Building the data foundation forAI-driven neurodiagnostics.

The Situation

This pioneering medical technology firm is building accessible, affordable neurodiagnostic technology — combining AI, signal processing, and sensor innovations to enhance care for patients undergoing complex surgical procedures. Their core product relies on Intraoperative Neurophysiological Monitoring (IONM): real-time feedback that protects the nervous system and guides precise neural mapping during surgery.

Over years of operation, the firm had accumulated over 60,000 IONM cases — a goldmine of bioelectric signal data, electronic medical records, and clinical documentation. Their goal was to unlock predictive healthcare AI from this data. The infrastructure to do so did not exist.

The Complication

The data was rich but completely fragmented. Bioelectric signals lived in raw JSON files. Clinical records sat in SQL databases. Hospital documents — handwritten notes, discharge summaries, PDFs — were scattered and unstructured. No single system could see across all three data types simultaneously.

The 45,000 pre-2021 cases and 15,000 post-2021 cases were stored in separate systems with incompatible schemas, making cross-case analysis or AI model training effectively impossible. Extracting and classifying raw signal data required specialized tools and subject matter expert involvement that no standard data platform could handle.

The firm had an ambitious roadmap — predictive surgical risk models, real-time analytics, data monetization. But their data maturity was at level zero: scattered, ungoverned, and unprocessable. The AI ambition and the data reality were miles apart.

The Solution

DataWalkers designed and built a four-phase intelligent data lake — taking the organization from scattered, incompatible data to a governed, AI-ready analytics platform purpose-built for clinical AI applications.

Phase 1 — Data Discovery: We profiled all data sources across signal JSON, EMR tables, and hospital PDFs. We built a unified patient data model and selected the cloud architecture and tooling aligned to the AI roadmap goals.

Phase 2 — Foundational Data Lake: We stood up the infrastructure and built custom data pipelines for each data type — including specialized signal extraction pipelines and PDF parsing for unstructured hospital records. We delivered initial BI reports and a data catalog of available, usable data.

Phase 3 — Analytical Data Lake: We automated all pipelines, implemented continuous data quality management and error automation, and delivered refined BI reports. The team could now see data readiness in real time.

Phase 4 — Intelligent Data Lake: With clean, governed, harmonized data in place, we built the AI/ML infrastructure — feature catalogs, signal classification datasets built with SME collaboration, and the architecture for real-time IONM analytics and predictive models.

The Impact

For the first time in the firm history, every IONM case — bioelectric signal, clinical record, and hospital document — existed as a single coherent patient view in one governed platform. The data foundation that had blocked AI ambitions for years was now ready for production ML workloads.

• 60K+IONM cases unified into a single AI-ready platformBioelectric signals, EMR records, and hospital PDFs harmonized into one governed data lake for the first time.
• 4 PhaseStructured journey from level 0 to intelligent data platformDiscovery → Foundation → Analytical → Intelligent — each phase building on the last with measurable delivery milestones.
• 100%Data quality visibility across all sourcesAutomated DQ monitoring and exception handling — teams know exactly when data is ready and why.
• AI-ReadyGolden dataset for ML model developmentFeature-extracted, SME-validated signal classification dataset ready for predictive healthcare model training.