical literature, clinical practice, and patient data. The systems then check and compare patient information against these parameters before providing advice and recommendations to clinicians. As an example, if a patient’s blood pressure is particularly high (IF), a knowledge-based CDSS may then recommend a certain type of medication (THEN)

• Non-knowledge-based CDSS: These systems are a form of CDSS that uses AI and machine learning to identify patterns in vast amounts of data. Rather than following fixed rules set by existing literature like knowledge-based CDSS, non-knowledge-based CDSS use past patient records and other resources to make predictions 

While knowledge-based CDSS has played — and will continue to play — a crucial role in modern healthcare, the ongoing integration of AI into non-knowledge-based CDSS represents a significant area of excitement, with the potential to completely revolutionize the delivery of patient care.

Recent advances in AI have elevated CDSS to new heights, by integrating specific AI technologies, such as machine learning, natural language processing (NLP), and deep learning.^1,4,5 AI-powered CDSS (or non-knowledge-based CDSS) in particular is bringing unprecedented value to patient care.¹

AI-powered CDSS has made it possible to:^1,2

• Process and learn from huge amounts of data
• Identify insights from complex datasets
• Offer personalized recommendations around patient needs
• Enhance clinical decision-making
• Analyze information from electronic health records, medical notes, and research

One of the key benefits of using AI in medical diagnostics and clinical decision support is its potential to improve patient diagnoses and cut down on human error.⁵

Research has indicated, for example, that AI-driven CDSS can achieve a similar level of performance to trained dermatologists when classifying specific types of skin cancer.⁶ In the case of Sepsis, AI CDSS is bringing powerful, life-saving possibilities to emergency room and intensive-care doctors. While the integration of deep learning models into CDSS is not intended to replace the role of the physician in making a diagnosis or treatment plan, this technology offers increased confidence and is becoming more and more capable of delivering comparable results.⁶

Other research has shown that natural language processing (NLP) — a form of AI — can be integrated into CDSS to identify cancer-related concepts by analyzing clinical notes and sets of text.^1,7

AI-powered CDSS has also demonstrated potential in identifying heart failure at an earlier stage.⁸ By using what’s known as a recurrent neural network, this integration has made it possible for non-knowledge-based CDSS to detect anomalous patterns and predict clinical outcomes. This illustrates AI’s potential to not only mitigate risk, but also ensure earlier medical diagnoses.^1,8

Within the ever-evolving world of healthcare, the integration of AI and machine learning technology into CDSS opens up a number of exciting opportunities.⁵ From improved patient care to reduced healthcare costs, AI-integrated CDSS can help healthcare players make smarter, faster decisions, for safer, more personalized, and more efficient healthcare delivery.