Healthcare decisions often run on thin evidence. Clinical guidelines lag years behind the treatments already in use, and the data that could close that gap sits in electronic health records, claims data, and patient-reported outcomes. No one has the time to read it all by hand. Real-world evidence could show which treatments work for which patients. Building the pipeline to generate it typically takes nine to twelve months.
AI changes what a health system can do with the data it already holds. An AI pipeline harmonizes records, claims, and outcomes from every source, reads the unstructured clinical text, and turns it into evidence a clinician or administrator can act on. The result is a tool for identifying effective treatments, predicting patient outcomes, and optimizing healthcare delivery. Shakudo deploys a functional version in weeks, so the evidence starts building early in the project.
Shakudo deploys an AI pipeline that turns vast amounts of real-world health data into actionable clinical insight. The system integrates electronic health records, claims data, and patient-reported outcomes into one evidence base, reads the unstructured medical text in it, and surfaces the trends behind treatment effectiveness and patient outcomes. Clinicians and administrators work from interactive views of the same data, so the evidence reaches the people who make the decisions. Resource allocation follows the data. Outcomes improve as the evidence base grows and updates. A system that would take a data team nine to twelve months to build is functional in weeks.
Because the AI runs entirely on the health system's own infrastructure, it can read patient records, claims, and outcomes that cannot leave the environment. That is what makes real-world evidence possible in the first place. Protected health information often cannot be uploaded to a cloud AI vendor, and most analytics platforms require it to. Sovereign AI on the health system's own infrastructure closes that gap. The pipeline stays on the customer's infrastructure, so the evidence base and the AI that builds it are fully owned and controlled from model to memory.
The pipeline runs on a stack the data engineering and analytics teams already know. Apache Spark processes the large-scale health datasets in a distributed way, and Ray extends that distributed computing across the full workload. Transformers extracts meaningful information from unstructured medical text, so the narrative notes become analyzable fields. Delta Lake keeps the data reliable and versioned, which is critical for maintaining the integrity of sensitive health information. Superset provides the interactive visualizations that make complex health trends accessible to clinicians and administrators alike, and Apache Airflow orchestrates the entire data pipeline so the evidence base stays timely and accurate.
Health systems, life sciences organizations, and health analytics teams that need real-world evidence from the data they already hold, and whose patient information cannot leave the environment. A strong fit for teams building comparative effectiveness insights, outcome prediction models, or resource optimization analytics on top of clinical and claims data, and for teams preparing an evidence package for payers or regulators where the data trail has to hold up to review.
Electronic health records, claims data, and patient-reported outcomes. The pipeline integrates and harmonizes all three into one evidence base, and the Transformers models read the unstructured clinical text in them, so the notes and narratives become part of the analysis.
Delta Lake versioning tracks every change to the data, so any finding traces back to the exact version of the evidence base that produced it. Apache Airflow keeps the pipeline running on schedule, so the evidence base stays timely and accurate as new records and claims arrive.
Developing a system like this in-house typically takes nine to twelve months. Shakudo deploys a functional version in weeks, so the health system starts generating evidence early in the project, with the full pipeline growing from there.
For healthcare decisions, that means a question that used to need a nine-month data build now has evidence from the records the system already holds. Book a demo and see real-world evidence generated from the data a health system already runs on.