By Gavin McNatt, Pharm.D., Clinical Consultant, Micromedex by Merative
LinkedIn: Gavin McNatt, Pharm.D.
LinkedIn: Merative
Artificial intelligence is transforming how clinicians access and process medical information
According to 2024 data from the American Medical Association, more physicians are using AI and recognize its benefits for documentation support and visit summaries, among other tasks. The research found nearly 66% of physicians surveyed now use AI, up from 38% in the 2023 report.
Clinicians rely on clinical decision support systems for evidence-based information and expertly curated data, often using keyword searches to simplify the process. That means clinicians, including doctors, nurses, and pharmacists, must know the phrasing and terminology used within the content to develop actionable keyword combinations.
AI-powered search within clinical decision support systems enables healthcare providers to ask natural language questions and receive context-specific answers from a trusted evidence base.
The benefits are compelling. Pharmacists can find accurate dosing information for complex patient cases. Physicians can access relevant treatment guidelines tailored for the patient’s demographic. Healthcare systems can improve workflow efficiency and reduce clinicians’ cognitive burden.
But not all AI-powered search clinical decision support systems are created equal. As healthcare organizations evaluate AI, or any emerging technology, they must carefully consider many factors to ensure continued delivery of high-quality care.
Here are 6 factors every organization should consider when evaluating AI-powered search in clinical decision support:
1. Data quality and integrity
Data accuracy, consistency and validity define data integrity. The reliability of AI–powered search in a clinical decision support system depends on the quality of its underlying data. For clinical applications, this means the knowledge base must contain trusted, evidence-based information that’s continuously updated with the most current available research.
Healthcare organizations should ask: How is the system’s clinical knowledge base maintained? What processes ensure data accuracy? The most effective AI tools draw from expertly curated, clinically validated evidence rather than unfiltered medical literature or general internet sources.
2. Transparency
Clinicians need to understand how an AI-powered search tool arrives at its recommendations. While advanced AI models can achieve remarkable accuracy, their decision-making processes aren’t always clear, creating a “black box” challenge for clinical interpretation.
Effective AI-powered solutions provide clear reasoning, cite source materials and offer direct hyperlinks to underlying evidence. This transparency enables clinicians to verify recommendations and build confidence in AI-generated answers, which is essential for safe integration into practice.
3. Clinical validation and feedback
Implementation of AI in clinical decision support requires stringent validation processes. This involves comprehensive, end-to-end evaluation to minimize risks and maximize accuracy at every deployment stage.
Validation isn’t a one-time event. AI tools operate within dynamic clinical environments, so continuous evaluation cycles are essential to ensure long-term accuracy, adapt to emerging data and user feedback, and maintain patient safety standards.
Effective clinical decision support systems with AI include mechanisms for continuous performance monitoring and user feedback collection. This enables improvements and ensures the system continues to meet clinical needs. Organizations should assess whether AI tools provide opportunities for feedback and insights on usage. These metrics help demonstrate value and identify areas for optimization.
4. Designed for the clinician
Purpose-built, AI-powered clinical decision support systems must understand and respond to clinician intent. This capability goes beyond keyword matching, it requires intelligent query processing that comprehends clinical context, healthcare abbreviations, and natural language.
For example, a clinical pharmacist is conducting a medication review for a 75-year-old patient with renal failure and needs the correct dosing for a specific drug. The AI-powered search lets the pharmacist input a natural language query, such as: “metformin dosing for 75-yo patient with rf.” The pharmacist then receives the relevant information tailored to that patient’s context.
The outcomes delivered by AI-powered clinical decision support benefit both the clinician and the patient. With faster access to the necessary evidence for treatment recommendations, the provider can use that extra time for patient care.
The system’s ability to intuitively interpret complex queries significantly enhances usability, particularly in specialized domains like toxicology, where nuanced search capabilities are critical.
5. Return on investment
Healthcare organizations must evaluate the financial impact of implementing clinical decision support tools with AI features. While there may be upfront costs, the long-term benefits—including time savings, improved efficiency, and a focus on patient outcomes, should justify the investment.
Consider both direct costs (licensing, implementation, training) and indirect benefits (reduced search time, improved workflow efficiency, enhanced clinical decision-making). A clear understanding of total cost of ownership helps organizations make informed decisions.
6. Workflow compatibility
Clinical decision support systems that use AI should integrate seamlessly with existing workflows. Busy clinicians are far more likely to adopt technology that is embedded in their workflow and is intuitive to use.
Systems that require extensive workarounds or disrupt workflows may face adoption challenges. The most successful implementations enhance existing processes rather than replacing them entirely.
Building trust through evidence and transparency
The future of AI in clinical decision support depends on building and maintaining trust through rigorous validation, transparent processes, and clear evidence of clinician benefit. Healthcare organizations that carefully evaluate AI tools using these criteria will be better positioned to harness the technology’s potential while maintaining the highest standards of patient care.
AI represents a powerful opportunity to enhance clinical decision-making, but realizing this potential requires thoughtful implementation. With a pragmatic approach, healthcare systems can successfully integrate AI tools that truly support clinicians’ work and help them focus on patient outcomes.