Abstract: A computer navigation system that represents categories and sub-categories of material with functionally labeled objects connected in a hierarchical organization. Descending nodes represent more detailed subsets of material. Hovering the computer mouse over a node displays thumbnails of the items available under it, enabling the user to search for items without opening the respective pages, transforming a literal or verbal data filing and retrieval process into a more intuitive and easy to use visual one. Once the user hovers the computer mouse over the node with the desired item and sees its thumbnail the user clicks on the node, opening the screen from which the user can access that item (or upload a similar one), or clicks on the thumbnail to access the item directly. In addition, the system employs algorithms to group items into various categories and sub-categories and assigns them to the appropriate nodes.

Abstract: A method and/or system that is a navigation engine with a visual interface that evaluates wellness programs based on their effect on the participants' healthcare costs and absences from work, and then calculates the programs' ROIs (Returns on Investment). The engine can also generate a list of employees and/or other individuals who could benefit from a wellness program but who are not participating, and the impact on the healthcare costs, medically-related absence costs and ROI if they did. In addition, the engine can recommend wellness and safety programs that have not been implemented, but should be, along with the impact such programs could have on the medical and pharmacy claims and medically-related absences.

Abstract: A computer navigation system that represents categories and sub-categories of material with functionally labeled objects connected in a hierarchical organization. Descending nodes represent more detailed subsets of material. Hovering the computer mouse over a node displays thumbnails of the items available under it, enabling the user to search for items without opening the respective pages, transforming a literal or verbal data filing and retrieval process into a more intuitive and easy to use visual one. Once the user hovers the computer mouse over the node with the desired item and sees its thumbnail the user clicks on the node, opening the screen from which the user can access that item (or upload a similar one), or clicks on the thumbnail to access the item directly. In addition, the system employs algorithms to group items into various categories and sub-categories and assigns them to the appropriate nodes.

Abstract: A navigation engine for medical and/or pharmacy claims (in combination with employer human resource records or on a stand-alone basis) that quantifies healthcare outcomes and ranks healthcare providers and other healthcare items by root diagnosis based on their overall average claims per healthy day (i.e., clinical efficiency). Claims per healthy day is the adjusted claims cost per day to keep a patient healthy (or in the case of an employer, keep an employee at work), so the lower, the better. The navigation engine uses drop-down menus and/or similar techniques that require the user to select a root diagnosis on which to search, as well as other variables (e.g., provider category, geographic proximity, in-network versus in or out of network, etc.), turning an open-ended question, e.g.

Abstract: A machine learning system and method utilizing artificial intelligence that improves the provisioning of healthcare and reduces the total cost of healthcare and lost productivity. The approach creates a quantifiable assessment of quality and a ranking number measuring a provider's quality of healthcare services. The machine learning system makes a determination of quality based on a clinical evaluation database, an employee related time and attendance database, and a costing database. The system analyzes how quickly a provider returns an employee to work at or near pre-absence productivity and at what cost. The system creates a ranking number that provides employees with a comparison of providers. Employees may then be incentivized to seek high value providers.

Abstract: A computer search engine for medical and/or pharmacy claims (in combination with employer human resource records or on a stand-alone basis) that ranks healthcare providers and/or intervention strategies by root diagnosis based upon their overall average outcome efficiency. Outcome efficiency is the adjusted cost per day to keep a patient functional (or in the case of an employer, keep an employee at work), so the lower the outcome efficiency the better. The search engine uses drop-down menus and/or similar techniques that require the user to select a root diagnosis on which to search, as well as other variables (e.g. provider category, geographic proximity, in-network versus in or out of network, etc.), turning an open-ended question, e.g.

Abstract: A computer navigation system that represents categories and sub-categories of material with functionally labeled objects connected in a hierarchical organization. Descending nodes represent more detailed subsets of material. Hovering the computer mouse over a node displays thumbnails of the items available under it, enabling the user to search for items without opening the respective pages, transforming a literal or verbal data filing and retrieval process into a more intuitive and easy to use visual one. Once the user hovers the computer mouse over the node with the desired item and sees its thumbnail the user clicks on the node, opening the screen from which the user can access that item (or upload a similar one), or clicks on the thumbnail to access the item directly. In addition, the system employs algorithms to group items into various categories and sub-categories and assigns them to the appropriate nodes.

Abstract: A machine learning system and method utilizing artificial intelligence improves the provisioning of healthcare and reduces the total cost of healthcare and lost productivity. The approach creates a quantifiable assessment of quality and a ranking number measuring a provider's quality of healthcare services. The machine learning system makes a determination of quality based on a clinical evaluation database, an employee related time and attendance database, and a costing database. The system analyzes how quickly a provider returns an employee to work at or near pre-absence productivity and at what cost. The system creates a ranking number that provides employees with a comparison of providers. Employees may then be incentivized to seek high value providers.