Computable Knowledge

Computable knowledge consists of many different types of health analytics and code systems. They typically support decision making with results integrated back into workflow and aggregated into retrospective, predictive or interdictive analysis. 'Knowledge' refers to health know-how, such as scores, algorithms, rules, prediction models, pathways, guidelines, and measures. Knowledge also includes value sets, ontologies, and concept sets (or codesets) which codify health information.

Making knowledge 'Computable' refers to taking knowledge information, which may only be available in paper form, and making them digitally available to a computer system transforming them into 'Computable' assets. These computable assets can then be orchestrated to quickly process large volumes of data, complex data, or data that changes frequently - tasks that humans struggle with. 

They cover many disciplines, like clinical, financial, strategic and operational.

To illustrate, below are a few computable knowledge building blocks that can be created on Apervita to construct powerful analytic solutions:


 Building Block


 Risk Scores

Build math based prediction models that can calculate a multitude of risks, such as claims denials, complications, readmission, severity, prognosis, and lifetime risk.

 Alert Rules

Alert rules triggered on predefined conditions which output scores, recommendations, interventions, messages and context.

 Screening Tools

Chained rules that monitor multiple aspects of a patient with a chronic disease such as BP control, med. mgmt., A1c control, lipid mgmt., examinations or vaccines.

 Clinical Intervention

Rules that identify care gaps and trigger intervention recommendations for clinicians or administrators to take action.

 Medication Optimization

Rules providing recommendations or alerts for alternate or complementary medications, medication interactions, dosing issues and titration opportunities.

 Care Pathways

Model care pathways with multiple knowledge assets, analytics, and chained analytics. Determine the state of an executed pathway.


Create industry defined or custom measures with numerators, denominators, inclusions and exclusions.


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