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Computers in Healthcare

Computers in Healthcare. Jinbo Bi Department of Computer Science and Engineering Connecticut Institute for Clinical and Translational Research University of Connecticut. Presented at UConn Engr 1000 11 9t h , 2012. HEALTHCARE – KNOWLEDGE OVERLOADED. /25. HEALTHCARE – DATA OVERLOADED.

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Computers in Healthcare

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  1. Computers in Healthcare JinboBi Department of Computer Science and Engineering Connecticut Institute for Clinical and Translational Research University of Connecticut Presented at UConn Engr 1000 11 9th, 2012

  2. HEALTHCARE – KNOWLEDGE OVERLOADED /25

  3. HEALTHCARE – DATA OVERLOADED /25

  4. COMPUTER SCIENCE TECHNIQUES – CRITICAL Known knowledge Newly-published discoveries Computing Techniques /25

  5. MEDICAL INFORMATICS becomes more and more important and indispensible due to • Aging population • Ever-increasing cost of delivering health care • Outbreaks of emerging infectious diseases • Availability and ubiquity of electronic health records • Large quantity of clinical data consisting of heterogeneous formats • Many others Let us look at 3 concrete examples … … /25

  6. APPLICATION 1: MEDICAL IMAGE INTERPRETATION Challenges: • Massive image size • Time-consuming to analyze • Difficult to interpret /25

  7. COMPUTER AIDED DIAGNOSIS (CAD) • CAD is an interdisciplinary technology combining elements of artificial intelligence and image processing with radiology • It provides doctors a “second opinion” for image interpretation radiologist CAD system Radiology image input CAD findings highlighted in red /25

  8. LungCAD – DETECTING LUNG CANCER LungCAD – a computer software product of Siemens Medical Solutions /25

  9. SUCCESSFUL CAD SYSTEMS LungCAD: (FDA Approval) multi-center Multi-Reader Multi-Case (MRMC) retrospective study to assess incremental value of LungCAD in assisting 17 general radiologists to detect pulmonary nodules Area under receiver operating curve with and without CAD for 17 readers Average nonparametric ROC curve of all 17 readers with and without CAD /25

  10. TECHNIQUE SUMMARY Image processing Machine learning Algorithm complexity Computer vision Mathematical programming Statistics Linear algebra Calculus Software Engineering Data structures Computer programming databases /25

  11. APPLICATION 2: TRAUMA PATIENT CARE Major Hemorrhage? Respiratory Compromise? Real-Time Data Processing Integrated Decision Reliable Data Decision Outputs Traumatic Brain Injury? Mortality? /25

  12. TECHNIQUE SUMMARY Numerical analysis Signal processing Classification Computer Architecture Digital logic design Linear algebra Calculus Software Engineering Data structures & Algorithms Computer programming databases /25

  13. APPLICATION 3: QUALITY REPORTING • Captures a provider’s compliance with accepted practices to improve the quality of care • Get providers prepared for “Pay for Performance” • Defined by external entities HQA, JCAHO, and CMS • Measures based on information traditionally stored in clinical notes /25

  14. CHART ABSTRACTION FOR QUALITY REPORTING Manual Chart Abstraction Clinical notes, computerized patient data ? Automatic Chart Abstraction /25

  15. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... PATIENT RECORDS heart failure diabetes Insurance Look up ICD-9 codes Statistics reimbursement /25

  16. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... PATIENT RECORDS heart failure diabetes Insurance Look up ICD-9 codes Statistics reimbursement /25

  17. AUTOMATIC CHART ABSTRACTION ? /25

  18. VALIDATION OF OUR AUTOMATED APPROACH • Databases from a hospital that deals with heart disease • Patient records of two quarters in 2005 • A patient cohort of 325 patients based on billing codes • Automated system completely blinded to manual results • Performance compared on the agreement of automatic results and manual reading results /25

  19. AGREEMENT BETWEEN MANUAL & AUTOAMTED Overall 96% /25

  20. QUALITY IMPROVEMENT Less than 30 Days Automatic /25

  21. TECHNIQUE SUMMARY Natural Language Processing Machine learning Data mining Knowledge representation Statistics Linear algebra Calculus Software Engineering Data structures & Algorithms Computer programming databases /25

  22. 2000 1000 0 100 80 60 710 720 730 740 time (s) 0 20 40 60 80 bpm MANY MORE APPLICATYION AREAS • Patient care management : trauma, diabetes, stroke, cancer, etc. • Genome linkage analysis to identify genes for cancer, substance dependence, cardiovascular disease • Therapy optimization • Automatic meta-review • Personalized medicine • E-heatlhcare • ….. ….. New Era of Medical Informatics hemorrhage control respiratory rate (RR) /25

  23. Thanks for your attendance /25

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