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Clinical patient data is a powerful resource in healthcare. But, as the healthcare IT ecosystem grows in complexity, data is more plentiful and problematic.
As patient data moves across various health information systems, it can become highly variable and lose key components. This leads to a drain on IT, analytics, and clinical resources, as staff must spend more time manually filling in the gaps.
NLP solutions must be trained in the complexity of clinical language to efficiently and effectively realize value from unstructured healthcare data. Larger NLP vendors often lack the expertise to manage these intricacies, resulting in inaccurate identification and extraction of relevant clinical concepts. Meanwhile, home-grown solutions that rely on open-source NLP models often require significant investment and resources to build the necessary domain expertise to support their models.