Recovery Exploration Technologies Inc.

Carl Bate Tulley

David A Epstein

Jennifer May Lee

Wian Stipp

David Robinson

Matthew McSorley

Robert Derward Rogers

Michelle S Keller

Fady Nakhla

Thomas Unger

Arthur Böök

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

Translation of medical evidence into computational evidence and applications thereof

A next-best-action system includes real-time operation and off-line training and processes. During off-line training databases of medical conditions and associated diagnostic factors, tests and treatments are used to create models used for later natural language processing of input electronic text. Diagnostic factors are ranked according to the probability that they indicate conditions and are stored in a matrix. Treatments corresponding to conditions are also stored in a matrix. During real-time operation electronic text is received from patient history of an EHR system, from a transcribed conversation between a physician and a patient, or from input that the physician makes in the EHR system or in an overlaid diagnostic user interface. The electronic text is processed by an NLP pipeline that derives clinical diagnostic factors and test results for the patient. The factors and test results are matched against the matrix of factors to produce a list of likely conditions, along with an ordered list of factors and tests that are either unknown or not yet performed for that patient.

Augmented intelligence for next-best-action in patient care

Growing community of inventors

San Fransico, CA, United States of America

Carl Bate Tulley