AI in clinical decision support: A game changer for healthcare?

The large language models (LLMs) behind generative AI tools are capable of providing detailed answers that align with search intent, allowing consumers to ask more complex, conversational questions as part of their search queries. This provides a more convenient way to access the specific, relevant information they are seeking. 

In a rapidly evolving, digital-first world that demands greater convenience, healthcare systems are actively seeking transformative solutions to optimize workflows, enhance clinician capacity, mitigate burnout, and ultimately reclaim valuable time for quality patient care. 

It’s therefore not surprising that the American Medical Association (AMA) reports a substantial increase in physicians using AI in practice, with growing recognition of the benefits.² Their reported use of AI includes support with documentation and summarization of notes, among other tasks. But what about using AI to ease the administrative burden of searching for clinical evidence? Can it bring the same convenience to clinical decision-making that we expect in our everyday lives?  

Navigating clinical evidence: from keyword search to AI-powered answers 

Clinicians face complex decisions daily. The exponential growth of published medical evidence and its associated complexities necessitate robust support systems. This is why clinicians rely on trusted clinical decision support systems for expertly curated evidence-based information, instead of having to source answers from the world’s medical literature themselves. 

These traditional clinical decision support systems often rely on keyword search to help clinicians find the information they need within the vast repository of content. This approach inherently demands a precise alignment between the user’s search intent and the specific terminology within the knowledge base. Clinicians must thus possess a pre-existing understanding of how evidence is phrased and presented to formulate effective keyword combinations. 

The integration of artificial intelligence into that search experience allows the clinician to search in a much more natural and intuitive way. 

Practical application: How is AI used in clinical decision support systems? 

Imagine a clinical pharmacist is conducting a medication review for an 80-year-old patient with renal failure, and wants to understand dosing for the drug Zosyn. Instead of crafting specific keyword combinations, the pharmacist could directly input a natural language query such as ‘piptaz dosing for 80 yo patient with rf’ into an AI-powered search within the same trusted clinical decision support database. This advanced capability quickly delivers the relevant context-specific information tailored for the patient’s demographic. 

The implications for clinical decision-making are impressive. In this scenario, the pharmacist gets faster access to the specific evidence to support their treatment recommendations for that patient, saving precious time searching for information, which can be spent on patient care. 

Discover more on the rise of artificial intelligence in pharmacy practice.  

What are the benefits of artificial intelligence in clinical decision support? 

From operational efficiency, to healthcare system agility and clinical excellence, the integration of AI into clinical decision support systems offers many advantages: 

• Faster, tailored responses: Using AI-powered search in a clinical decision support tool reduces time spent on manual information retrieval by unlocking concise, context-specific summaries from comprehensive datasets such as drug monographs or toxicology information.  

• Streamlined decision-making: By reducing search time, it enhances clinical workflow efficiency and helps reduce the cognitive load, allowing clinicians to focus on informed decision-making, and patient-centered care delivery.  

• Agile care delivery: It’s not just pharmacists that benefit. When adopted by clinicians across an entire healthcare system, this streamlining of clinical workflows translates into enhanced organizational capacity, fostering a more agile care delivery model. 

• Accuracy and patient safety: when AI-driven answers are sourced within an expertly curated, clinically validated evidence-base, they become part of the robust mechanism to support reliable, safe, informed decisions, helping reduce the potential for medication errors. 

• Upfront transparency and traceability: Advanced clinical decision support tools that accompany AI-powered answers with full citations and hyperlinks to the supporting evidence allows clinicians to easily track data sources, streamlining fact-checking for confident decisions. 

Are all AI-powered clinical decision support systems created equal? 

As healthcare organizations navigate the evolving landscape of artificial intelligence, a critical question emerges: how do we effectively evaluate AI-powered clinical decision support systems (AI-CDSS)? Not all systems are engineered to the same rigorous standards. Here are five key considerations for assessing the efficacy and trustworthiness of these tools:  

1. Data quality and provenance: The foundation of trust

The core of any effective AI system is the data. The intelligence and reliability of an AI-CDSS are directly proportional to the quality of the data it processes. For clinical application, this means the underlying data must be trusted, evidence-based, and critically, the most current available. How is the system’s clinical knowledge base maintained and updated? Ensuring robust data provenance is paramount, as it forms the bedrock upon which clinical decisions are informed. 

2. Explainability and algorithmic transparency: Demystifying recommendations

A critical aspect of clinician adoption is understanding how an AI-CDSS arrives at its recommendations. While advanced AI models can achieve remarkable accuracy, their decision-making processes often remain opaque, presenting a “black box” challenge when it comes to interpretability of the reasoning behind AI outputs. Clinicians require not only the what but also the why behind a clinical answer to build trust and integrate it effectively into practice. AI-powered solutions must provide clear reasoning, cite source materials, and offer direct hyperlinks to underlying evidence, to foster confidence and informed clinical decision-making. 

3. Rigorous clinical validation: Ensuring safety and efficacy

The implementation of AI-CDSS mandates a stringent focus on clinical validation. This process should involve rigorous, end-to-end evaluation to minimize risks and maximize accuracy at every stage of deployment. Crucially, validation is not a singular event; AI tools operate within dynamic clinical environments. An iterative validation cycle is essential to ensure long-term accuracy, adapt to emerging data and user feedback, and continuously prioritize patient safety. 

4. Contextual understanding and intuitive interaction: Designed for the clinician

An effective AI-CDSS must be purpose-built to understand and respond to clinician intent. This involves more than just keyword matching; it requires intelligent query processing that comprehends clinical context, common healthcare abbreviations, and natural language. Can healthcare providers ask questions their way? The system’s ability to intuitively interpret complex queries significantly enhances usability and efficiency, particularly in specialized domains like toxicology where nuanced search is critical. 

5. Strategic partnership and future vision: Evolving together

Beyond immediate functionality, evaluating an AI-CDSS involves considering the vendor’s long-term vision and commitment. Is the solution aligned with your organization’s strategic goals and future value propositions? A true AI-CDSS provider should be viewed as a strategic partner, capable of evolving the technology to meet future challenges, integrating new data sources, and adapting to the ever-changing landscape of clinical practice. This ensures sustained relevance and maximal return on investment. 

Understanding the AI technologies behind clinical decision support systems 

Behind the impressive capabilities of AI-powered clinical decision support systems lie sophisticated technological engines. They are the culmination of decades of computer science research, now applied to curated, evidence-based clinical datasets to empower healthcare decisions. Understanding these core technologies helps demystify how AI-CDSS actually operates. 

Advanced technologies like large language models (LLMs) and machine learning are applied to the search functionality within clinical decision support (CDS) systems to help optimize the clinical workflow.  

“Traditional keywords searches can be limiting,” explains Brendan Bull, Principal Data Scientist at Merative. “They often require users to know the exact terminology within the source material.”

“AI can improve information retrieval because it can better map natural human thoughts and questions to the information in the underlying clinical decision support. This significantly reduces the cognitive load on clinicians because they don’t have to translate their questions into a form that the system can understand.”  

- Brendan Bull, Principal Data Scientist at Merative

Natural language processing enables more intuitive interactions, allowing clinicians to ask real-world clinical questions naturally. These deep learning models, powered by neural networks, enhance the “findability” of specific information needed to support clinical practice, ensuring that clinicians can access critical evidence exactly when it’s needed. 

Explore a practical guide to AI technologies in clinical decision support.  

Further considerations for healthcare providers 

While the benefits of AI technologies in clinical decision support are clear, healthcare systems must evaluate AI tools carefully to ensure patient safety and patient outcomes remain the top priority. 

When AI-powered search is applied to a database of curated medical evidence, it’s important to ensure AI algorithms are properly validated to maintain accuracy and reliability. Occasionally, outputs may vary if tools are not designed for purpose. By continuously monitoring and refining AI methodologies, we can enhance their effectiveness in clinical settings. 

Furthermore, if AI models are applied to clinical decision support systems that are integrated within electronic health records (EHRs), additional considerations for data security and data privacy for health information are paramount. To maintain public trust and regulatory compliance, healthcare systems must protect sensitive patient data and establish clear, robust guidelines for AI implementation that address these concerns and uphold ethical standards in clinical practice. Health informatics experts should be included in healthcare provider governance committees, to ensure ethical and safe use of AI technology.  

The human element in AI-powered clinical decision-making 

With the rapid advancement in AI technologies, we must be clear that the human element of patient care is essential. The critical foundation of clinical expertise, specialized training, and human judgement is needed to fully address a patient’s unique needs. The integration of AI in clinical decision support can enhance and optimize the clinician workflow, but it’s the healthcare professionals that make the ultimate decisions on treatment plans, patient interventions and overall care delivery. 

The verdict: AI tools are setting a new paradigm for clinical decision-making 

The use of artificial intelligence is rapidly becoming the new standard in everyday work, saving time and enhancing efficiency across numerous sectors. It is only logical that its integration into the clinical decision support workflow would also become the new standard, setting a new benchmark for excellence in healthcare delivery. 

The need for such innovation is amplified by mounting pressures from ongoing workforce challenges, public health issues like medicine shortages, and rising patient expectations. In this healthcare landscape, it has become critical to optimize provider workflow efficiency, with a focus on patient outcomes, accelerating evidence-based decision-making in the care setting. 

Realizing this potential requires a cautious approach. Ongoing validation is critical to ensure these tools are both safe and effective, while strong partnerships between all stakeholders, including healthcare providers, clinical users and technology developers, are essential for navigating the complexities of integrating AI into clinical decision support. 

Ultimately, AI is not a replacement for human expertise in clinical decision-making, but a powerful tool to augment it. The question is no longer if AI will transform clinical practice, but how effectively healthcare systems can implement and adapt to these technologies, harnessing AI to create a new paradigm of patient care. 

Watch the webinar

Watch this 15-minute webinar for expert insights from both a seasoned clinician and a data scientist perspective, examining how AI can unlock greater value from the rapidly growing universe of clinical evidence. The discussion explores considerations around human judgement, content sources, clinical validation and patient safety.