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Sharing Medical Images Your Way

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1 Sharing Medical Images Your Way
The RSNA Image Share Network Wyatt M. Tellis, Ph.D. Informaticist Laboratory for Radiological Informatics Department of Radiology & Biomedical Imaging

2 Project Overview In Sept. 2009, RSNA was awarded two-year $4.7 million contract by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Funding extended for four additional years Explore use of open standards (IHE) for sharing images and reports Phase One: Provide for patient control by enabling access via personal health record (PHR) accounts Phase Two: Expand network to support direct transfer between sites for clinical and research images

3 Imaging and Information Sharing
Medical imaging is fasting growing physician service in US health system Medicare data show annual utilization increases of about 9%, three times the rate of other physician services Driven by growth of advanced imaging techniques such as MRI, CT, PET, Nuclear, and Cardio. Value of medical imaging in providing accurate, noninvasive diagnosis is unquestioned Costs of “overutilization” and excessive radiation exposure are growing concerns Inaccessibility of prior studies is responsible for a significant number of duplicate studies

4 Current State of Image Sharing
Part of the health record most frequently provided to patients & physicians in digital form No more film Nearly all images are now in a standard digital format (DICOM) Frequently distributed on portable media (CD, DVD)

5 Limitations of Physical Media
Lost or misplaced disks Damaged or unreadable disks Scratches Disk wasn’t “closed” by burning software Non-DICOM disks A few vendors store images in proprietary format only viewable by their systems Locked down PCs prevent viewing of images on disk Wastes physician and patient time and is responsible for a significant number of duplicate studies There must be a better way

6 To The Cloud… Electronic exchange means images everywhere at anytime
Growing market with a lot of companies, but no standards Tower of babel problem: Numerous vendors with proprietary solutions that don’t talk to each other

7 Project Goals Demonstrate a method for sharing images and reports that improves upon portable media Simulate “CD over the Wire” Utilize open standards to create image exchange network Demonstrate patient control of image exchange via personal health record (PHR) accounts Deliver open source reference implementation of systems developed for project

8 Participants Five initial sites: Phase Two Sites:
University of California, San Francisco (UCSF) Mount Sinai Medical Center (NYC) Mayo Clinic (Rochester, MN) University of Chicago Medical Center University of Maryland Medical Center Phase Two Sites: Gillette Children’s (St. Paul, MN) St. Barnabas Health (NJ) Stanford Advanced Radiology (CT)

9 Design Principles Based on IHE XDS model
Data to be shared images (CT, MR, X-Ray) and diagnostic reports Phase One: All data access will be through PHR accounts Phase Two: Support research and site-to-site clinical transfers

10 Steps in Sharing via PHR
At UCSF: Coordinator obtains patient consent Coordinator sends images & reports into network Coordinator s patients instructions and credentials for retrieving their images & reports At home: Patient logins into PHR and retrieves their images and reports Using PHR patient shares images with their consulting physician(s)

11 System Components Edge Device at each site to bridge local imaging and information systems with image sharing network A Clearinghouse to temporarily store images and reports One or more Web-based PHR providers capable of securely retrieving images and reports from clearinghouse into patient account

12 Data Flow PHR‏ Clearinghouse PHR ‏ Imaging Center/ Hospital PACS DICOM
Retrieve Edge Server Upload HL7 Retrieve PHR RIS

13 Edge Device Created by phase one consortium
Open source, available on GitHub UI for site personnel to select exams to share Generates security token for retrieving images Assembles and sends payload (reports & images) to clearinghouse HL7 interface from RIS for reports DICOM query/retrieve from PACS SOAP calls to clearinghouse for submission

14 Clearinghouse Created by LifeImage
Pass-through for all images & reports Receives submissions from edge servers 30 days of temporary storage Responds to queries from designated PHR systems for images & reports Has no customer facing UI Add life image

15 Personal Health Record (PHR)
Internet accessible Web based UI for patients to retrieve, view & share their images and reports Provides persistent storage for images and reports Owned and controlled by patients Independent of sending institution. This is NOT a patient portal Four vendors: LifeImage, Dell, itMD and DICOM Grid

16 IHE®: Integrating the Healthcare Enterprise
Initiative between industry and healthcare professionals 100+ member organizations & individuals Is a framework not a standard. Relies on existing standards: DICOM for imaging HL7 for clinical messaging Web services (SOAP = XML + HTTP) for enterprising messaging Create consensus on how to use standards Profile: describe the solution to a specific integration problem, and document the system roles (“actors”) and design details for implementers (“transactions”)

17 Key IHE Concepts Profiles: workflow models of business processes
Cross Enterprise Document Sharing Profile : For exchanging documents between organizations Import Reconciliation Profile: For importing outside images into an institution's archive Actors: systems that create or process data. Examples: Image Manager & Image Archive = PACS (moves & stores images) Order Filler = RIS (schedules ordered exams) Transactions: interactions between actors/data flow RAD-4 [Procedure Scheduled]: RIS schedules an previously ordered exam RAD-8 [Image Stored]: When a scanner stores a new images to PACS

18 XDS Profile XDS = Cross Enterprise Document Sharing
Defines actors & transactions for exchanging clinical documents between healthcare organizations (affinity domain) Actors: Document sources (edge servers) & consumers (PHRs) Document registry & repository (clearinghouse) Transactions are all SOAP based web service calls: ITI-41: Provide & register document set (upload to CH) ITI-18: Registry stored query (PHR query of CH) ITI-43: Retrieve document set (PHR retrieve from CH)

19 Alterations of XDS Model
Normally XDS relies on patient identifiers to locate documents in registry ITI-18 supports queries on demographics (name, MRN, etc) OK in a regular XDS deployment since all nodes within an affinity domain can access PHI RSNA Network utilizes a one time use transaction id to retrieve content 64 character string: ce401a161c6061ae74baf7cb96593d1a2fdd7c58d951b7439e67f96099b1e07a Used in ITI-41: Provide & Register Document Set transaction Eliminates the need to store PHI in the registry

20 Tokens & Transaction IDs
Transaction id is generated from SHA-256 hash of: One time token: Created when images are selected for transmission: ce401a16 Password: Selected by patient or site staff when images are queued for transmission: Patient’s DOB: 11/7/1972 To retrieve images and reports from PHR: Patient enters token & password (PHR already has DOB) PHR calculates transaction id: SHA256(token + pw + dob) PHR uses transaction id to locate documents (ITI-18) and retrieve content (ITI-43)

21 Queuing Exams at Edge Server

22 Retrieving Exams via PHR

23 Usage of PHR Sharing 8 sites are currently enrolling patients
6015 patients (UCSF: 1404) 23989 exams (UCSF: 9386) At UCSF majority of exams were cross sectional:

24 Summary Electronic image exchange is viable and growing industry
Facilitated by pervasiveness of cloud infrastructure Standards needed to prevent vendor lock in and information silos Tower of Babel problem PHRs demonstrate feasibility of granting patient access to and control of their images Number of signups show patient interest Meaningful Use will require greater patient access 22 patients at UCSF

25 Next Steps Expand functionality of the network:
Use for exchange of research images such as in a clinical trial Direct site-to-site exchange for trauma or urgent transfers Data flow changes, but transactions remain the same Final destination of images and reports are no longer a PHR, but a research or clinical archive Research data must be anonyimzied due to IRB requirements Utilize RSNA’s Clinical Trials Processor (CTP)

26 Sharing Medical Images Researchers’ WAY
Maxwell Cheong, M.S. Medical Imaging Informaticist Quantitative Image Processing Center Department of Radiology & Biomedical Imaging UCSF

27 Once Upon A Time… The story of Dr. Felonius Neurosurgery researcher
Mild traumatic brain injury (mTBI) study “I need more data to support my hypothesis” Multi-site study of patient’s CT and MRI scans Graphics:

28 Challenges HIPAA Privacy Rule Compliance – Research
Protected Health Information (PHI) Identifiers patient name, birthday, medical record number Institutional Review Board (IRB) approval at each participating site Consistent MRI scan quality Efficient and cost effective Scalable for big data CDs from film library Manual de-identifcation of imaging data An easy but yet secure way to share data Graphics:

29 Solution: Research PACS (rPACS) at UCSF
Started in 2008 to address the challenges of enabling HIPAA compliant mult-site studies Dcm4chee Image Management Server DICOM compliant Open source with Jboss, PostgreSQL backend Scalable with J2EE multi-server deployment Redundant Array of Independent Disks (RAID) Daily backups But how do you get the data? Graphics:

30 rPACS + QUIPC DICOM File Uploader
Powered by CTP Medical imaging data anonymization and upload all in one Open source Easily configurable – XML, Java On-demand anonymization Pre-defined mapping table Automatic anonymized ID assignment Many different workflows configured to suit every need

31 Secured VPN Connection QUIPC DICOM File Uploader (powered by CTP)
rPACS Data Flow PACS Researcher DICOM (PHI) Internet DICOM (De-Identified) Secured VPN Connection rPACS QUIPC DICOM File Uploader (powered by CTP) (De-Identified)

32 On Demand Anonymization

33 Pre-Defined Mapping Table

34 Automatic Anonymized ID Assignment

35 Phase Two: rPACS + CTP + RSNA ISN
Hospital 1 Researcher CTP Enabled Edge Server DICOM (De-Identified) Clearing House Hospital 2 CTP Enabled Edge Server rPACS Hospital 3 (De-Identified) CTP Enabled Edge Server Hospital 4 CTP Enabled Edge Server

36 Where do data come from?

37 Where do data come from?

38 Where do data come from? 24 medical centers providing imaging data

39 How do I download the data?
DICOM Viewer: Osirix Network File Servers Direct Download via FTP Laptop Desktop iPad and other mobile devices

40 DICOM Viewer - Osirix DICOM compliant on-demand data query and retrieve Clinical viewing / FDA cleared


42 Network File Servers Automated image processing pipeline friendly

43 Direct Download s sent to collaborators with password protected links to the anonymized imaging data

44 Direct Download (con’t) pw: sharecase

45 rPACS - Metrics 2.3 TB 9,936,374 images 15 concurrent servers
Multiple modalities CT, MRI, MicroCT, X-Ray, Angiogram… 4 multi-site studies Mild Traumatic Brain Injury (mTBI) Austism Osteoarthritis Epilepsy

46 Summary Dr Felonius’ brain study is a success rPACS + CTP + RSNA ISN
Exceeded patient enrollment goals by recruiting patients through multiple medical centers across the world Large dataset of anonymized imaging data collected HIPAA compliant rPACS + CTP + RSNA ISN Collaborative research platform Scalable Secure Graphics:

47 Future Plans Social sharing via web portal integrations
Box? Facebook? Research system integrations The Federal Interagency Traumatic Brain Injury Research (FITBIR) informatics system

48 Questions? Wyatt M. Tellis, Ph.D.
Laboratory for Radiological Informatics (LRI) University of California San Francisco 530 Parnassus Ave, CL-158, San Francisco CA 94143 Tel: (415) Maxwell Cheong, M.S. Quantitative Image Processing Center (QUIPC) 185 Berry St, Suite 350 Lobby 6, San Francisco CA 94107 Tel: (415)

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