2018 Data Science Summit: The Economics Of Artificial Intelligence In Healthcare

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1 May 30, Data Science Summit: The Economics Of Artificial Intelligence In Healthcare Making AI Work In Clinical Practice Bibb Allen, MD FACR Chief Medical Officer ACR Data Science Institute

2 OVERVIEW Keys For AI Deployment In Clinical Practice Clinically valuable use cases available within existing workflow flows - Modalities - PACS - Reporting software - Non-interpretive use cases for radiology Transparency in algorithm output Documented safety and efficacy of AI algorithms for patients AI applications that augment the value radiologists provide to their patients and health systems Economically viable business models

3 GOALS FOR THE ACR DSI Artificial intelligence will not replace radiologists Radiologists using AI may replace radiologists who do not Curt Langlotz, MD PhD Radiologists need to be prepared to embrace AI in their practices Radiologists can influence the development and deployment of AI

4 THE QUADRUPLE AIM FOR US HEALTHCARE Doing Better With Less Improving the work life of those who deliver care Improving the individual experience of care Improving the health of populations Reducing the per capita costs of care How will AI affect each of these goals? Don Berwick, MD 4

5 IMAGING 3.0: VALUE-BASED RADIOLOGY Imaging 3.0 is a vision and game plan for providing optimal imaging care. Demonstrating the value of radiologists beyond imaging interpretation 3 Key Actions: Culture Change Portfolio of IT Tools Alignment of Incentives 5

6 IMAGING 3.0: VALUE-BASED RADIOLOGY Clinical Decision Support for Ordering Physicians Providing >24 Million examinations per month Image Sharing RSNA / NIH / Vendors Imaging 3.0 is a vision and game plan for providing optimal imaging care. Structured Reporting Incorporated in all VR reporting platforms Registries Radiation Exposure / Patient Outcomes / Quality 3 Key Actions: Clinical Decision Support for Image Interpretation Integrated into >75% of radiologists desktops Culture Change Portfolio of IT Tools Alignment of Incentives Artificial Intelligence 6

7 MOVING AI TOOLS TO CLINICAL PRACTICE: DEFINING A RADIOLOGY AI ECOSYSTEM CLINICAL PRODUCTS NEEDS Mechanisms to integrate and monitor, AI models in clinical practice using real world experience Standardized methods for AI model validation CHALLENGES And OPPORTUNITIES ALGORITHMS Bring great ideas and clinical needs together Standardized methods to annotate, or aggregate, data for AI model training and testing IDEAS

8 ACR DSI USE CASE DEVELOPMENT Human Actors (Users / Questions) Patients Resources Actors (Electronic) Payer Researcher Appropriate Use Criteria Lexicon CDE Compute Computer Assisted Reporting Data Aggregator / Transfer Radiologist Radiology Technologist Guidelines / Tables / RADS EHR / System Software AI Registries Claims Finance Data Referring Physician ACR REPORTING AND DATA SYSTEMS (RADS) Public Reporting Software Modalities PACS / Reports Regulators / CMS PHYSICIAN SPECIALTY SOCIETY RESOURCES Health System Hospital Administration Hospital Staff PHYSICIAN SPECIALTY SOCIETY RESOURCES

9 INTEGRATING AI INTO CLINICAL WORKFLOW XML Reporting Framework Report UI Solid 7 mm Lung-RADS 3 Radiologist Input Lung RADS 3 7 mm nodule with.. Rad Report Registry Classic Radiologist Decision Support XML Reporting Framework Report UI Solid 7 mm Lung-RADS 3 Lung RADS 3 nodule with.. Rad Report Registry Hybrid Radiologist Decision Support With AI FULL INTEGRATED AI

10 INTEGRATING AI INTO CLINICAL WORKFLOW: DSI USE CASE IMPLEMENTATION Full Initial Lung Cancer Screening AI AI Output Rad Report Registry XML Reporting Framework (CARDS) Visualization And Reporting UI Solid 7 mm Lung-RADS 3 Lung RADS 3 7 mm nodule with.. EHR Registry Other Performance Analytics And Quality Improvement Registries ALGORITHM Cloud / On-prem Modality PACS Transcription PERFORMANCE ASSESSMENT END USERS DEVELOPERS REGULATORS Detect and Localize Quantify and Characterize Classify

11 SUMMARY: MOVING AI INTO ROUTINE CLINICAL PRACTICE Radiology AI Ecosystems to Support Development And Deployment Of Both Image and Non-image based AI Use Cases, Algorithm Training, Algorithm Validation Regulatory Approval, Clinical Integration, Reimbursement Monitoring performance Reimbursement Models For AI In US Government And Commercial Payment Systems Fee For Service Payment Systems Value-based Alternate Payment Models Coverage Decisions Navigating The Regulatory Process And The Value Of Registries Who Will Be The Consumers Of AI? Looking Beyond The Traditional Models

12 THANK YOU! 12