How TAGDit Works

VECTOR-PARAMETIZED HEALTH INFORMATION SYSTEM (VPHIS)

Multiple Stream Data Input

Quality Vector Layer Output

TAGDit receives data through multiple streams:

3rd party databases such as from CDC, WHO and other health organizations, census data, socioeconomic status, crime data, climate data, ecological data, industry data, pollution data and many others and output to TAGDit.
Electronic Health Record (EHR) databases such as FHIR/Google, which contains patient address and health information. This data is protected with the highest level of network encryption and security.

Intellipole Intelligent IoT lamp posts with built in sensors, and data input stations,
Personal health information on personal health and field medical devices
Information from different players in the health sector, amalgamating social networking data such as conversation threads, surveys, vote transfer, preferences and relationships.
The general public will also have read/write access to general information to improve public health information access.

TAGDit software then converges, normalizes and processes all data streams using advanced AI which it outputs to a 6D Black-Scholls processor for constructing the final quality vector layer. This construct a quality error band around any GIS datapoint. It can be visualized as a mapping layer correlated to the normative GIS data layer. Software control allows for flexible data exploration. Varying the control can compare the standard GIS data map with GIS data filtered through the quality vector layer, enabling public health decision makers to explore the data in a multitude of new ways.

TAGDit Measures Trust

Approved medical professionals will have write access to the database, where the information they post is correlated to the trust of their colleagues, creating a high quality database.

A liquid democracy algorithm called the transferable vote, found within the core TAGDit software encodes network social capital. Members of the platform can engage in dialogue with other members, post their comments and questions, or answer questions posted by others. If they can’t answer posted questions, they have the option to transfer their vote to an agent whom they believe can. Vote transfers correspond to the very common medical practice of referring another physician. A transferred vote can be considered a type of trust or “unit” of social capital. Currently, such referrals are qualitative and there is no way to compare trust levels between health professionals. By tracking vote transfers, the network can build up a picture of competencies, which can be encoded as a scaling factor to affect the Information Rank algorithm. Hence, the modified algorithm returns a much more transparent result. The TAGDit algorithm consists of Information Rank + Qualitative Information = Prioritized Results.

SEARCHING AND RATINGS

When a search query is entered, TAGDit sorts through all the content posted within the group’s database to find the most relevant and high quality results. These have been filtered through key words and user ratings. The champion on the specific topic can also be found, this is based on what relevant information they have inputted and the feedback received from the group on this content.

QUESTIONS

On TAGDit any member can post a question to their entire group. Each individual can then:

Answer by selecting one of the multi-choice options, and respond with a comment – if they more to add.
Or they can pass their vote on to a trusted member to represent them, by answering on their behalf.

By viewing the results of multi‑choice answers and the comments, the groups wants, needs and opinions are easy to overview.