1. Faculty of Computer Science © 2006 CMPUT 605March 3, 2008 Concept-Based Electronic Health Records: Opportunities and Challenges S. Ebadollahi, S Chang, T. Mahmood, A Coden, A. Amir M. Tanenblatt 14th Annual ACM International Conference on Multimedia (2006) Amit Satsangi amit@cs.ualberta.ca

2. © 2006 Department of Computing Science CMPUT 605 Focus  ECG Video: document is not important; behavior of sub-organs like valves, ventricles, myocardium is  ECG –Text Report  sub organs, diagnosis  Efficient access to the elements of the content of the data ???  New Paradigm – Concept based Multimedia Medical Records

3. © 2006 Department of Computing Science CMPUT 605 Problems with the present system  Electronic Health Records (EHR) Data—mixed format: HIS for lab reports, ECG’s etc. RIS for reports generated after reviewing medical images, and PACS for diagnostic images.  Different Standards: HL7, DICOM, etc.  Information extraction regarding a single concept of interest (Right Atrium) is difficult  Hence the need for (re)organizing the health records at the information level

4. © 2006 Department of Computing Science CMPUT 605 Concept-Based Records Organization: Advantages  Goes beyond dealing with data at the document level  Caters to different categories of users of medical records – Physicians: Ejection fraction of left ventricle measured while reviewing the ECG. Ideally system should calculate this using quantification Algorithms. Should also be able to link it with the diagnosis reports, textbooks, research papers etc. – Students: Teaching files with history of medical cases + diagnostic images + medical journals + textbooks

5. © 2006 Department of Computing Science CMPUT 605 Concept-Based Records Organization: Advantages – Patients: Illustrated version of patient’s disease – Insurance companies: Prevent misuse of expensive tests (MRI) when not justified by the results of earlier, less expensive tests (EKG)  Timely and decision-enabling information extraction  It entails a better organization of medical records from the scratch in order to deliver all that is promised …

6. © 2006 Department of Computing Science CMPUT 605 Architecture  Analytic Engines —domain knowledge  Heart Chambers in Video  Parse diagnosis report  Relationships b’n concepts —ontologies (UMLS)  Is a, spatially/temporally/ functionally related to etc.

7. © 2006 Department of Computing Science CMPUT 605 Example

8. © 2006 Department of Computing Science CMPUT 605 Addendum  New information may need to be added  Graph Structure with Nodes as concepts and links are relationships between these concepts  Need federation of Ontologies – different concepts of interest in different domains  Multimedia content restructuring required – Vision, NLP etc.  Not a new way of analyzing data, but a novel way of organizing the medical records

9. © 2006 Department of Computing Science CMPUT 605 Case Study: Video Content Restructuring  Echocardiography – Imaging of the heart in several planes  Inherent spatio-temporal strcuture  Feature-extraction tools used to target areas of interest  Text snippets extracted from diagnosis report  Undirected graphical models used to learn the spatial arrangement of cardiac chambers

10. © 2006 Department of Computing Science CMPUT 605 Schematic

11. © 2006 Department of Computing Science CMPUT 605 Text Analytics for Cancer Pathology Reports  MedTAS (Medical Text Analysis System) was used  Several models – conceptually separate pieces of knowledge  Pieces of knowledge  Disease description, evaluation procedures etc.  4 sub-models: Tumor model, Specimen model, Lymph-node model and the disease model

12. © 2006 Department of Computing Science CMPUT 605 Text Analytics for Cancer Pathology Reports  Models are annotators (can be institution specific)  MedTAS built on IBMs Ustructured Information Management Architecture (UIMA). (Open Source)

13. © 2006 Department of Computing Science CMPUT 605 Models

14. © 2006 Department of Computing Science CMPUT 605 Potential Avenues  Three main issues — Determining the unifying architecture — Determining the concepts that need to be extracted — Development of robust Analytic engines  Testing & Feedback issues when such records in use  Seamless Integration with existing data

15. © 2006 Department of Computing Science CMPUT 605 Thank You For Your Attention!