Enterprise Vendor Neutral Archives and your Pathology Department

Transcription

1 Enterprise Vendor Neutral Archives and your Pathology Department Alexis Carter, MD Physician Informaticist Department of Pathology and Laboratory Medicine

2 Disclosures 2 No financial disclosures to report

3 Abbreviations Abbreviation VNA PACS DICOM RIS LIS EHR Meaning Vendor Neutral Archive Picture Archival and Communications System Digital Imaging and Communications in Medicine Radiology Information System Laboratory Information System Electronic Health Record (a.k.a. Electronic Medical Record)

4 Objectives List the advantages and barriers of Vendor Neutral Archives (VNA) both within and outside Pathology for storage and retrieval of medically important images Describe critical factors for researching, planning, and properly staging the implementation of a VNA that includes pathology images

5 Pediatric patients only (18 yrs or less) 21 yrs or less if chronic condition present Largest pediatric health system in the United States Patients from all over southeastern US Cancer, trauma, transplants, hematologic disorders, genetic conditions, etc. Affiliated with Emory Healthcare / Emory University 5

6 My Experience Installed a VNA (of sorts) in pathology only in 2008 at a different institution Lots of lessons learned Enterprise wide VNA project just starting at CHOA Inclusive of radiology, pathology and cardiology 6

7 Vendor Neutral Archives (VNAs) Various definitions none widely accepted Buyer beware check your definition against the product Ideal VNA: One stop shop for all image archiving and dissemination Provides viewer support (directly or via 3 rd party) Works with ALL data types generated by ALL vendors In reality No VNA can work with all images, but it can get close 7

8 VNA Architecture - Example Image Acquisition Pathology Cardiology Radiology Ophthalmology Dermatology Many others PACS WSI Platform Radiology PACS EHR Image Repository Scanned document Repository Enterprise VNA EHR / LIS / RIS / Other Enterprise VNA Viewer Worklists Report signout Image annotation Clinical Data Health Information Exchange 8

9 Advantages of VNAs - Many Centralizes viewing of all patient images in one place No more searching various image silos (Increased provider satisfaction) Historical images can be compared to current (earlier detection of problems) Better patient care Increased workflow efficiency Better security if viewer bound within EHR, LIS, RIS, etc. Images requiring special handling Authentication, authorization & audits Reduces risk of duplicate procedures / exams Reduces radiation exposure Improved patient satisfaction Decreased cost New opportunities for patient engagement (portals), research and education Remote viewing / Telehealth support Imagine having the mammogram right next to your breast pathology case at sign out Advantages can depend on the type of VNA 9

10 Types of VNAs [Shoemaker 2011] Viewer True VNA As close to ideal VNA as possible Has integrated viewer (same or different vendor) Hybrid VNA Lacks a viewer which must be supplied by 3 rd party If viewer is compatible with non DICOM images, then both DICOM and non DICOM images are supported DICOM Archive VNA A.k.a. PACS neutral archives (PNA) Store/archive images from multiple DICOM systems Limited to DICOM compliant images Image Storage Central VNA Images are stored in a central archive with a viewer Federated VNA Images are stored at the source and the VNA provides a viewer for them 10

11 Advantages of VNAs Central VNA Repository for disaster recovery / business continuity May have lower cost of maintenance Backbone for health information exchanges with outside organizations May not need data migrations when new PACS installed Federated VNA Less hardware infrastructure to set up (cheaper, faster) Lose advantages of central VNA 11

12 Reasons why VNAs don t get implemented Hardware and software components are expensive Creating sound business case takes a lot of time and resources Workflow changes are large for end users Lack of enterprise approach No enterprise governance No strategic plan Too little stakeholder engagement Not knowing who all your stakeholders are 12

13 Reasons why VNAs don t get implemented #2 Lack of research Poor product selection Integration problems Awkward workflows Poor planning Too much change at one time or benefits not obvious Long downtime required Problems with security 13

14 Security Risks Voltz reported: 45% of hacks are external; 55% internal to the organization Internal hackers may be careless or have willful intent 89% of healthcare organizations reported at least 1 data breach, 79% reported >=2 breaches Avg cost of a single healthcare breach is $2.2 million Financial services firms spend 1/3 budget on IT Healthcare only spends 2 3% on IT on avg Only 0.1% of overall budget is on IT security Voltz D. HIT Think: Why artificial intelligence is needed to prevent cyber attacks. HealthData Management: SourceMedia; 2016:

15 Specific Risks to Genetic Information Identifying an individual from their genetic information seems far fetched except Online genealogy databases are growing rapidly Metadata often accompanies the genomic data in various file formats (gender, zip code) 42% of patients were able to be accurately identified in the Personal Genome Project because the genomic sequences contained metadata for zip code, birthdate and gender (see references) IMAGES HAVE THE SAME RISKS Facial recognition software Metadata accompanies images 15

16 Status of VNAs [Klas 2015, lifeimage 2016] Technology implementations expected in next year Enterprise federated viewer, VNA and PACS installations are each getting installed in ~40% institutions surveyed Key drivers (all except last are specialty independent) Improving care coordination is #1 86% Reducing redundant testing 71% Improving physician satisfaction 55% Reducing radiation exposure 42% 16

17 Service lines that use VNAs [Klas 2015, lifeimage 2016] Service lines connected to VNAs Radiology 91% Cardiology 71% Dermatology 16% Gastroenterology 15% Ophthalmology 10% Pathology 6% 17

18 Other Service Lines and Their Images Radiology GI and Pulmonary Ophthalmology Cardiology Neurology Emergency Medicine Dermatology Infectious Disease Obstetrics and GYN EVERYTHING Endoscopy, bronchoscopy Fundoscopy Echocardiograms, ECGs EEGs Injuries, ultrasound, radiology Lesions and wounds Wound progression / healing Ultrasound 18

19 Medical images for VNAs - Pathology Gross Autopsy Histopathology Whole slide images Flow cytometry Microbiology cultures Microbiology smears Peripheral blood smears FISH Banded Chromosome analyses Electropherograms Signal graphs over time (e.g., real time PCR) Electrophoresis gels Electron microscopy SNP Arrays, etc. 19

20 HIMSS-SIIM White Papers 2016 Key elements of a successful Enterprise Imaging program are: [Roth et al., 2016 and see other references] 1. Governance 2. Enterprise Imaging Strategy 3. Enterprise Imaging Platform (Infrastructure) 4. Clinical Images and Multimedia Content 5. EHR Enterprise Viewer 6. Image exchange services 7. Image analytics Good workflow re engineering and change management are critical 20

21 VNA strategy Most VNAs get implemented when the Radiology PACS is due to be replaced This may drive timelines Determine your fundamental patient matching strategy Do inventory of all medical images at your institution 22

22 VNA strategy Rank service lines acquiring medical images in order of importance and feasibility to utilize VNA Set loose timelines Pathologists do not wait for the enterprise to come to you! If you are not involved, the VNA may not be able to import your images 23

23 Image inventory Make and model number of device that captures the image Image type? Format? Settings (color, resolution, etc.)? Image identifies patient (case identifier, face, unique tattoo)? Type of Compression and/or annotation, if any Image metadata at the time of capture? (be specific) Image transmits via interface? HL7 vs. flat file vs. other? Image is compliant with DICOM standard? Needs alteration to be viewed in the Enterprise viewer? EHR? What workflow drives capture of this image? What other tasks does the presence of the image trigger? Post processing performed on image? (e.g., image analysis) Is the image imported into any reports or other documents? If the image is moved to the VNA, what is the impact? 24

24 Pathology Image Inventory Image type Gross Autopsy Histopathology Whole slide images Flow cytometry Microbiology cultures Microbiology smears Peripheral blood smears FISH Banded Chromosome analyses Electropherograms Signal graphs over time (e.g., real time PCR) Electrophoresis gels Electron microscopy SNP Arrays, etc. Questions for each type Make and model number of device that captures the image Image type? Format? Settings (color, resolution, etc.)? Image identifies patient (case identifier, face, unique tattoo)? Type of Compression and/or annotation, if any Image metadata at the time of capture? (be specific) Image transmits via interface? HL7 vs. flat file vs. other? Image is compliant with DICOM standard? Needs alteration to be viewed in the Enterprise viewer? EHR? What workflow drives capture of this image? What other tasks does the presence of the image trigger? Post processing performed on image? (e.g., image analysis) Is the image imported into any reports or other documents? If the image is moved to the VNA, what is the impact? 25

25 Implementing your VNA 1. Set up your governance 2. Engage all appropriate stakeholders what s in it for them? 3. Do your homework on your current images 4. Develop your strategic plan & sequence of implementation 5. Research the potential VNA products VERY thoroughly (RFP) 6. Make your selection 7. Plan, plan, plan your implementation (and plan some more) Stage your implementation according to your strategic plan Don t do all images or all service lines at once 8. Use good Workflow Re Engineering and Change Management 9. Lessons learned 26

26 Objectives List the advantages and barriers of Vendor Neutral Archives (VNA) both within and outside Pathology for storage and retrieval of medically important images Describe critical factors for researching, planning, and properly staging the implementation of a VNA that includes pathology images

27 References HIMSS SIIM White Papers Clunie, D. A., D. K. Dennison, D. Cram, K. R. Persons, M. D. Bronkalla and H. R. Primo (2016). "Technical Challenges of Enterprise Imaging: HIMSS SIIM Collaborative White Paper." J Digit Imaging. Cram, D., C. J. Roth and A. J. Towbin (2016). "Orders Versus Encounters Based Image Capture: Implications Pre and Post Procedure Workflow, Technical and Build Capabilities, Resulting, Analytics and Revenue Capture: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5): Roth, C. J., L. M. Lannum, D. K. Dennison and A. J. Towbin (2016). "The Current State and Path Forward For Enterprise Image Viewing: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5): Roth, C. J., L. M. Lannum and C. L. Joseph (2016). "Enterprise Imaging Governance: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5): Roth, C. J., L. M. Lannum and K. R. Persons (2016). "A Foundation for Enterprise Imaging: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5): Shoemaker, J. (2011) "VNA: Solutions for Medical Image Archiving and Dissemination." HIMSS News. Towbin, A. J., C. J. Roth, M. Bronkalla and D. Cram (2016). "Workflow Challenges of Enterprise Imaging: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5): Vreeland, A., K. R. Persons, H. R. Primo, M. Bishop, K. M. Garriott, M. K. Doyle, E. Silver, D. M. Brown and C. Bashall (2016). "Considerations for Exchanging and Sharing Medical Images for Improved Collaboration and Patient Care: HIMSS SIIM Collaborative White Paper." J Digit Imaging 29(5):

28 References Other References "Data Privacy Lab." Retrieved November 9, 2016, from (2016). "IBM Watson for Genomics." Retrieved November 22, 2016, from (2016). "Watson Health: Welcome to the Cognitive Area of Health." Retrieved November 28, 2016, from Bialecki, B., J. Park and M. Tilkin (2016). "Using Object Storage Technology vs Vendor Neutral Archives for an Image Data Repository Infrastructure." J Digit Imaging 29(4): Burgelman, L. Attention, Big Data Enthusiasts: Here's What You Shouldn't Ignore. Wired. Cook, R. (2014) "Is VNA the future of image delivery? A platform independent archival system has strong appeal." Healthcare IT News. Erlich, Y. and A. Narayanan (2014). "Routes for breaching and protecting genetic privacy." Nat Rev Genet 15(6): Forsberg, D., B. Rosipko, J. L. Sunshine and P. R. Ros (2016). "State of Integration Between PACS and Other IT Systems: A National Survey of Academic Radiology Departments." J Am Coll Radiol 13(7): e812. Gymrek, M., A. L. McGuire, D. Golan, E. Halperin and Y. Erlich (2013). "Identifying personal genomes by surname inference." Science 339(6117): Hagland, M. (2013). "Strategic radiology systems implementation. Challenges and benefits of moving to a vendor neutral archive." Healthc Inform 30(1): 48,

29 References Hagland, M. (2016). LIVE from RSNA 2016: Joe Marion Considers the Present and the Future. Healthcare Informatics. Hagland, M. (2016, November 28). "LIVE from RSNA 2016: Radiology Thought Leader Eliot Siegel, M.D., on What Value Really Means." Healthcare Informatics Retrieved November 29, 2016, from informatics.com/print/article/interoperability/live rsna 2016 radiology thought leader eliot siegel md what value really. Harwalkar, K. (2013). DICOM Prefetch: Quick access to priors. The DICOM 2013 International Conference & Seminar. Bangalore, India, National Electrical Manufacturers Association. Integrating the Healthcare Enterprise (2012). IHE Radiology Technical Framework Supplement: Basic Image Review (BIR): Trial Implementation, IHE International, Inc. Kalinski, T., H. Hofmann, D. S. Franke and A. Roessner (2002). "Digital imaging and electronic patient records in pathology using an integrated department information system with PACS." Pathol Res Pract198(10): lifeimage (2016). CIO Perspectives on Enterprise Imaging: A survey of CHIME members explores the drivers, challenges and benefits of image data interoperability. 30

30 References Marchevsky, A. M., R. Dulbandzhyan, K. Seely, S. Carey and R. G. Duncan (2002). "Storage and distribution of pathology digital images using integrated web based viewing systems." Arch Pathol Lab Med 126(5): Murphy, K. (2016). Lack of EHR Interoperability Holding Back Enterprise Imaging. HealthIT Interoperability. Rasband, M., J. Chistensen, E. Paxman, B. Smith, A. Rasmussen and J. Zidel (2015). Enterprise Imaging 2015: Strategies, Options, and Vendor Performance. KLAS Performance Report. Tanner, A. (2013). Harvard Professor Re Identifies Anonymous Volunteers in DNA Study. Forbes. Williamson, J. and H. Danielson (2014) "The Business Case for VNA." Imaging Technology News. 31

31 QUESTIONS? 32

32 11/4/17 Digital Pathology Milestones Liron Pantanowitz MD Professor of Pathology & Biomedical Informatics Director of Pathology Informatics UPMC, Pittsburgh, USA 1

33 11/4/17 Market Prediction Poised to explode! $ million in Whole Slide Scanner 2

34 11/4/17 Image courtesy of Johan Lundin Pixel Pathway 6 3

35 11/4/17 High Resolution Imaging + High Throughput Device 4

36 11/4/17 WSI Files Daniel et al. Diagn Pathol 2011; 6 (Suppl 1):S17 CXR WSI (biopsy at 40x scan 654Mb with 20:1 loss compression) vs chest x-ray (pixelnormalized display) Pantanowitz et al. Pathology Informatics. ASCP Press

37 11/4/17 Digital Pathology Uses Primary diagnosis 2 nd opinion (consultation) Telepathology Quality Assurance (PT) Archiving Education Image analysis Research Marketing & Business tissuepathology.com Education Paradigm Shift 6

38 11/4/17 Validation of WSI for Primary Diagnosis Saco A et al. Pathobiology 2016; 83:89-98 Pathology Subspecialty Accuracy (Concordance) Surgical Pathology 75% - 98% Breast Pathology 90% - 99% Dermatopathology 94% - 100% GU Pathology 88% - 90% GI Pathology 95% GYN Pathology 96% Pediatric Pathology 90% - 93% Pulmonary Pathology 85% - 100% Renal Pathology 84% Cytopathology 89% - 97% 7

39 11/4/17 Clinical Lab Scanning Histology Lab Pathologist Slide Storage OSU Toronto Pittsburgh Netherlands Sweden Singapore 8

40 11/4/17 From Sten Thorstenson, M.D. Barriers Technical Regulatory Cultural Financial 9

41 11/4/17 Technical Barriers It takes too long to scan glass slides. Not every glass slide can be digitized. WSI is not as good as light microscopy. WSI is not as fast as reading glass slides. It s not very easy to use a mouse all day. The WSI system is separate from the LIS. Workflow Hartman et al. J Digit Imaging

42 11/4/17 INTEROPERABILITY AP-LIS DPS IA DPA Connect-a-Thon DICOM WG 26 Path Visions 2017 Multiple vendors DICOM successful LIS was missing Ongoing efforts 11

43 11/4/17 WSI Regulatory Timeline (USA) 12

44 11/4/17 FDA Approval Implication FDA cleared for only surgical pathology primary diagnosis. Philips system (PIPS) has a monopoly at present. Locked down system (no subsystem coupling). Closed system (no plug-ins yet permitted). Predicate system for other vendors (510k vs PMA) New players are entering the market (IA start ups) Pathologist interest has perked (but what s ROI?) Still confusion about remote primary diagnosis (CLIA) WSI is Enabling Technology 13

45 11/4/17 Image Analysis for Pathology Image analysis is the holy grail of digital pathology. Current shift from research to useful clinical applications. Promise of automated CAD & Computational Pathology. Precision medicine demands better precision diagnostics. Transition from qualitative to quantitative science. Digital Pathology is poised for killer apps to emerge. Apps need to do something the microscope can t accomplish. It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change. Charles Darwin 14