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Fragmented healthcare data continues to limit the ability to deliver efficient, personalized care at scale. Patient information is scattered across multiple providers, payers, and systems, with some stakeholders from the healthcare ecosystem, namely hospitals, reporting only being able to access 43% of patient data from outside their own networks, according to the Office of the National Coordinator for Health Information Technology (ONC). Missing or incomplete data often leads to duplicative testing, delayed interventions, and higher costs for payers while resulting in a frustrating patient experience for plan members.
Achieving a complete, longitudinal view of a patient’s health is essential for delivering high-quality care. Health plans that adopt an AI-enabled care model as a front door give members access to hyper-personalized care, as these models leverage interoperability to integrate patient context. This translates into:
Despite decades of digitization, patient information remains scattered across providers, payers, labs, pharmacies, and ancillary services. In fact, approximately 70% of providers rely on fax machines to exchange medical information.
This fragmentation in healthcare contributes to both operational and clinical consequences. Without proper context and a complete clinical picture, care may be delayed, risks may be obscured, and suboptimal routing decisions may needlessly escalate members into high-cost settings. From an operational perspective, incomplete data drives redundant, often costly testing while limiting a health plan’s ability to proactively manage utilization. Without a longitudinal view of member history, care navigation efforts struggle to deliver consistent and defensible outcomes.
Personalized care begins with integrated patient context retrieved from disparate settings. When there are gaps in context, downstream impacts compound quickly.
Incomplete member histories can affect the accuracy of claims, risk stratification, and quality measurement. Care teams may lack visibility into prior utilization patterns, recent diagnostic results, or compliance with preventive care. As a result, opportunities to intervene earlier, close care gaps, or redirect members toward lower-cost options may be easily missed.
Complete patient context enables smarter triage and more appropriate decision-making across the care continuum. A context-aware model has a holistic understanding of what has already happened, what is currently underway, and what risks are emerging, rather than treating each interaction as a standalone event.
HIEs and interoperable EHR standards act as connective tissue, enabling context-aware care at scale. Progress is real, but rarely linear. According to the Office of the National Coordinator for Health Information Technology (ONC), only 70%of U.S. hospitals engaged in interoperable exchange across the core functions of sending, receiving, finding, and integrating electronic health information. This increases the likelihood that critical clinical data that exist outside the claims record can be retrieved quickly and reliably when a member needs care.
Standards such as Health Level 7 (HL7) messaging, Consolidated Clinical Document (CCD) summaries, and Fast Healthcare Interoperability Resources (FHIR) make exchange operational by structuring how clinical data moves and how it can be used:
Nationwide interoperability networks, such as CommonWell and Carequality, help provider organizations exchange patient records across different EHR vendors and health systems. CommonWell is an alliance focused on enabling record location and exchange among its participating members, while Carequality provides a common “rules of the road” framework that connects many networks and EHRs, enabling organizations to query and retrieve data across entities more consistently.
These networks expand the reachable footprint of clinical data beyond local HIE boundaries, supporting a more complete member context when powering navigation, risk assessment, and care gap identification.
HIEs become actionable when shared data meaningfully improves decisions at the moment of care. Interoperable exchange enables access to lab results, imaging, medication histories, and prior procedures across providers, filling clinical gaps that claims data alone may miss. That richer context supports more accurate triage, safer recommendations, and fewer unnecessary escalations.
The difference between having access to data and actually using it cannot be overlooked. Hospitals and health systems have experienced this firsthand. While 71% of hospitals report that external clinical information is electronically available at the point of care, only 42% say that clinicians routinely use it. Hospitals that fully engage in interoperable exchange across all domains see far higher utilization. In 2023, 70% of these routinely interoperable hospitals reported that clinicians often used external information when treating patients, compared with just 26% of hospitals that were only sometimes interoperable.
While HIEs and EHRs facilitate the bi-directional exchange of data to deliver measurable outcomes for payers, this data must be actionable to have an impact on patients’ health. AI-enabled primary care solutions, such as Counsel, become this actionable layer.
Counsel connects to HIEs, EHRs, and health plan datasets to inform care interactions in real time, leveraging a RAG AI framework that synthesizes medical history, prior interactions, and validated clinical sources to support care delivery. This ecosystem-level integration enables identification of care gaps, earlier recognition of higher-risk members, and proactive interventions aligned with plan networks and benefits.
Rather than operating as a standalone platform, Counsel functions as a responsible front door to care that members naturally return to over time. Through a messaging-based experience, members receive timely care informed by their full medical history, while health plans gain a scalable way to support intelligent triage, reduce claims costs, and improve how care is accessed. The result is a care model that feels seamless for members and effective for payers, especially as 96% of medical concerns can be addressed by Counsel without the need for escalation.
To satisfy the highest payer compliance and risk standards, every interaction is safeguarded by providers, in addition to HIPAA and SOC 2 security protocols, and audit-ready guardrails.
To evaluate how responsible medical AI can function as a front door to care within your network, request a demo of Counsel and explore how we can integrate into your existing ecosystem.
The Counsel Health editorial team is a multidisciplinary group of writers and editors dedicated to delivering clinically grounded, evidence-based health information. Their work is informed by real-world care delivery and guided by physician expertise, ensuring content is accurate, accessible, and trustworthy. By translating complex medical topics into clear, practical guidance, the team helps readers understand their health, explore care options, and make informed decisions in a rapidly evolving healthcare landscape.
Javier Monterrosa is a healthcare marketing leader who has spent his career driving growth across AI, metabolic health, interoperability, and EHR companies. He holds a Master’s in Analytics and has co-authored published research examining how strategic decisions shape business growth. Having grown up in Latin America, he is driven to partner with mission-driven teams committed to improving healthcare access and outcomes through responsible technology.
Our content is created for informational purposes and should not replace professional medical care. For personalized guidance, talk to a licensed physician. Learn more about our editorial standards and review process.
