AI in Clinical Practice: Why Intelligence Must Be Integrated into Care
Summary:
Although artificial intelligence in medicine has advanced rapidly, a global survey reveals that only 16% of healthcare professionals use AI tools to support clinical decision-making. This finding suggests that the greatest challenge facing AI today is no longer algorithm accuracy, but usability at the point of care.
The fourth article in the editorial series “AI in Healthcare: Credibility, Safety, and Impact on Clinical Practice” explores the pillar of clinical workflow integration. Discover why clinical intelligence must be “invisibly useful,” removing operational barriers and delivering actionable insights at the exact moment decisions are made, without competing for the time and attention dedicated to patient care.
Key Topics Covered:
- The challenge of low AI adoption in clinical practice
- The realities of the hospital environment, where clinical decisions are made under far-from-ideal conditions
- Friction barriers and cognitive burden
- External AI vs. workflow-integrated AI
- The value of AI in time-sensitive situations
- Epimed Solutions’ practical approach
Content:
Artificial intelligence (AI) has become part of everyday life and is no longer a distant promise. Today, models exist with remarkable capabilities for diagnostic triage, prediction of clinical events, identification of risk patterns, and, increasingly, as larger volumes of data become available, support for clinical decision-making. Yet the practical adoption of AI in routine care remains limited.
A recent global survey, Clinician of the Future 2025, found that only 16% of healthcare professionals currently use AI tools to support clinical decisions. This figure is revealing: despite the technological advances of recent years, adoption remains slow and often fragmented within the care environment.
This helps explain one of the most important challenges facing AI in healthcare today. Technology itself is no longer the primary barrier. It has evolved rapidly and become increasingly accessible. The central challenge now lies in how intelligence reaches the end user and becomes embedded in clinical practice.
Everyday clinical decisions are not made in ideal circumstances, where there is ample time to research information or explore new technologies. They occur during multidisciplinary discussions, in response to sudden changes in a patient’s condition, while interpreting diagnostic tests, and amid the constant interruptions of clinical routines. In these contexts, speed, context and workflow integration become just as important as accuracy itself.
For this reason, when we say that AI must be integrated into care, we are no longer talking only about system integration, data quality or data privacy, although these remain important considerations. The primary challenge is ensuring that AI is available precisely at the moment of clinical decision-making, integrated into the care workflow and accessible without requiring professionals to interrupt their work.
This distinction may seem subtle, but it represents one of the greatest barriers to AI adoption in clinical practice. Many solutions already achieve satisfactory technical performance. However, they still present important limitations in day-to-day use:
- Additional navigation requirements
- Multiple login credentials
- Interruptions to established workflows
- Information retrieval outside the immediate clinical context
In complex healthcare environments, these barriers have a significant impact. Every additional step introduced into the workflow directly competes with the time and attention dedicated to patient care.
In a 20-bed ICU, for example, it is not uncommon for a physician to consult five or more different systems during a single shift: electronic health records, laboratory results, imaging platforms, clinical applications and decision-support tools. Every additional screen, login or process introduces another interruption into an environment where time and attention are already scarce resources.
For physicians and nurses, attention and time are among the most valuable assets. Tools that increase operational burden or fragment care are unlikely to be adopted, regardless of the quality of the information they provide.
This leads to one of the most important concepts for the future of AI adoption in healthcare: the distinction between AI as an external tool and AI integrated into the clinical workflow.
External AI operates outside the usual care process. To use it, professionals must interrupt patient care, access a separate platform, and manually search for information. Workflow-integrated AI, by contrast, becomes an organic part of clinical decision-making, delivering contextualized, actionable insights exactly when they are needed, without requiring additional navigation, screen changes or disruption of clinical reasoning.
AI systems designed for the early identification of clinical deterioration, for example, can operate directly within the care workflow, without requiring parallel platforms. Existing clinical data can feed automated processes, whether powered by AI or clinical rules, enabling more timely activation of support teams.
AI must therefore reduce cognitive burden, not simply generate information.In highly complex environments, this imperative becomes even more important. Healthcare professionals face enormous volumes of information, multiple digital systems, heavy operational workloads and, frequently, burnout.
In practical terms, an AI solution that requires clinicians to step away from patient care in order to use it is unlikely to become part of routine clinical practice.
Reducing operational friction is therefore one of the most decisive factors in this landscape. It increases adoption, improves real-world utilization, and enhances the ability to anticipate risks and support decisions in real time, particularly during direct patient care.
This becomes even more critical in time-sensitive situations, where minutes can directly affect clinical outcomes: early recognition of sepsis, detection of clinical deterioration, prediction of prolonged mechanical ventilation or estimation of ICU mortality risk. In these scenarios, the value of AI lies not only in generating sophisticated analyses but, above all, in delivering actionable information at the right moment, embedded within the care workflow and available while there is still an opportunity to influence decisions and alter the course of care.
This principle is central to the way Epimed Solutions develops its clinical intelligence solutions. The goal is to integrate intelligence directly into the care workflow, ensuring that relevant information is available at the moment decisions are made, without requiring professionals to interrupt patient care or access additional platforms.
In practice, AI models are incorporated directly into team workflows and the systems already used by healthcare institutions. Predictions of mortality, estimated ICU length of stay, risk of prolonged hospitalization, likelihood of mechanical ventilation, and risk of early readmission become available in a contextualized manner, precisely when they can support clinical or operational decisions.
The future of artificial intelligence in healthcare may depend less on increasingly sophisticated algorithms and more on the ability to make intelligence invisibly useful. The best AI in healthcare is not necessarily the one that attracts the most attention. It is the one that becomes a natural part of care delivery, provides context at the right moment, and supports decisions without interrupting what matters most: the patient.
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This is the fourth publication in the editorial series “AI in Healthcare: Credibility, Safety, and Impact in Clinical Practice,” produced by Epimed Solutions.
Author: Dr. Carlos Eduardo Brandão, board-certified intensive care physician by AMIB, graduated in Medicine from the Federal University of Espírito Santo (UFES), with residency training in Internal Medicine and Intensive Care Medicine at the University of São Paulo Medical School (FMUSP), and an MBA in Executive Management of Clinics and Hospitals from Fundação Getulio Vargas (FGV). Epimed Partner.
