Establishing an Optimal Operational Infrastructure

Transcription

1 Establishing an Optimal Operational Infrastructure David Holmes III Imaging in Clinical Trials Conference March 30 th, MFMER slide-1

2 My Background Ph.D. in Biomedical Engineering/Imaging M.S. in Clinical Research Research Lab Biomedical Analytics and Computational Engineering (BACE) Lab Core Facility Biomedical Imaging Resource (BIR) 2015 MFMER slide-2

3 Take Away Messages 2015 MFMER slide-3

4 A camel is an even-toed ungulate within the genus Camelus, bearing distinctive fatty deposits known as "humps" on its back MFMER slide-4

5 A horse-drawn vehicle is a mechanized piece of equipment pulled by one horse or by a team of horses. These vehicles typically had two or four wheels and were used to carry passengers and/or a load MFMER slide-5

6 A tree is a perennial plant with an elongated stem, or trunk, supporting branches and leaves in most species. Trees provide shade and shelter, timber for construction, fuel for cooking and heating, and fruit for food as well as having many other uses MFMER slide-6

7 Objectives Mayo Clinic Research Infrastructure Clinical Trials Imaging Core Activities Core Infrastructure Generalizability Workflows Communication Perspectives 2015 MFMER slide-7

8 Mayo Clinic Three primary sites Rochester, MN Scottsdale, AZ Jacksonville, FL Mayo Clinic Health System Over 70 locations Mayo Clinic Care Network Network of Providers 64,000 employees 2015 MFMER slide-8

9 Mayo Clinic Statistics Faculty in most medical disciplines, but often focus on complex, tertiary care 1,318,300 patient registrations 128,000 hospital admissions 641,000 hospital days of care 2,400+ hospital beds 2015 MFMER slide-9

10 Mayo Clinic Research People Full-time scientific faculty: 332 Physicians actively involved in research: 758 Full-time research personnel: 3,120 Studies, publications and grants Active grants and contracts: 4,815 Infrastructure Research Systems infrastructure Core research laboratories: 24 Research laboratory space: 372,157 square feet Total research space: 952,605 square feet 2015 MFMER slide-10

11 Mayo Clinic Research Productivity 2,723 IRB Protocols submitted annually 11,028 Active human studies 7,305 Research publications annually 2015 MFMER slide-11

12 Research Systems 2015 MFMER slide-12

13 Biomedical Imaging Resource (BIR) Conceived as an advanced image analysis and visualization research lab in the early 1980s Became one of the first Mayo Core Facilities in the early 1990s Investigators didn t have the compute/storage infrastructure to analyze and manage medical image data for their research Provided a wide variety of services to support the research community (internal and external) Protocol design and consultation Custom software development Medical image analysis Data management 2015 MFMER slide-13

14 The Analyze Imaging Software System Support for many modalities and many analysis pipelines Over 150 person-years of development First integrated biomedical visualization software system: Distributed worldwide Institutions Users 40+ countries 2015 MFMER slide-14

15 BIR Today Provided a wide variety of services to support the research community (internal and external) Protocol design and consultation Custom software development Medical image analysis Data management We still develop and distribute Analyze widely around the world MFMER slide-15

16 So, what has changed in the past 25 years? The world has changed: As a society, we are collecting more and more data Imaging has improved greatly, resulting in larger datasets Clinical trials are using imaging more than ever More clinical trials for more conditions 2015 MFMER slide-16

17 BIR Today We continue to offer the same services, but under the hood everything has changed (from a back-end, infrastructure perspective) Virtualization and Cloud Environments Data storage goes beyond filesystems Technology deployment lightweight and virtual Workflow engines drive our daily process To a long-standing customer, it appears that we just continue to simply provide good service at a good cost MFMER slide-17

18 Generalizability 2015 MFMER slide-18

19 Generalizability Generalizing infrastructure is at odds with the variety of expectations/specifications/design of new trials We get the best performance when we build projectspecific tools Net negative proposition Instead, we gain generalizability by developing building blocks Hardware platforms which can be rapidly configured Databases which don t define the structure of the data Software APIs, rather than software applications 2015 MFMER slide-19

20 Hardware Generalization Agile platforms that adjust to the demand 2015 MFMER slide-20

21 Database platforms Moving away from traditional relational database design to NoSQL / Semantic Graph / Entity-Relationship models 2015 MFMER slide-21

22 Software APIs Anonymization APIs Imaging Survey APIs Communication APIs Image Processing APIs AMD 2015 MFMER slide-22

23 Workflow 2015 MFMER slide-23

24 Workflow Workflows enable efficient processing of data; however, complex workflows can quickly become burdensome. While we strive for a simple, flexible, generalizable workflow, we often find that every study is different. In some cases, the requirements change over time. As a result, we layer more and more branches, constraints, etc, on the workflow In the end, we end up with a Frankenstein s monster MFMER slide-24

25 Business Process Management (BPM) BPMs facilitate the structuring and validations of analysis workflow 2015 MFMER slide-25

26 From Koo, 2009 Increased visibility and knowledge of company s activities. Increased ability to identify bottlenecks. Increased identification of potential areas of optimization. Reduced lead-times. Better definition of duties and roles in company. Good tool for fraud prevention, auditing, and assessment of regulation compliance MFMER slide-26

27 BPM for Core Facilities 2015 MFMER slide-27

28 BIR Experience BPM for imaging is a very nascent BPM engines needs to be modified to meet the needs for imaging and imaging core facilities We prefer the open-source platforms Needs expertise Development of BPM Implementation of workflows Test/Validation expertise Experienced users 2015 MFMER slide-28

29 Communications 2015 MFMER slide-29

30 Communication Discussions with the sponsor Feedback to the sites Documentation/Auditing to regulatory Findings to other core labs/centers Guidance to staff Communication can make or break studies 2015 MFMER slide-30

31 Issues 91% Security Risks Data formatting Auditing Reporting Findings 2015 MFMER slide-31

32 Managing Communications Own as much of the process as possible Build the data transfer strategy Implement the workflow Provide the tools to sites/sponsor Define the communication pathways Accommodate variations without changing the central process Build adaptors into the pipeline Don t forget: The best laid plans of mice and men oft go awry 2015 MFMER slide-32

33 Perspectives 2015 MFMER slide-33

34 Perspectives Getting Ahead At the end of the day, core facilities gain an edge by keep up with (or ahead of) technology Technology is driving performance To do so, core facilities need to ensure (little r) research time (big d) Development time A core must be able to quickly assess, implement, test, and integrate, new technologies without risk of down time MFMER slide-34

35 Perspectives Ensuring Success To keep up with technology and ensure continued success, it all comes down to the staff Technology does no good without an experienced operator Core facilities must be staff with a people who have diverse skill sets to support many roles: Imaging science Data science Application development HPC development Web development Database management System administration Image Analysis Study Coordination Project management Corporate memory is almost more valuable than the data itself, so staff retention is critical 2015 MFMER slide-35

36 Perspectives Future planning Generalizability Data storage (scientific database, graph databases) Automation (Pre-fetching data, pre-processing pipelines, consolidating findings) Workflow Compute will move to data, rather than the other way around Deep-learning technologies which will reduce the burden on staff (not eliminate it) Communications Evolutions in cloud with dynamic recruitment of resources and flexible architectures. Project-specific VPN environments Enabling collaborative tools (along with full featured security and auditing for free) 2015 MFMER slide-36

37 Concluding Thoughts The world is changing quickly Data Collection Complex Care Regulatory Environment New approaches to infrastructure will keep up with the changes Experienced and diverse staff continue to be critical to success 2015 MFMER slide-37

38 Questions & Discussion 2015 MFMER slide-38