Jay Srini - Liberating Image Data Panel

1. Imagine Imaging in the
New world
Jay Srini
jsrinij@gmail.com
412 760 9593

2. MU and Imaging
• Stage 2 MU requirements call for remote viewing systems to allow referring physicians or
others access a patient’s EMR to view images and reports from various departments.
• It calls for the integration of patient access to health information and images via patient
portals to increase patient engagement. This may play a larger role in the future, as patients
with chronic conditions like diabetes and heart failure will be asked to take a more active
role in their healthcare with remote monitoring programs. C
• Clinical decision support and computerized physician order entry (CPOE) is now being
required to improve patient safety, eliminate illegible written orders, record all orders in one
location, reduce redundant tests and to justify use of expensive tests or imaging exams.
• VNAs also can help enable remote access to patient data using mobile devices, such as
tablets by physicians on rounds, anywhere in the hospital, on the road or at home. This may
lead to a reduced reliance on workstations in fixed locations, allowing greater workflow
efficiency and improved patient communication and education.

8. The world is changing as
we see it
• The Past The future
• Hospital Centric Patient Centric
• Episodic Continuous
• Fee for Service Value Based
• Sick Care Well + Sick

9. Collaborative Care

10. http://www.graycons.com/wp-
content/uploads/2013/03/eHealth-
Technologies-Unified-Approach-to-
Sharing_White-Paper.pdf

11. What is needed
• No more Data Siloes
• Enterprise – Community View
• Universally Acessable
• Break the Glass model
• Do not ignore Privacy Rules
• Respect the patient- Epatient Dave
• Data by itself is useless unless converted to actionable
information.

12. Data Liquidity
• In 2001 the IOM report "Crossing the Quality
Chasm" and the NCVHS report "Information for
Health" were released and they provided the context
for the development of information systems used to
support health-supporting processes. Both had as
their goals, implicit or explicit, to ensure the right
data is provided to the right person at the right time,
which is one definition of "Data Liquidity

13. Data liquidity is not just about
Technology

14. Culture Trumps technology

15. Data is King

16. Data Liquidity does not mean
Chaos
• Data Management
Image Life cycle Management
Storage Tiers
Prefetch
Compliance and Auditing cannot be Ignored!!
Authentication Rules are not a matter of choice!

17. Key Considerations
• Regulatory Compliance
• Risk Management
• Data Trustee/Stewardship
• Centralized Data management

18. XDS-Technology is not the
real issue
• Cross-Enterprise Document Sharing (XDS)
facilitates the registration, distribution and access
across health enterprises of patient electronic health
records.

20. DouG Laney
VALUE

21. Beyond Volume Variety and Velocity
https://datafloq.com/read/3
vs-sufficient-describe-big-
data/166
https://datafloq.com/read/3vs-sufficient-
describe-big-data/166

22. Just not radiology
• A fully integrated cardiovascular department with a CVIS
enables staff to remain in one area and have complete visibility
of a patient’s prior history along with current procedural
data/imaging. Most of the market-available solutions have
structured reporting modules that have been optimized to
improve cardiologists’ reporting workflow with reduced steps of
data entry and conventional dictations.
• Structured templates for echo, pediatrics, peripheral vascular,
cath lab and several others are offered by many solutions in
the market, and this has truly become a standard practice for
increased performance. -
• See more at: http://www.dicardiology.com/article/essential-
elements-new-cardiovascular-information-
systems#sthash.GvwkY3u0.dpuf

27. Figure 1. A logic diagram of an example of the field of radiogenomics for breast cancer using digital mammography and DCE MRI.
Rivka Colen, Ian Foster, Robert Gatenby, Mary Ellen Giger, Robert Gillies, David Gutman, Matthew Heller, Rajan Jain, Anant
Madabhushi, Subha Madhavan, Sandy Napel, Arvind Rao, Joel Saltz, James Tatum, Roeland Verhaak, Gary Whitman
NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics
Signatures
Translational Oncology, Volume 7, Issue 5, 2014, 556–569
http://dx.doi.org/10.1016/j.tranon.2014.07.007

28. Imaging + Genomics
• The early assumption that DNA sequencing alone could explain disease and help
define specific therapies has given way to a more nuanced view that epigenetic
processes, the cellular matrix, and aspects of the intracellular milieu such as micro
RNA and proteomics are elements that modulate gene expression and are the
scaffold of the diseased state (4).
• Many in the basic-science community admit, at least in a perfunctory way, that
genotype manifests itself as phenotype (here read “imaging”) and that the one may
inform the other. Also, there is increasing recognition that there is not only
substantial heterogeneity between tumors but also within tumors (5)
• . In one study (6), two-thirds of the mutations found in single biopsy samples were
not uniformly detected in all the sampled regions of the same patient’s tumor. Here
again, imaging has the potential to help noninvasively characterize the whole tumor,
all tumors in the patient, and tumors at multiple time points over the course of
treatment. However, there are few investigators who have yet pursued that potential
connectivity in the published imaging literature. It is also true that federal program
announcements and grant funding review committees have been slow to
acknowledge or encourage investigation of that science intersection.

29. Figure 2. Radiological and histological feature extraction and correlation to genomic data. Following identification of the contrast
enhancing region (top-left) or cancer nuclei (right) various imaging features can be extracted. Using corresponding genomic dat...
Rivka Colen, Ian Foster, Robert Gatenby, Mary Ellen Giger, Robert Gillies, David Gutman, Matthew Heller, Rajan Jain, Anant
Madabhushi, Subha Madhavan, Sandy Napel, Arvind Rao, Joel Saltz, James Tatum, Roeland Verhaak, Gary Whitman
NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics
Signatures
Translational Oncology, Volume 7, Issue 5, 2014, 556–569
http://dx.doi.org/10.1016/j.tranon.2014.07.007

30. Surgical Simulation

34. Unlike mammography and breast ultrasound, breast MR imaging assesses the blood
flow to a tumor. The blood flow to cancers is different from the blood flow to normal
tissue, and the radiologist uses this information to determine which lesions need to be
biopsied, and which can be left alone. This is valuable information, but it presents a
slew of data to the radiologist that must be interpreted.
In particular, the radiologist must identify that region of the lesion that has the most
suspicious type of blood flow. This is a tedious process when done by hand, but is
relatively straightforward once the computer has generated an “angiogenesis map.”
The map is a color representation of the kind of blood flow in a given tissue, and is
superimposed on the anatomical images. Much like a weather map is used to indicate
regions with particular temperatures or heavy precipitation, so too can an
angiogenesis map identify tissue associated with malignant-type blood flow. The
angiogenesis map is an intuitive tool that allows the radiologist to quickly identify the
tissue that is most suspicious for a malignancy. - See more at:
http://www.itnonline.com/article/cad-improves-breast-mri-
workflow#sthash.3N7ZFS2n.dpuf
Sophisticated data needs
sophisticated manipulation

35. Mmodal catalyst- software to bring
images to actionable information
• NLP
• Standard Radlex
• Structured info
• Locked with radiologist narrative
Critical findings
Billing issue
PQRS reporting

36. From data to Information
knowledge to wisdom

37. Proactive Predictive
Precise care

39. It is the Workflow Stupid
• It is not about Dicom Vs non Dicom
• It is not about VNA
• It is not about petabytes or yottabytes
• IT is not about Private cloud or Public Cloud
• It is strictly about

41. So the future of Imaging is about
Expect the Unexpected
• Consolidate the VNA
• VNA is beyond the ologies..
• Coordination and Collaboration
• Workflow is key
• Outcomes and quality
• Behavior change
• All leads to the Triple aim objectives of cost quality and
Experience

42. http://nocamels.com/2013/10/israeli-tech-
puts-3d-holographic-heart-in-the-doctors-
hand-to-save-lives/
A view of things to come