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My Philosophy

The financial crisis in 07-08 was a global economic downturn sometimes compared to the Great Depression. In the year that followed, the U.S. government passed a spending bill outlining the American Recovery and Reinvestment Act of 2009 (ARRA) often referred to as the "stimulus package", was to spur the economy, invest in infrastructure, enhance healthcare, and other fiscal needs.

 

At the time, electronic health records (EHRs), were becoming increasingly implemented and ARRA underscored the investments by hospitals and clinical practices. Included under ARRA was the Health Information Technology for Economic and Clinical Health (HITECH Act). The total cost for ARRA is outside the scope of this statement, but the HITECH Act provided ~$35 billion to promote the adoption of health information technology, particularly EHRs.

 

Of this, approximately $26 billion was specifically designated for Medicare and Medicaid that aimed to incentivize healthcare providers to adopt and properly use EHR systems. Through the program called “Meaningful Use”, the government basically subsidized part of the cost of installing and using EHRs for those organizations.

 

The remaining funds were allocated to various other initiatives such as development of health information exchanges, workforce training, enhanced privacy, and compliance.

 

In the past 15+ years, EHRs have been universally adopted and a massive source of valuable data, not just for clinical care, but also research. This spans from outcomes analysis to predictive models, to artificial intelligence. Structured data within EHRs are now a treasure trove of data for clinical care and research. Even unstructured data is becoming minable

 

Healthcare data, when appropriately integrated with non-clinical data such as geographic encoding, is driving newer concepts called social determinants of health that are focused on public health and welfare.

 

Recent standards such as the HL7 Fast Healthcare Interoperability Resources (FHIR) allow universal data exchange between data silos using standardized clinical terminology and ubiquitous web technology, that is, Application Programming Interfaces (APIs).

 

Artificial intelligence and machine learning algorithms are redefining how we (hopefully) appropriately and best use healthcare data for new insights. Furthermore, cloud-based storage and computing power have begun to leverage vast, centralized datastores which is of particular interest to the National Institutes of Health (NIH).

 

Data liquidity represents how data flows through an ecosystem to enable these key concepts that drive success. It is imperative these data flows are analyzed and monitored from the local level to the higher-level architectures.

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