The RAHN™ architecture accelerates value by delivering the following set of benefits and advantages:
- Provides global interoperability. Enables heterogeneous systems to share data across disparate platforms, applications and data silos for global interoperability worldwide. This
saves time, money and hassle when exchanging information in loosely-coupled networks of people and machines. It allows fluid data exchange across organizational boundaries and between disparate data
stores (e.g., transmitting data between incompatible EMRs, EHRs and PHRs).
- Requires no infrastructural build-out. Saves money, time, and hassle by using the same infrastructure that currently exists. All that’s required is an e-mail account and
- Requires minimal bandwidth and Internet access. Minimizes bandwidth requirements by transmitting RAHN™ data files in their most efficient form, i.e., in delimited formats (such
as simple spreadsheet and CSV files) that contain no formatting instructions, markup tags, or programming code. This means people with dial-up modems can use the system to send and receive data
(though large graphic files require greater bandwidth). It also eliminates the need for continuous Internet access and works well even when the Internet is disrupted and when traffic or latency is
- Operates in disaster situations. Can exchange patient data in emergency disaster situations where command & control units and first responders (fire fighters, police, EMTs,
trauma center staff, etc.) must communicate quickly and effectively even when they each have different data needs and when the Internet is unreliable. This includes overcoming the "last mile
problem"—which refers to the challenge to provide connectivity to all end-users' devices at all locations due to bandwidth mismatches and other connectivity constraints—by providing multifaceted
asynchronous communications capability that includes radio, land line, and satellite communication options.
- Prevents and single point of failure. Eliminates the single point of failure problem of centralized systems because RAHN™ nodes are connected in a decentralized manner, with one
node to connected another in pairs (node-to-node).
- Enables collaborative decision-making. Supports an automated decision-support system in which a "loosely-coupled decision network" of people from multiple locations and with
different roles collaborates to make decisions beyond the knowledge or skills of any individual. Its data transformation and universal translation capabilities accommodate the diverse information
needs and preferences of the participants in a way that reduces misunderstandings due to regional, departmental or cultural differences.
- Performs biosurveillance and ongoing evidence-based research. Can automatically send certain data concerning communicable diseases and other biosurveillance data to and from the
Center for Disease Control (CDC) via nodes connected to providers' EHRs/EMRs, patients' PHRs and the CDC's databases. In a similar manner, can also automatically send specified data to disease
registries and other research-based data warehouses for researcher to use in developing evidence-based guidelines (including protocols, clinical pathways, suggested diagnostic and treatment
procedures, etc.). In addition, present the guidelines as needed to the end-user and then send feedback to the researchers as the guidelines effectiveness and cost.
- Speeds and simplifies data entry. Presents forms and reports that ease the burden of complex workflow processes.
- Speeds and simplifies information presentation. Minimizes the time, effort and cost of converting raw data into the display of useful information (such as graphs) by
pre-arranging the data into the necessary arrays (i.e., by putting the data into a “serialized order” in columns and rows) in the RAHN™ data files prior to storing and transmitting the files.
- Enables immediate presentation offline. Eliminates the need for subscribing nodes to do database queries, XML parsing and XSLT transformations, online analytic processing (OLAP),
and other time-consuming operations because they have all been done prior to producing RAHN™ data files. Data presentation is therefore immediate and can be done offline.
- Creates personalized reports for different audiences. Enables 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. In fact, an unlimited variety of models can be used to create different reports; each model can have different user interfaces, use
different data sets, implement different analytic/computational rules, and present informaiton via different types of reports.
- Creates composite reports for longitudinal health records. Enables a single subscribing node to receive RAHN™ data files from multiple publishing nodes and integrate them into
composite reports. This is the basis for building longitudinal, cross-disciplinary health records able to manage, in a single file, every conceivable piece of health-related data for a person’s
- Enables data decomposition. Enables a node to export any portions of the data it receives into different data stores via RAHN™ data file decompositing. This data decompositing
process also increase security by splitting a patient’s identifiers from one’s clinical data when transmitting RAHN™ data files.
- Prevents access to restricted data. Restricts particular nodes for accessing portions of a RAHN™ data file, 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.
- Allows data files to be modified and passed along. Enables authorized people and machines to make changes and additions to the contents of RAHN™ data files as they pass from node
to node via a store & forward approach.