RHINoVision looks at another publicly available database, the International Master Facility List that was discussed in the Nature.com article, “A spatial database of health facilities managed by the public health sector in sub Saharan Africa”: Here is the link to the RHINoVision MFL DSS: snisnet.net/MFLDSS/MFLDSS.php
The RHINoVision MFL DSS global dashboard has visualizations for the summary statistics of available data, and you can choose a country and get the country dashboard with that countries summary statistics and data. The list of facilities can also be filtered and sorted by the categories available in the database (facility name, type, location, ownership and source of Lat/Long). You can also generate a (Google) map view of the chosen country and filtered characteristics.
Link to the Nature.com Article:
RHINoVision is both an innovative concept for visualization of routine health information and a software application that builds on the methodology outlined in the MEASURE Evaluation “Building a Web-Based Decision Support System” working paper. The RHINoVision open source Decision Support System presents two graphs in tandem that play to the strengths of routine health information system (RHIS) data, in that they empower users with simultaneous visualization of data over time and geography. RHINoVision features powerful multivariate visualizations for triangulation of data from different programs and data sources and for “drill down functionality” that empowers users to visualize patterns in the underlying data.
The Rhino is the spirit animal of the Routine Health Information (RHINo) Network. But Rhinos don’t see very well. So the birds gather on their backs and the Rhinos use the birds to help see. That’s RHINoVision. Not only do the birds help the Rhinos monitor and evaluate their situation, the birds can examine the underlying data to improve the quality of the Rhino’s skin by keeping them clean and bug-free. The birds also fly around and have a “birds-eye-view” of everything around, helping Rhinos triangulate data across various routine and non-routine sources, thus helping to verify that their data is valid and credible. RHINoVision adapts the principles of Biomimicry to empower users of routine health information.
RHINoVision Decision Support Systems vastly improve the data use capacity of users of routine health information, both for validating data quality and for program indicator monitoring. The RHINoVision HIV/AIDS DSS can be used by country PEPFAR programs to monitor their results compared to targets, as well as drill down into the sub-national data, and the dis-aggregated data (age, sex, etc.). The HIV/AIDS DSS can also be used for improving data quality, as it exposes numerous data quality issues, for example, missing years of data and mis-classification of DSD/TA support categories can be seen. Another demonstration of the RHINoVision functionality, using national tuberculosis data for 23 USAID Priority Countries (data source: https://www.who.int/tb/country/data/download/en/) can be found in the TB Decision Support System (snisnet.net/TBDSS/TBDSS.php).TB stakeholders in national governments, ministries of health, and national TB programs can use it to make informed decisions on USAID TB interventions.
For further information, contact:
Michael Edwards, PhD, MPH
RHINoVision Decision Support Systems