AI Can Accelerate Value Analysis, But It Can't Govern It
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Suzanne Smith, Solution Advisor, Lumere
Thursday, July 2, 2026
Suzanne Smith, Solution Advisor, Lumere
With increased demands on qualified personnel across clinical and supply chain functions, AI technology that can summarize complex information in seconds, spot patterns across large datasets and reduce administrative drag can be a valuable addition to the toolbox. For value analysis teams under pressure to move faster, that promise is compelling.
But let's be clear: value analysis is not a productivity function. It is a governance discipline that requires accountability, nuance, trust and consistency. In other words, AI systems can help accelerate clinical decision-making and value analysis workflows where appropriate, but they should absolutely not replace human judgment.
Value analysis decisions:
These are not low-risk recommendations. They are defensible decisions that affect care delivery.
AI can inform decision-making and play an important supporting role, but AI cannot own these decisions.
Used appropriately, AI tools can:
In short, AI can improve efficiency and visibility. It can help teams spend less time on administrative work and more time on evaluation. This is where AI clinical decision support tools can provide value.
What they cannot do is validate clinical truth. The message is clear: healthcare stakeholders should use AI in decision-making rather than rely on AI decision-making.
This distinction is particularly important when AI is used for clinical decision support. A 2025 study published in Communications Medicine found that leading language models repeatedly fabricated clinical details between 50% and 82% of the time when those details were subtly embedded in case scenarios. The models sounded confident, but they were wrong.
That distinction matters in governance. If a system cannot reliably distinguish between what is real and what is merely plausible, it cannot be the final arbiter of committee-ready insight.
The researchers also found that safeguards such as prompt engineering could reduce hallucinations but could not eliminate them entirely. In other words, artificial intelligence can support decision-making processes, but it cannot independently ensure accuracy, accountability or clinical appropriateness. Those responsibilities remain with clinicians, value analysis leaders and governance committees.
At its best, AI agents help clinical decision-makers process massive volumes of structured and unstructured data, identify emerging trends and generate predictive insights more efficiently than traditional methods. AI models powered by machine learning and natural language processing can assist with analyzing data, screening literature and synthesizing information from patient data and historical data.
These capabilities can enhance decision-making processes and improve operational efficiency, but they do not replace human judgment, critical thinking, human review or accountability.
| Function | AI Systems Can Help By… | Human Decision Makers Must… |
|---|---|---|
| Literature Review | Screening research and summarizing findings using natural language processing | Validate evidence quality and clinical relevance through human intelligence |
| Data Analysis | Analyzing historical data, patient data and emerging trends | Interpret findings within clinical and operational context using human analysis |
| Clinical Decision Support | Providing AI-generated insights and recommendations | Exercise human judgment and approve recommendations |
| Risk Assessment | Identifying patterns and predictive insights | Evaluate exceptions and unintended consequences as part of risk management and quality control processes |
| Decision Making Processes | Improving efficiency and reducing administrative burden around routine tasks | Maintain accountability, governance and decision quality |
| Strategic Decision Making | Supporting informed decision making with data-driven insights | Balance patient outcomes, cost, safety and clinician alignment |
Healthcare organizations are already using artificial intelligence and machine learning models in their clinical and business processes to support activities ranging from medical imaging interpretation, predictive analytics and inventory management to personalized treatment plans and risk assessment. These AI-powered tools can help clinical decision-makers identify patterns, analyze patient data and generate insights more efficiently. However, even as AI technology becomes more sophisticated, human expertise remains essential for interpreting results and determining the appropriate course of action.
AI's role in healthcare decision-making is not to replace human decision-makers but to support informed decision-making. By processing structured and unstructured data, AI aids clinical and operational teams in identifying patterns, surfacing relevant information and improving operational efficiency. The goal is better decision-making—not autonomous decision-making.
Strong value analysis programs require:
| Governance Requirement | How AI Supports Decision Making | Why Human Expertise Is Required |
|---|---|---|
| Evidence Review | Screens literature and summarizes findings | Validates clinical relevance |
| Product Evaluation | Analyzes data and identifies patterns; supports data management | Applies clinical judgment |
| Risk Assessment | Generates predictive insights | Evaluates real-world implications |
| Stakeholder Alignment | Improves information sharing | Builds trust and consensus |
| Final Recommendation | Provides decision support around clinical and business objectives | Maintains accountability and governance |
As healthcare organizations adopt more AI-assisted decision-making capabilities, maintaining human oversight becomes even more important. Many of the ethical issues surrounding artificial intelligence in healthcare stem from a lack of transparency, accountability and clinical validation.
Effective value analysis has always relied on data-driven decision-making. AI can help accelerate that process by analyzing data more efficiently and surfacing relevant insights, but governance ensures recommendations remain clinically appropriate, defensible and aligned with organizational goals.
The challenge is not simply accessing information. Healthcare organizations already have access to massive volumes of clinical, operational and product data. The real challenge is transforming that information into defensible recommendations. That requires evidence curation, expert review and governance structures capable of distinguishing meaningful insights from noise—a process that cannot be fully automated.
The future of value analysis is not human versus AI. It is AI-enabled, human-led governance that combines the speed of artificial intelligence with the accountability, expertise and judgment of experienced healthcare professionals.
At GHX, we believe technology should strengthen—not replace—clinical and operational expertise. Our advisory and research teams, which include clinicians, PhDs and healthcare operators, ensure that insights are not just fast, but defensible.
After all, the question is not simply, "What does the data say?" It is, and always should be, "What does this mean for patients, clinicians and outcomes?"
AI can accelerate insight, support clinical decision-making and help teams act on AI-generated insights more efficiently, but accountability remains human. Regardless of how advanced AI algorithms become, responsibility for patient outcomes and governance remains with people.
The strongest healthcare organizations do not rely on AI to make decisions; they combine data-driven decision-making with clinical expertise, evidence review and stakeholder alignment.
That philosophy is reflected in our approach to value analysis, which combines evidence-based research, clinically integrated analytics and human partnership to help organizations make informed, defensible decisions. We give decision-makers the information they need while preserving human accountability for outcomes.
Learn more about GHX Value Analysis, powered by a proprietary product taxonomy, an expertly curated research library with more than 49,000 product profiles and proactive human partnership.
Artificial intelligence in healthcare is increasingly being used to analyze large datasets, summarize clinical research, identify patterns and support operational workflows. Many healthcare organizations use AI to improve efficiency and provide decision support, but human experts remain responsible for clinical decision-making and governance.
No. While AI clinical decision support tools can provide recommendations and insights, they should not independently make clinical decisions. Effective clinical decision-making requires human judgment, accountability, patient context and consideration of organizational policies and standards.
Human oversight is critical because AI models can generate inaccurate or incomplete information. In healthcare, AI-assisted decision-making must be supported by expert review to ensure patient safety, regulatory compliance and clinical appropriateness.
Some of the most commonly discussed ethical issues of artificial intelligence in healthcare include transparency, bias, accountability, explainability and patient safety. Healthcare organizations must ensure that AI supports decision-making processes without replacing professional judgment or governance structures.
AI can help clinicians and value analysis teams process data and information more efficiently by identifying relevant research, summarizing evidence and highlighting trends. However, clinicians remain responsible for interpreting information, applying clinical expertise and making final decisions that affect patient care.
AI recommendations are generated through algorithms and data analysis. Healthcare governance involves human-led processes that establish accountability, review evidence, evaluate risks and ensure decisions align with organizational goals, patient outcomes and regulatory requirements. Governance provides the structure that makes healthcare decisions defensible and repeatable.
Healthcare organizations and their suppliers are using AI for business decision making, specifically throughout the procure-to-pay/order-to-cash cycle. By embedding AI into these workflows, provider and supplier teams can gain better understanding of what’s happening across orders, invoices, inventory and payments. AI for supply chain decision making can provide stakeholders with earlier signals, clearer context and the ability to act with confidence. Providers and suppliers can work more proactively, communicate more clearly and drive more predictable outcomes.
The future of artificial intelligence and decision-making in healthcare is likely to be collaborative rather than autonomous. AI will continue to improve efficiency, evidence review and operational insight, while clinicians, value analysis professionals and healthcare leaders retain responsibility for governance, accountability and patient outcomes.