Fig 1.
Sample HealtheRx generated for a patient with cancer.
Fig 2.
Visualization of how a HealtheRx is Generated Using Data from a Patient Electronic Medical Record and the CommunityRx Software Algorithm.
Fig 3.
Interdisciplinary process and timeline of clinical and computational trial activities with data inputs and outputs.
Fig 4.
Average minutes per week, stratified by age agents spent doing activities during which they could use a self-care service listed on the HealtheRx; Chicago, Illinois 2016–2018.
Fig 5.
Exemplar instance of the evolution of three agents’ knowledge (Beta, β) of eight resources over time (λ of 0.991 used in this instance based on sensitivity analysis and model calibration previously reported in [39]); Chicago, IL 2016–2018.
Note: Each column (n = 3) represents a unique agent. Each row represents a unique resource (n = 8). Each black dot indicates the β scores (left y-axis) at in point in time in hours (bottom x-axis). Information dosing events (receipt of information about a given resource) that occurred during a given hour are indicated by vertical lines as: receipt of a HealtheRx (blue), receipt of information about resources from a social contact (green) and use of a resource (red).
Fig 6.
Visualization of network pathways through which agents, stratified by age (16–25 years = orange, 65+ years = purple) exchange information about resources; Chicago, Illinois 2016–2018.
Fig 7.
Differences in network degree distributions (distribution of total incoming and outgoing information pathways) as the rates of information exchanged are adjusted higher, black to red to blue; Chicago, IL 2016–2018.
Fig 8.
Simulation of the geographic spread of community resource information via (A) clinical dosing and (B) social dosing using the CRx agent-based model. Agents who received clinical dosing could also receive social dosing. The base layer of this map was obtained from Stamen Maps available at https://stamen.com/open-source/.