Markets Served

Value Based and Population Health

These programs require melding of structured and unstructured data, integration with multiple providers across the continuum of care, and permissioned disclosure of healthcare data. HSBlox has developed solutions to these challenges using distributed ledger technology and machine-learning approaches.  

Patient Permissioned Data

Patient needs are not being addressed with respect to personal medical records and control of patient data and integration of patient-generated data is not easily done. HSBlox solutions put the patient in charge.   

Analytics

The healthcare system possesses an immense amount of rich, yet disparate data. HSBlox is applying its integration tools and machine-learning algorithms to aggregate, analyze, and report on data with unprecedented accuracy and insight.  

Chain of Custody / Supply Chain

Existing logistics and chain of custody processes lack integration, visibility and interoperability amongst systems and stakeholders, and are susceptible to losses and delays. HSBlox solutions use DLT technology to track and trace products and samples through clinical trials, supply chains and distribution networks while providing stakeholders and trading partners with precise and real time data.  

White Papers

Application of Blockchain in Clinical Research

In this paper we discuss how blockchain can generate real time access to lab and trial data for Pharma and CRO decision makers and permissioned stakeholders.

This is critical for Pharma companies whose success is defined by the breadth of portfolio and ability to maximize its value, including time to market.

We describe our project with a leading CRO tracking and sharing sample data using Digital Sample Manager™ - our chain of custody solution. 

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Machine Learning and Blockchain in Population Health

The volume of healthcare data continues to increase exponentially—burdening healthcare organizations with the management of disparate data, interoperability challenges between source systems and the complexities of unstructured data.

Machine learning can be used to build models for patient risk scores, cost prediction and patient behavior, and tap into unstructured data, incorporate SDOH and IoT edge data, as well as reconcile with structured data.

Blockchain generates auditability and traceability between stakeholders. With a single source of truth that reduces errors and the need for reconciliation, it addresses patient privacy concerns and population health data requirements. 

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