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 t h, 2012

HEALTHCARE – KNOWLEDGE OVERLOADED 2 /25

HEALTHCARE – DATA OVERLOADED 3 /25

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

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 … … 5 /25

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

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

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

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 9 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

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

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

TECHNIQUE SUMMARY 14 /25 Signal processing Classification Numerical analysis Calculus Linear algebra Computer programming databases Data structures & Algorithms Software Engineering Digital logic design Computer Architecture

APPLICATION 3 : QUALITY REPORTING 15  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

CHART ABSTRACTION FOR QUALITY REPORTING 16 Automatic Chart Abstraction Manual Chart Abstraction ? Clinical notes, computerized patient data /25

PATIENT RECORDS Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Look up ICD-9 codes Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance 17 /25

Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Look up ICD-9 codes Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance 18 PATIENT RECORDS /25

AUTOMATIC CHART ABSTRACTION 19 ? /25

VALIDATION OF OUR AUTOMATED APPROACH 20  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

AGREEMENT BETWEEN MANUAL & AUTOAMTED 21 Overall 96% /25

QUALITY IMPROVEMENT 22 Less than 30 Days Automatic /25

TECHNIQUE SUMMARY 23 /25 Natural Language Processing Data mining Machine learning Calculus Linear algebra Computer programming databases Data structures & Algorithms Software Engineering Statistics Knowledge representation

bpm respiratory rate (RR) hemorrhage control time (s) MANY MORE APPLICATYION AREAS 24  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  ….. ….. /25 New Era of Medical Informatics

25  Thanks for your attendance /25