Linked Data for Local Search: Helping patients find their way around a geographically complex academic health center uri icon


  • Oregon Health and Science University’s (OHSU) main and expansion campuses are respectively situated at the top and eastern base of Portland's Marquam Hill, a beautiful but geographically challenging location that can present significant obstacles for patients finding their way to appointments with OHSU healthcare providers. These wayfinding challenges are exacerbated by a lack of search engine exposure to detailed structured data describing the university’s campuses, buildings, clinics, satellite locations, and providers, which also hampers the ability of both current and future patients to find information about seeking healthcare services at the university in general. OHSU commits significant resources to assisting patients find their way around once they arrive at a campus, including parking valets and information concierges, but until recently there had not been a focus on the quality and accuracy of information about OHSU entities found on the web. In 2016, OHSU launched the Project to Inform Local Search, also known as PILS, a collaborative effort between the university’s Digital Engagement and Digital Strategy teams and the OHSU Library to implement a semantic data model that would allow the university to canonically describe all of its campuses, buildings, locations, clinics, and providers in order to provide accurate and trustworthy structured data about these entities to search engines, map providers, healthcare review sites, and other consumers of structured and linked data on the web. The ultimate goal of the project is to enhance patient experience around seeking information on the web about the university’s healthcare services, with a particular focus on the structured data that would assist patients in getting to appointments. This presentation will describe some of the specific local search issues OHSU set out to resolve, the background research conducted to develop competency questions to inform the creation of the model, the implementation of the semantic model, the data integration approach, the project deliverables, and potential future expansions and applications of the model. The PILS collaborators hope our work might inspire similar efforts at other academic health centers

publication date

  • August 4, 2017