Enhancing information retrieval in electronic health records through collaborative filtering
When we consider buying a book on Amazon’s Website, we often benefit from items listed in a section called “Customers also viewed.” These recommendations, generated by a method called collaborative filtering (CF), suggest items of possible interest based on what other customers have viewed and purchased. However, when clinicians search the electronic health record (EHR) with regard to a particular patient problem, the EHR does not make suggestions for potentially useful information. Instead, it requires clinicians to go through the same manual, cumbersome and laborious process of searching for and retrieving information for similar patients/problems every single time. This limitation is magnified in high-risk situations, such as managing chest pain in the emergency department (ED). The goal of this project is to implement and evaluate CF as a method to improve information retrieval from EHRs and reduce cognitive overload. The central hypothesis of our proposal is that CF will (1) help clinicians retrieve and review the right patient information more efficiently and effectively than current methods; and (2) score higher on usefulness and ease-of-use than current EHRs. We will implement our CF algorithms in CareWeb Plus, a SMART-on-FHIR app we are currently building to integrate relevant information from the Indiana Network for Patient Care (INPC), Indiana’s major health information exchange, with the ED workflow in Cerner/Epic.