- Click here for Overview and Instructions
This application simulates the use of a network graph to dispense drugs at hospitals based on the pharmaceutical specialties available for prescription for a particular ailment along with the prices in an effort to contain costs.
According to the Centers for Medicare & Medicaid Services, hospitals in the United States spend a combined total of $936.9 billion each year. A great portion of this budget is spent on the pharmaceutical drugs distributed to patients. In most cases, hospitals do not have a system in place to easily compare drugs in an effort to cut costs.
This application illustrates how network graphs can be used to enable hospitals to effortlessly compare drugs based on their cost, posology, active ingredients, and function. The three anatomical classifications I explored belong to the digestive and metabolic, cardiovascular, and blood and blood-forming systems. The network graph can be used to navigate from an ‘Anatomical Classification’ to the actual drug with dosing specific details.
- The networked is centered around the red "Anatomical Classification" node. The network tree can be navigated to locate a suitable drug to dispense based on cost. I was also thinking of adding the stock level for a future release of this illustration.
- Hover the mouse over any node to see the node name. The search box on the left panel can be used to search the network graph.
- Click any node to highlight its local neighborhood. Information about this node's incoming and outgoing connections will appear in a panel on the right. If a drug node is selected, its price will be displayed. Click on "Return to the full network" if the right panel is open to go back to the full network.
- Click and drag within the network graph section to reposition the graph. The zoom icons on the bottom of the screen can be used to zoom in and out. The scroll wheel on the mouse can also be used to zoom in and out as well.
I would love to hear back from you. Please email me your feedback. Thank you so much – Mayha.