SHAARPEC healthcare

Data-driven management

SHAARPEC creates a foundation for:

  • Identifying opportunities for improvement at the system level through variability analysis of utilization, outcomes, cost for populations of interest as well as by quantifying the variability
  • Evaluating and prioritizing proposed new initiatives for process change based on the likely impact on outcomes, cost, social equity etc.

Personalized precision medicine

  • Evidence-based guidelines – working at the patient and population level to determine which patients meet inclusion criteria for which evidence-based guidelines, tracking compliance with the diagnostic and treatment recommendations for each guideline to determine which patient’s care is compliant, providing feedback to physicians about which patient’s care could be improved
  • AI-based machine learning predictive modeling – determine which patients are at risk for specific health outcomes of interest so that they can be started on proven treatments or preventative strategies earlier, thereby improving outcomes and reducing downstream healthcare system expenditures


Privacy within SHAARPEC is protected by hosting the platform and data within the healthcare system and pseudo-anonymizing the clinical data.

Research and benchmarking are made feasible without breaching GDPR and HIPAA by applying several techniques, such as

  • Secure multiparty computation
  • Homomorphic encryption
  • Federated learning

Grouped together, we call these techniques Base Jumping. Base Jumping makes it possible to run queries across all systems and return the results to one centralized node, while the data itself stays within the local healthcare system.

This allows not only for benchmarking between the systems, but also faster and more accurate training of AM/machine learning algorithms.