Wired Informatics

Wired Informatics is a Boston-based healthcare technology company specializing in natural language processing (NLP) through its flagship platform, Invenio. The company focuses on extracting actionable knowledge from unstructured clinical data within electronic health record systems, addressing the challenge that approximately 80% of EHR data exists in unstructured formats.

The Invenio platform combines statistical machine learning algorithms trained on various clinical documentation types including primary care notes, pathology reports, radiology reports, oncology reports, and discharge summaries from major healthcare institutions. The company's technology serves three primary use cases: quality and safety auditing for hospitals and pharmaceutical companies, mobile health transcription and billing applications, and big data analytics by normalizing unstructured text for integration with Hadoop and MongoDB data lakes.

Wired Informatics has established partnerships with notable healthcare organizations including Boston Children's Hospital, University of Florida Health, and genetics company Invitae, as well as technology partnerships with Cloudera and Hortonworks for big data integration capabilities.

Choose Wired Informatics when your organization needs to unlock value from unstructured clinical text and has specific use cases around quality improvement, documentation workflow, or population health analytics. Their Invenio platform is particularly well-suited for organizations already invested in big data infrastructure who need clinical NLP capabilities to complement their analytics stack.

Expect a technology-focused engagement centered on the Invenio platform's machine learning capabilities. The company's partnerships with major healthcare institutions suggest experience with complex clinical environments and diverse documentation types.

Wired Informatics is exclusively focused on healthcare, with its Invenio platform designed specifically for clinical natural language processing. The company's machine learning models are trained on clinical documentation including pathology reports, radiology reports, oncology notes, and discharge summaries. Their solutions address healthcare-specific challenges including quality auditing, medical documentation standardization, and population health analytics, with clients spanning hospitals, health systems, and pharmaceutical companies.

Mid-to-large healthcare organizations with significant volumes of unstructured clinical documentation and existing data analytics initiatives. Organizations with technical infrastructure including Hadoop or MongoDB deployments seeking to incorporate clinical text analysis. Pharmaceutical companies needing medical information automation or health systems focused on quality improvement and population health management.