This is the Clincial Reasoning Platform central repository. It is designed to be a launch point for content and all the specific components and additional artifacts that enable and make Clinical Reasoning an extensible platform can be found. For general awareness, any Clinical Reasoning components ARE ONLY intended to be run within resources that leverage the needed upstream software or equivalent licensed software as defined below within the content. In addition, this effort through its open source licensing selection ensures appropriate protection for all parties. Red Hat Healthcare is doing this to help the broader healthcare community and while a market within Red Hat this is an independant effort.
Any system or platform needs data, since the mainframe started producing applications fifty plus ago data has been powered by data. As systems have improved the way systems and platforms can leverage data has also improved. For decades data was file based only and some critical systems are based on this, Then, in the early 1970's a series of universities and then companies started working on technologies named Relational Database Management Systems (RDBMS). Companies like IBM, Oracle, Sybase and others.
Over the next few decades technology in general continued to improve and as the PC and Server markets become a standard in the enterprise and with consumers RDBMS platforms and their capabilities continued to expand and grow. RDBMS are all about consistency in data structure. This growth was powered by business needs based on what having data was driving. In the 80's the SQL capability came into being and that started to even more rapidly accelerate even more capabilities.
As the 80's took effect with now many RDBMS options and SQL capabilities there was a need for businesses to combine many large data tiers, welcoming in the need for Data Warehouses. Data Warehouses enabled organization to do MPP (Massive Parallel Processing) of information and move data from many sources into one place and in a more cohesive manner respond to business demands centrally. Data Warehouses were one of many ways that the technical industry helped business address the demands on information, there also started to be a industry of reporting tools that came into being.
Over the next twenty years the technical industry around RDBMS, Reporting and Analytics and Data Warehouse capabilities continued to grow and expand exponentially. However, as the Web came into the main stream and its usage in the late 90's took a foot hold it was clear that new data innovations would be needed. NoSQL and Big Data were the next capabilities to come about in the data world. Big Data and NoSQL are used to fill a few voids. The void Big Data files is dealing with massively large and complex data sets. Behind Big Data's industry success are often the AI, ML and NLP capabilities that companies use the technologies to enable. The void NoSQL helps with is a different perspective on data, it's focus and driver is all about the capability to store unstructured data and retrieve it for analysis. Both Big Data and NoSQL are driven almost exclusively by the open source movement as well. Big Data and NoSQL adoption have continued to grow through the early 2000's and started exploding in about 2010 till today. This explosion is driven by new business needs and drivers and fueled by every industry.
As with many industries healthcare's electricity is clinical and/or financial information specific to patient care. This is being driven because there are many forces from a competitive, legislative or reimbursement perspective that are applying pressure across the entire healthcare market continuum. Over the last decade these forces have continued to both grow and morph, sometimes in exponential ways. Some of the external forces include state and federal mandates continuing to expand, competitors that are also partners, third party business leveraging various aspects of both clinical and financial data, financial pressures from areas such as reimbursement levels dropping while requirements around care and information continue to increase, technical demonstration of value to business stakeholders, and areas like value based care continuing to expand. With all these forces the one thing healthcare sees consistently is their main means to address all these forces is found with their data. Because of the extensive amount of data involved within the healthcare organizations they continue to procure or are transforming to develop and deliver solutions. Irrespective of healthcare organizations business model(s): buy, build or a combination of buy and build the key remains that healthcare’s effectiveness is driven by data driven capabilities from more accurate access to information in near real-time that can give them intelligence and enable them to have better platforms and capabilities in near real time.