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Multimodal User Interface with Natural Language Classification for Clinicians At Point of Care

Multimodal User Interface with Natural Language Classification for Clinicians At Point of Care. Health Informatics Showcase. Sponsors: NCCH - Donna Truran Microsoft - Steven Edwards. Peter Budd. A Language Model of Health Information. >80% of information of interest is language.

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Multimodal User Interface with Natural Language Classification for Clinicians At Point of Care

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  1. Multimodal User Interface with Natural Language Classification for Clinicians At Point of Care Health Informatics Showcase Sponsors: NCCH - Donna Truran Microsoft - Steven Edwards Peter Budd

  2. A Language Model of Health Information • >80% of information of interest is language. • Patients and clinicians use language for > 90% of information exchange. • An EMR should be more like a document than a database record. • Data Capture is a language processing problem more than a form filling problem

  3. Purpose of Hospital Information Systems • Retrieve patient records for clinicians • Provide data to answer research questions • Provide data to answer Management questions • Provide clinical alerts for critical incidents • Provide decision support for patient care management plan • Provide auditing of patient care

  4. Data Analytics • The primary purpose of HIS is to provide extensive support for patient care, • It is not for the medico legal protection of clinicians interests • Data Analytics should be the fundamental objective of a HIS • The storage repository has more in common with a Content Management System than a relational database IS. • Language should be reduced to a canonical form – SNOMED CT

  5. Data Entry - Objectives • Mimic the workplace processing as closely as possible • Identify text as the primary content • Make canonical encoding as automatic as possible • Make canonical encoding as hidden from view as necessary • Maximise flexibility in data entry modes

  6. Technology Strategy • Multimodal Interface • Developed on the Tablet PC • Handwriting & Drawing Capabilities • Sub-vocal microphones for speech input • Designed to closely mimic “real” paper forms • Generic Form Generation • Able to be localised for individual hospitals • Automatically classify Natural Language • Classify free text into SNOMED-CT ontology

  7. Top Level Overview Form Generator Clinician Token Matcher Augmented Lexicon & Standard Lexicon Interface Database

  8. Token Matching • Phase 1 • Currently implemented • Matching based off sequence runs of medical terms • Adjacent words compared against each other • Match with most words used chosen as optimal match • SNOMED-CT Description table used; Multiple descriptions map to the same concept

  9. Token Matching • Phase 2 • To be implemented as future work • If bad matches are found, words close in spelling may be used to accommodate mistakes in the handwriting or speech recognition • Matching algorithm allows inconsistencies/ missing elements in the input • Uses language knowledge to fill in the gaps

  10. Token Matching • Phase 3 • Also not yet implemented • Uses sophisticated Natural Language Processing techniques to break sentences into “clumps” • Token Matching is then run on the clumps • Allows the negation of SNOMED terms based off sentence clumps

  11. Form Generation • Necessary attributes of the form are extracted out into an XML format • Form generated “on-the-fly” at program runtime • Allows hospitals to have non-technical staff use interface generator software to localize standard forms or create their own • Output into standard XML for saving into Database

  12. Form Generation • Next Phase of Implementation • Form can be loaded pre-filled or seeded with data based off statistically average usage • Allow multiple clinicians (Doctors and Nurses) access to the same form at the same time (from multiple Tablet PCs) to speed up data entry and reduce duplication • Add speech recognition and video capture to the interface

  13. Conclusion • Project outcomes • User Interface was created which closely mimics actual forms currently used in the workplace • Automatically classifies natural language into a medical ontology • Performance issues • Classification runs in acceptable time as a background process • Form Generation runs in pseudo-real time • Time for form generation well inside time required to pick up a real paper form

  14. Current Progress • Building an ED information system based on this model • Using Process diagram collated from 3 month study at Westmead ED • Subject of ARC Linkage grant application with Sydney West Area Health Service

  15. Questions

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