BlueLENS, better information for healthcare professionals - overview
Care management is critically dependent on collecting and providing the right information, to the right people, at the right time across the care continuum. This information is multi-sectorial, comprising medical, psychological, behavioural and social factors, while the "right people" come from a variety of disciplines (nurses, doctors, social workers, care assistants, care managers), operating at diverse points of care (referral, intake, discharge across healthcare and social care providers).
BlueLens gives care workers a customizable, context-sensitive and queryable 360-degree view of a patient, including medical and demographic information, social interactions, services received and more. It also allows care professionals to insert notes that are automatically classified and organized in this 360-degree view.
(The UX design is based on an original concept by Saar Byrne, Ciara Layden and Ryan Baker.)
BlueLENS is customizable to a variety of roles/situations. We present it in the context of a care assistant, Maria, newly assigned to an elderly client, Teresa Taylor.
Maria is visiting Teresa for the first time and needs to get an overview of her situation. She gains insight on her family situation through social media analytics and Watson Personality Insights, adds new notes, which are categorized using the Natural Language Classifier and are sentiment analyzed by AlchemyAPI, She is exploring the current information on the responsive UI, based on various available lenses, designed to re-rank UI elements based on information needs.
She can pose questions, which are answered using QuerioDALI (to provide question answering and semantic search across Linked Data sources). QuerioDALI uses assets from the Data Lake (including WordNet and DBPedia), information extracted using DALI (including biomedical open Linked Data sources such as drugbank, side effects, diseasome and the 211 taxonomy, and enterprise data for smarter care), in addition to using functionality from the Watson NLP pipeline. She is adding a voice note, using Watson Speech-to-Text. The system is recommending tags and people to notify and the note is added to the information about Teresa.|