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As healthcare organizations deepen their investments in value-based care, one obstacle continues to stymie progress: incomplete visibility into the patient’s health status. The problem isn’t limited to a single source, such as claims data, though claims are often blamed. Rather, it’s the fragmented nature of healthcare data itself. Patient information is dispersed across a patchwork of systems—EHRs, HIEs, scanned documents, specialist consults, diagnostic reports, lab results, payer files, and hospital notes—and it exists in multiple formats: structured fields, free-text notes, image annotations, and audio transcripts.
Each source captures part of the story. But none offers a complete picture on its own. Clinicians are left to piece together patient narratives from these disparate fragments, often under tight time constraints and with little confidence in the completeness of what they’re seeing. Making sense of the flood of multi-source, multi-modal data is an inhuman challenge.
This chronic failure to rise to this challenge goes beyond mere inconvenience to directly affect clinical and financial outcomes. Inaccurate or incomplete documentation can lead to under-coding, poor risk adjustment, and ultimately lost revenue or regulatory risk. Worse, it can delay diagnoses or result in suboptimal treatment decisions. But when clinicians are given recommendations based on a broad range of sources, their confidence rises accordingly. Based on our own research, we’ve found that diagnosis suggestions based solely on claims data are accepted by physicians less than half the time. When the same suggestions are supported by reconciled multi-source clinical evidence, acceptance rates jump to nearly 80%.