1 / 17

Archived File

Archived File. The file below has been archived for historical reference purposes only. The content and links are no longer maintained and may be outdated. See the OER Public Archive Home Page for more details about archived files. Automated Referral Workflow System.

makana
Télécharger la présentation

Archived File

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Archived File The file below has been archived for historical reference purposes only. The content and links are no longer maintained and may be outdated. See the OER Public Archive Home Page for more details about archived files.

  2. Automated Referral Workflow System

  3. Current Referral Process Automated Referral Workflow System

  4. IRG Referral Methods Suggested by Prior Studies Automated Referral Workflow System

  5. Deployed February 2007 • Automated mining of PI requests • Referral suggestions based on machine learning • Decision support/workflow tool Automated Referral Workflow System

  6. Cover Letter Mining -- Approach • Fuzzy match to full study section names • Exact match to study section acronyms • No semantic analysis Automated Referral Workflow System

  7. Automated Letter Classifications • “High Confidence” • “MESH Study Section” • “Reduced Confidence” • “MI”, “Medical Imaging”, “MI” • “No SRG Requests” • No acronyms or names found Automated Referral Workflow System

  8. Cover Letter Mining Results • First electronic new unsolicited R01s (October 2007 Council) • 59% of applications included SRG request • Only 47% two years ago Automated Referral Workflow System

  9. Automated Classification of All Letters Automated Referral Workflow System

  10. How Accurate Are High Confidence Classifications? • 92% of applications reviewed by IRG identified in letter by ARWS • Consistent with data from existing human referral process Automated Referral Workflow System

  11. Cover Letter Mining Conclusions • Automated referral based on requests is feasible • More “High Confidence” letters needed • Better algorithms • Structured cover letters Automated Referral Workflow System

  12. Proposed Structured Cover Letter Please assign this application to the following: Institutes/Centers National Cancer Institute - NCI National Institute for Dental and Craniofacial Research – NIDCR Scientific Review Groups Molecular Oncogenesis Study Section – MONC Cancer Etiology Study Section – CE Please do not assign this application to the following: Scientific Review Groups Cancer Genetics Study Section – CG Automated Referral Workflow System

  13. Machine Learning Predictions • Applications without requests • Requests are rare for some mechanisms Automated Referral Workflow System

  14. IRG Assignment Prediction Automated Referral Workflow System

  15. Exit Ramp Automated Referral Workflow System

  16. Next Steps • IMPAC II interface is critical • Structured cover letters • Improved machine learning • More mechanisms • Benefits • Reduced referral staff • Review meetings 2-3 weeks earlier (or later receipt dates) Automated Referral Workflow System

  17. Acknowledgements • Support • Office of the Director • Extramural Affairs Working Group • ARWS Project Team • CSR Staff (Dipak Bhattacharyya, Eileen Bradley, Suzanne Fisher, Richard McKay, Richard Panniers, Laura Roman, Kalman Salata, Sean Tate) • Discovery Logic (Kirk Barden, Marty Brown, Mike Pollard, Greg Young) • IC Staff (Arthur Castle)

More Related