Healthcare systems spend heavily on chronic disease once symptoms emerge, yet many conditions are preventable or better managed with earlier intervention. Artificial intelligence is shifting the balance by supporting earlier diagnoses, generating actionable insights and improving how resources are allocated. At the HIMSS25 Global Conference, healthcare leaders from industry and provider organisations outlined practical ways to embed AI and digital tools in clinical care to enhance patient outcomes, support clinicians and deliver long-term value for health systems. Their focus spanned prevention, risk stratification and the operational foundations needed to scale responsible innovation. The direction is clear: integrating early detection with AI can help move care upstream, reduce downstream cost and strengthen sustainable, value-based delivery.
From Reactive Treatment to Preventative Care
Across the United States and parts of the European Union, policy and investment are tilting toward proactive, preventative approaches. Spending is growing on early detection with AI and on prevention programmes, while the market for digital tools that enable early screening and continuous monitoring is expanding. This momentum reflects recognition that traditional models centred on treating illness after symptom onset leave avoidable morbidity and cost on the table. A preventative stance seeks to identify risk earlier, intervene sooner and keep populations healthier for longer.
Progress, however, is uneven. Workforce shortages, limited physician availability and long waits for appointments constrain capacity to expand screening and outreach. Routine screening programmes suffer from low adherence, weakening the link between expanded capability and real-world uptake. Even when AI-enabled tools generate rich clinical data, the value arrives only when those data are translated into insights that guide targeted, individualised care at the right moment in the workflow. Making multidimensional patient data usable in practice is therefore as important as building models that can detect risk.
The imperative is to embed early detection within everyday care rather than run it alongside. That means connecting models to clinical pathways, surfacing timely prompts and aligning outputs with resource availability. It also means measuring impact not only through detection rates but through changes in outcomes and exploitation patterns that reflect earlier engagement and smarter allocation of care.
Clinical Applications: Sepsis and Risk Stratification
Sepsis illustrates both the need and the challenge of early detection. Presentation varies across populations, definitions are inconsistent among decision-makers and deterioration can be rapid without clear warning signs. Predictive algorithms powered by AI and machine learning can help clinicians make sense of large volumes of clinical data to identify who is ill now or at rising risk. In practice this supports timelier recognition, more accurate antibiotic use and improved adherence to documentation and coding standards that underpin appropriate reimbursement.
AI-enhanced clinical decision support is being explored to extract meaningful signals from external sources to strengthen population health efforts. Data quality is a prerequisite. A large share of patient information sits as unstructured free text, estimated at 70%, which makes retrieval and analysis difficult. Data harmonisation that renders inputs from diverse systems consistent and interoperable is therefore essential to build effective decision support. Once generated, insights must be delivered into appropriate workflows so teams can act, whether that means prioritising high-risk patients for additional resources or confidently directing lower-risk patients to alternative pathways.
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Building the Foundations for Responsible Deployment
Care delivery is moving toward real-time, on-demand and setting-agnostic models, spanning acute care, ambulatory environments, retail clinics and the home. AI and digital tools are accelerating this shift by providing the right information in the right place. Predictive analytics and risk stratification enable earlier intervention and help optimise resources at scale. AI-powered clinical decision support offers real-time prompts during care, reduces cognitive load on clinicians and promotes more consistent, evidence-based decisions. In diagnostics, AI can accelerate imaging analysis, reduce diagnostic errors and support point-of-care and at-home testing. Automating routine tasks such as documentation and triage frees clinicians to focus on complex or high-value needs.
Personalised care pathways are a further benefit. AI can tailor monitoring plans, trigger alerts for preventive screenings and flag missed follow-ups to sustain engagement. Realising these gains requires more than algorithms. Organisations need robust digital infrastructure and interoperability so data can flow, governance frameworks that set boundaries and maintain trust and management and clinician training that ensures safe, effective use. Sustainable funding models are also required to support integration over the long term rather than rely on short-lived pilots.
Security and compliance must be embedded from the outset. Treating them as afterthoughts risks fragmentation and erodes confidence. Placing cybersecurity and data privacy at the core supports safe scaling across settings and helps systems maintain public trust as they introduce AI-enabled services. When these enablers are in place, AI can develop along dimensions that matter most to clinical teams, with improvements in speed, accuracy and predictive power aligned to measurable gains in patient care.
Early detection supported by AI offers a practical path to shift care upstream, personalise interventions and allocate resources more intelligently. Sepsis detection and population risk stratification show how models can guide timely action, while data harmonisation and workflow integration determine whether insights translate into improved outcomes. Building the necessary infrastructure, governance, training and funding, with privacy and security at the core, allows organisations to deploy AI responsibly across settings. For healthcare leaders, the opportunity is to integrate early detection into routine practice now to improve care quality and reduce avoidable costs while supporting clinicians and patients through more consistent, preventative care.
Source: Healthcare Transformers
Image Credit: iStock
