Leveraging EHR data and predictive analytics for CDS

True interoperability and predictive analytics for positive health outcomes

Clinicians with experience using an electronic health record (EHR) understand all too well just how much information they have at their fingertips—sometimes too much information.

What if clinicians had a system that delivered this information in the form of continuously updated and structured data? And what if they could complement more usable data with automation that proactively surfaces recommendations and use AI tools that act as a partner in clinical decision support?

Find out how leveraging the power of an AI-native EHR at scale with the deployment of predictive analytics will be able to help assist in clinical decision support (CDS) and care coordination.

The importance of operational scale and an AI-native EHR

Some EHR systems include fragmented or bolted-on AI tools. But these tools may be less effective if they act on outdated or unusable data. Conversely, athenaOne® combines a true SaaS architecture with network-wide learning, instantly integrating updates and continuously refining data.

At the core of athenaOne is native AI — AI that is foundational in the Advanced Intelligence Layer and operates directly within the EHR ecosystem, rather than as an external add-on. Native AI can comb through vast amounts of data in the network — from electronic health records, lab results, imaging, historical diagnoses, and more.

An AI-native EHR can impact clinical decision support by helping anticipate clinician needs. This can translate to predictive analytics models that can make actionable recommendations that reflect the most comprehensive, up-to-date knowledge available. The AI doesn’t just identify patterns; it helps paint a richer clinical picture, which can reinforce or help guide CDS at the point of care.

The next frontier of predictive analytics in healthcare

Different healthcare systems may harness predictive analytics for things like optimizing operational efficiency or anticipating payer rule changes. But that’s merely scratching the surface of what predictive models can achieve in healthcare – particularly as it relates to powering insights at key moments of care and enhancing patient satisfaction.

One of the promising forthcoming tools in athenaOne is the Clinically Inferred Diagnosis feature. This predictive AI model will use multi-dimensional data analysis related to elements like past patient encounters, historical patient health data, medication history, and social determinants of health. Once the data is synthesized, the model will suggest diagnoses for clinician review. By doing so:

• Clinicians get a window into potential conditions — helping to enable timely intervention and patient engagement.
• Regardless of whether clinicians act on model recommendations, the insights can help them create or adjust personalized care plans.
• Clinical documentation becomes more precise and comprehensive, aiding Clinical Documentation Improvement (CDI) efforts.
• Increased awareness of potential diagnoses or associated risk factors may help reduce missed or delayed diagnoses, which can improve patient safety and outcomes.

Moreover, Clinically Inferred Diagnosis will eventually be available in ambient form. This advancement means AI featured in ambient listening tools will help surface diagnosis recommendations in real time. The diagnoses will cite conversational cues and clinical context during active patient encounters for clinician review. This ambient approach integrates seamlessly into the natural flow of care, augmenting clinician awareness without adding workflow burdens.

Evidence-based recommendations that can elevate patient care

Each context-aware recommendation provided by the Clinically Inferred Diagnosis tool will link directly to clinical evidence that helps provide the underlying rationale for the diagnosis. Clinicians can view this supporting data and click through to relevant evidence sources directly within the EHR, providing transparency and reinforcing trust:

• The AI-driven suggestions integrate clinical guidelines with patient-specific data.
• Suggestions are prioritized based on risk stratification and predicted outcomes.
• Insights can help clinicians identify care gaps and optimize treatment plans.

This means clinical decisions are not only faster but more informed — supporting personalized patient care plans that are both effective and efficient.

An AI-native EHR can impact clinical decision support by helping anticipate clinician needs. This can translate to predictive analytics models that can make actionable recommendations that reflect the most comprehensive, up-to-date knowledge available.

Up-to-date data helps close care gaps

Identifying patient risk is key to proactive healthcare management. Native AI in athenaOne supports this by continuously updating and structuring vast datasets to identify patients at higher risk due to chronic conditions, social determinants of health, or potential diagnosis gaps. Clinicians can:

• Quickly identify patients who need closer monitoring or intervention.
• Anticipate emerging health issues before they become acute.
• Target resources effectively to help improve outcomes and reduce costs.

Sharing predictive insights with payers supports meeting value-based care (VBC) requirements, such as quality reporting and care coordination.

Clinical Documentation Improvement reinforced by AI

CDI has traditionally relied on retrospective documentation audits and manual chart reviews. The new AI-powered tools in athenaOne will:

• Analyze encounter data and patient chart updates to help pre-fill diagnoses and orders, improving accuracy with real-time updates and any clinician revisions or inputs.
• Provide AI-powered overviews of diverse clinical data from disparate sources to help with preparedness ahead of patient encounters.
• Leverage generative AI to sift across documents, charts, and clinical events relevant to individual patients and help answer pressing clinician questions. This can help reduce clicking through patient charts and simultaneously allow clinicians the opportunity to document notable follow-ups.
• Provide model choice optionality within the Ambient Notes solution – which gives clinicians the chance to fine-tune their documentation style over time and help them work toward crafting more accurate notes.

This real-time CDI augmentation can help reduce administrative burden while providing clarity and utility for patient records.

Making EHR data more intentional and valuable

As healthcare systems embrace interoperability at scale through initiatives like TEFCA, the power of native AI demonstrates how integrated, data-rich AI can help transform healthcare delivery with precision, speed, and empathy.

Clinicians can utilize predictive tools like Clinically Inferred Diagnosis that work off more usable, updated EHR data. With predictive models that can deliver evidence-based, personalized recommendations and help empower proactive risk stratification, the EHR becomes a true representation of an integrated system that can aid clinical decision support.

It's important to remember that the AI supports, rather than replaces, clinician judgment. It provides suggestions backed by relevant clinical data and guidelines, helping prevent errors by prompting reviews or additional documentation rather than making autonomous decisions.

Ready to see how AI-native athenaOne can integrate into your practice? Explore our AI capabilities and join the network benefiting from some of the most advanced predictive healthcare technology in the market.