By Kevin Ritter, EVP for CareInMotion, Altera Digital Health
LinkedIn: Kevin Ritter
LinkedIn: Altera Digital Health
Despite near-universal EHR adoption, healthcare still operates as if patient data were scarce, fragmented and unreliable. The issue isn’t access, it’s trust. And without trust, data can’t drive decisions, outcomes or value.
For years, disjointed systems and fragmented records have undermined clinical decision-making and patient outcomes at-scale. But this isn’t a volume problem. It’s a value realization problem.
That status quo is ready to break. We now have the technological capability to deliver integrated, normalized and harmonized data with advanced, intelligent platform systems designed to deliver it where and when it is needed in a usable form—turning data from a burden into a strategic asset that genuinely supports clinicians and the patients they serve.
Building a stronger foundation
More than 87% of hospitals can now send information to and receive it from external providers, but only 42% of clinicians routinely use that data when treating patients. The systems “talk” to each other, but they don’t consistently deliver the right information at the right time in a common language. With disorganized and duplicated data pouring in from lab results, physician notes, claims, social determinants and more, vital information remains buried and out of reach for providers.
The next phase of health IT must close that gap. Clinicians need to make informed decisions when and where care is delivered without tabbing between systems or skimming dozens of PDFs to piece together a patient’s story. Modern data platforms can ingest diverse data formats and run real-time validation to flag missing or conflicting information before it becomes a clinical risk. Additionally, modern healthcare intelligence platforms can measure the quality of data throughout its entire lifecycle.
This is how healthcare moves beyond basic data exchange toward comprehensive, streamlined patient records. When clinicians no longer need to manually crosscheck documents or comb through fragmented, unusable files, errors decrease and time shifts back to patient care, where it belongs.
Aligning to incentives
Data quality challenges don’t stop at the clinical sphere. As value-based care and full-risk reimbursement models expand, accurate and auditable records are becoming a business imperative. Payment models that reward better patient outcomes rely on trusted, usable data for quality programs, care coordination and patient engagement. When patient data is unreliable or incomplete, providers face real misalignment between the care they deliver and the reimbursement they receive.
This shifting landscape demands healthcare organizations rethink their data governance and operational practices. Many are finding traction by moving away from point-to-point strategies to platform-based interoperability to support stronger, more frictionless data exchange.
Rather than passively storing information, organizations can manage it proactively with platforms that evaluate inbound messages for structural accuracy, appropriate coding, semantic alignment and identity completeness in real time. With FHIR-native architectures, they can build high-quality data fabrics that reconcile conflicting information to maintain a complete patient story.
Organizations that treat data quality as a foundational requirement rather than just another IT metric will close care gaps and improve patient outcomes more effectively. That’s the real goal of prioritizing value-over-volume payment models.
Enabling improvements with intelligence
Once a dependable data foundation is in place, intelligent systems can unlock the full potential of healthcare organizations’ vast amount of data, transforming raw information into a trusted resource for clinicians and care teams. The result is a shift from reactive care to more preventative and personalized approaches.
With unified clinical, claims, social and utilization data, organizations can leverage predictive models that forecast risks before they happen, positively impacting clinical deterioration or avoidable admissions at the individual level, not just for broader patient cohorts. Clinicians can leverage artificial intelligence (AI) to tailor treatment plans based on the patient’s comorbidities, social needs and other relevant factors, supporting care plan adherence that leads to better outcomes.
Agentic AI can also help streamline documentation, chart summarization, prior authorization, quality reporting and pre-visit preparation. This isn’t just time savings; it frees clinicians to focus on more valuable, meaningful tasks that ease clinician burden.
Shifting from volume to value
Healthcare is on the precipice of overdue change. The deluge of data that overwhelms clinicians and contributes to duplicative testing, denials and waste can become the engine behind operational efficiency, defendable billing and clinical clarity.
We don’t need less data. We need less complexity. With a foundation of comprehensive, trustworthy data, healthcare organizations can quiet the noise of processes and paperwork and put patients and providers at the center of more effective care and better outcomes for each encounter.