CPS HealthNode™ Network

Our novel communications architecture, called the CPS HealthNode™ Network, employs the patented CP Split™ (CPS) software method. As described on the CP Split™ technology site, this architectural framework, coupled with a unique underlying technology, provides a simple, transparent way to transfer information securely and economically between any software programs and data repositories via asynchronous, publisher-subscriber, node-to-node networks.

The CPS HealthNode™ Network is arguably the least complex, least costly and most durable way for healthcare providers, patients and researchers to securely share information by:

  • Integrating, organizing, storing, exchanging and presenting large amounts of disparate data residing in dissimilar formats at widely scattered locations
  • Allowing any software programs to work together (i.e., interoperate)
  • Enabling collaborators in loosely coupled networks of collaborators to build, exchange, examine and refine information models used to analyze those data
  • Protecting patient health information.

What is a Node?

A node is a software program residing in a computer (including computerized device) that manages information transfer between two or more computers.

What are CP Split™ (CPS) HealthNodes?

CPS HealthNodes leverage the patented CP Split™ software method. This novel technology is arguably the least complex and most efficient way to organize, store, transmit and present large amounts of data. It uses automated templates in spreadsheet programs to produce and consume delimited data files (containing no formula, formatting or metadata) in a way that:

  • Provides meaningful, helpful information to all end-users (including providers, consumers, researchers and administrators) through the exchange, evolution and use of decision-support models
  • Speeds and simplifies the exchange of information between disparate data stores.

What is an Asynchronous, Publish/Subscribe,
Node-to-Node Network Architecture?

An asynchronous (asynch), publish/subscribe (pub/sub), node-to-node (N2N) architecture consists of a network of nodes in which publisher nodes produce and send information to subscriber nodes. The subscriber nodes then retrieve, store and present (display/printout) that information; they may also export data to databases and other data stores. This N2N information exchange process connects networks of nodes via the Internet. At one end of the connection, the publishing node must authorize the information transfer by authenticating that the subscribing node is allowed to receive the information. At the other end of the connection, each subscribing node must allow the publishing node to deposit the information in an accessible place.

This network architecture is asynchronous because a publishing node sends the information when it is ready, rather than waiting until the subscribing nodes signals it is ready to receive it. And rather than being a centralized architecture in which most communications are routed through a major central hub, it is a decentralized (federated/distributed) architecture in which any nodes can communicate with any other nodes, just like the telephone system works.

What happens when an Asynch, Pub/Sub,
N2N Network uses CPS HealthNodes?

An asynch, pub/sub, N2N network comprised of CPS HealthNodes follows the same process previously described—with one difference: They use the novel CP Split™ method to produce, consume and present data files using automated spreadsheet template software programs.

Publishing nodes use their CPS Publisher Templates to:

  • Retrieve data from the requisite data stores
  • Assemble the data in smart data structures that are stored in cost-efficient data files.
  • Ship the data to its subscribing nodes automatically by taking the data from the CPS Publisher Template, storing them in a simple, encrypted, delimited CPS Data File, and sending the file as an encrypted e-mail attachment (or transmit the file using other methods).

Upon delivery, the subscribing nodes retrieve and decrypt the CPS Data File, then use their corresponding CPS Subscriber Templates to:

  • Interpret the meaning of the CPS Data File’s contents based on the position of the data in the file, which is a completely unique method that minimizes file size and complexity (i.e., it avoids the overhead of sending metadata and markup tags with the actual data.
  • Render (present) the Data File’s contents in a report by applying formatting instructions to the data, and it can export the contents of the CPS Data File to any databases.

The data structures created through the CP Split™ method are “smart” because they organize data in spreadsheet cells in a meaningful/logical and preplanned uniform structure that:

  • Saves time Increases security, usability, flexibility and money
  • Reduces complexity
  • Increases security, usability, flexibility.

The advantages of this process include:

  • Providing an interoperable architecture that enables heterogeneous systems, across multiple platforms and applications, to exchange and use data.
  • Using a simple, transparent (human readable) and auditable data structure that enables people to easily understand the relationships between data, as well as clearly recognize data errors, by color coding the data, adding notes/annotations, and arranging the data into logical groupings.
  • Minimizing the time and effort required to convert the raw data into the display of useful information, such as graphs, by arranging the data into the necessary arrays (i.e., data are put into a “serialized order” in columns and rows) prior to storage and transmission.
  • Minimizing bandwidth requirement by greatly reducing data file size and complexity, so that the data can be transmitted in its most efficient form, i.e., in delimited formats (such as in CSV files) that contain no formatting instructions, markup tags, or programming code.
  • Eliminating the need to re-entry data, use “screen scrapers,” or do time-consuming data parsing and transformations because it preserves the numeric values of data instead of having to convert numbers to text and then back to numbers (or embed them in XML markup tags); that is, it keeps numeric content "live" so they are ready for reuse immediately.
  • Pre-processing the data so that there is no need for subscribing nodes to do database queries, XML parsing and XSLT transformations, and online analytic processing (OLAP), and other time-consuming operations. This saves time, increases security, and reduces complexity and hassle.
  • Enabling different audiences to receive different data from the same publishing node and have it rendered in particular ways based on what the subscribing nodes need for personalized reports.
  • Enabling a single subscribing node to receive data from multiple publishing nodes, which are easily integrated into composite reports.
  • Enabling a subscribing node to export any portions of the data it receives into different data stores.
  • Enabling changes to be made to the data and distributed as necessary, which is important when (a) editing out mistakes or updating sets of data, (b) creating and analyzing what-if scenarios, and (c) computing the same set of data using several analytic models because these activities may require somewhat different data.
  • Enabling data to be added to data sets distributed when necessary, which is important in collaborative situations when (a) different people must input data to complete a data set, (b) automated (unmanned) nodes supply data, and (c) several analytic models are used to compute the same set of data, and certain models require additional data.
  • Restricting portions of a data set from being accessed by particular nodes, which is important when different users with different roles require only a portion of a data set. This helps assure people get to see only the data they need to minimize information overload and protect data from being viewed by unauthorized persons.