How AI reduces data overload in your EHR

Getting the full picture of patient care through AI

Clinicians may no longer be struggling to access their patients’ data from disparate sources, but they may be struggling to sort through it. As interoperability expands, more patient data is flowing into the EHR than ever before. For example, Patient Record Sharing (PRS) automatically exchanges medical records with care sites across the U.S., giving clinicians access to a broader, longitudinal view of their patients' histories.

Although data visibility is powerful, it comes with new challenges. External hospital records often arrive as a flood of mixed-content documents. Some documents are clinically essential while others are irrelevant, duplicative, or administrative. When clinicians must open and review each document manually to determine its value, the promise of interoperability can turn into information overload.

The challenge is no longer gaining access to patient data. It is identifying what matters quickly, confidently, and within the realities of a busy clinical workflow.

The interoperability paradox: Connected but overwhelmed

PRS has significantly strengthened continuity of care. It gives practices access to encounter summaries, discharge notes, lab results, and other documentation from hospitals and specialists outside their organization. For patients who move between care settings, that exchange helps reduce blind spots and improve coordination. But broader access can also increase cognitive burden.

External hospital encounters often populate the record of care with numerous documents that vary in relevance and quality. Some contain meaningful clinical updates. Others repeat information already documented in the chart. Still others contain little actionable insights. For example, a hospital visit might generate multiple documents, including administrative intake forms, duplicate medication lists, and discharge paperwork, alongside a single note documenting a new diagnosis or treatment decision that is clinically significant.

Before AI-supported workflows, clinicians had to open each document individually to determine whether it was worth reviewing in detail. In high-volume environments, this manual sorting process meant extra clicks, more time spent navigating the chart, and greater risk that critical information could be overlooked amid lower-value content.

Interoperability has succeeded in connecting systems, and the next step is making that connectivity usable at the point of care.

Using AI to surface what matters

ChartSync, a native and no-cost capability within athenaOne®'s electronic health record (EHR), was built to transform external data into actionable insights. As part of athenahealth's AI-native platform strategy, ChartSync applies intelligence directly within clinical workflows as an embedded enhancement.

With its latest expansion, ChartSync now displays brief AI-enabled summaries alongside all hospital encounter documents received via PRS. A clear icon denotes AI-generated content, allowing clinicians to quickly scan and prioritize incoming records.

When ChartSync first launched in summer 2025, it labeled administrative documents. Now, it also summarizes clinical documents. This expansion enables clinicians to identify high-value content, such as discharge outcomes, procedure results, or meaningful changes in condition—without opening every file.

The summaries do not replace clinical review. Instead, they act as a triage layer, helping clinicians determine where to focus their attention. An in-app feedback mechanism allows users to flag summary accuracy, continuously improving performance over time. This means fewer unnecessary clicks, less manual scanning, and faster identification of relevant patient history.

Promising early results

Early outcomes suggest that clinicians are finding value in this approach. Approximately 1,200 athenahealth customers, representing practices of all sizes, participated in alpha and beta programs. Among evaluated summaries, 90% were marked accurate by clinicians. Fewer than 1% of documents summarized as containing no clinical information were opened, indicating strong alignment between AI prioritization and clinician judgment. Beta participants also reported an 80% improvement in satisfaction.¹

These results are promising and could be interpreted to show that AI-enabled summaries may be reducing manual review work and building clinician trust. In an environment where time is a limited resource, reducing layers of friction has the potential to meaningfully improve the clinical experience.

Improving reconciliation of external allergies

ChartSync's role in reducing information overload extends beyond document summaries. It also enhances reconciliation of external allergy data, which is another area where fragmentation and duplication can create risk and inefficiency.

ChartSync now pulls allergy information from Carequality, CommonWell, and Direct Secure Messaging. The system automatically deduplicates and consolidates allergy data, surfacing it within a dedicated ChartSync panel. From there, practices can choose automatic reconciliation or complete a one-click manual reconciliation to add an allergy to the chart or move it to a dismissed list. Importantly, dismissed allergies do not resurface during future data transfers, preventing repetitive review.

This enhancement addresses a meaningful documentation gap. An estimated 25% of patients in the U.S. have an allergy. Previously, legacy reconciliation workflows captured allergies in only 8.3% of athenaOne patient charts. With ChartSync, that number has increased to 21.5% of patients with encounters, approximately doubling the number of allergies surfaced per patient and moving closer to national prevalence levels.¹

In addition to improving visibility, ChartSync's enhanced reconciliation of medications and allergies saves users an average of 10.6 clicks per reconciled data source.¹ Fewer clicks translate directly into improved clinical efficiency and reduced administrative burden.

Reframing AI in the EHR

ChartSync does not make clinical decisions. It reduces the friction involved in reviewing patient medical history and transforms raw data exchange into prioritized, point-of-care insights.

In its use of AI, ChartSync’s approach reflects athenahealth's AI-native strategy of embedding intelligence directly into workflows to enhance usability, improve clinical experience, and support efficiency across practices of any size.

Turning data volume into clinical clarity

Clinicians should not have to navigate a sea of irrelevant documents to find critical insights.

By surfacing high-value content through AI-enabled summaries and strengthening reconciliation of external allergies, ChartSync helps transform interoperability into a practical advantage. The result is:

• Less time spent manually reviewing external documents
• Fewer clicks required to reconcile external data
• Greater confidence in the completeness of the patient record
• More focus on patient care rather than chart navigation

AI is helping clinicians move from information overload to clinical clarity and making interoperability usable.

In a healthcare environment defined by expanding connectivity, the real advantage lies in clarity. By helping clinicians access greater insights on their patients, athenaOne supports more informed care delivery. Learn more about AI-native athenaOne.