1. Data Quality: Practice, Technologies and Implications
Kathy Grise, Senior Program Director, IEEE Future Directions
DaSH 2017

2. Data
Information is only as good as the quality of data that supports it.
Data quality is only as good as the observations, documentation and standardized coding of the details that report the key parameters of the patient condition.
A focus on data quality is essential to assuring that we have the information needed to improve healthcare value.
Joe Nichols, MD, Principal, Health Data Consulting, Inc.
DaSH October 2017

3. Data Sources
Within 15 Years or less, Health Data Goes Up to 1 Yottabyte!
Just where is all this data coming from?
Lower cost sensors
More sensors
Lower cost equipment
Lower cost data storage
Easier access to technology
High Performance Computing
IoT
Cloud
Everything in between
DaSH October 2017

4. Data Sources Personalized from the every day consumer Social networks
DaSH October 2017

5. Data Implications
How to ensure data quality? Must first address questions of,
Where is the data source?
Do you trust the data?
How old is the data?
Are there multiple instances of same data?
Which instance is more reliable?
Is the data secure?
Will the data be here one day and gone tomorrow?
Traceability
Reproducibility
All valid questions to consider, which may directly impact one’s ability for informed healthcare decision making!
DaSH October 2017

6. Examples of Data Implications
Consumer generated data and social media
What safeguards are in place to guarantee data integrity and accuracy?
DaSH October 2017

7. Examples of Data Implications
Healthcare generated data by clinician
EHRs stored on cloud
Data is monetized
What safeguards are in place to guarantee data integrity and protection from hackers? How will you even know data has been hacked?
Healthcare records for sale on Dark Web: Clinic in Baltimore had its records stolen which went on sale Dark Web for less than one cent per record.
DaSH October 2017

8. What is IEEE doing?
Establishing consistency in data formats via standards
Mobile Health Data
Data Privacy Process
Algorithmic Bias Considerations
Big Data Metadata Governance and Management
DaSH October 2017

9. ​Who should participate: Wearable device makers Medical device makers
​Goal:
Define specifications for a mobile health data application programming interface (API) and standardized representations for mobile health data and metadata. Mobile health data encompasses personal health data collected from sensors and mobile applications. No mobile health data or metadata standards currently exist.
​Who should participate:
Wearable device makers
Medical device makers
Health data aggregators
Health information technology systems managers
Health information infrastructure providers
Mobile health app developers
Biomedical researchers
Clinicians
Data scientists
Government
​Why get involved:
Mobile apps and sensors are an increasingly valuable source of health-related data. Standardization of mobile health data and metadata will make data aggregation across multiple mobile health sources easier and more accurate, and will reduce the costs of using mobile health data to make biomedical discoveries and to improve health and manage disease.
DaSH October 2017

10. Goal of these standards
Define medical-specific 3D printed and virtual reality model applications for clinical diagnosis and practice based on real human information, and approach new frameworks for 3D medical application established on visualization, data management, simulation and 3D printing.
Who should participate:
Manufacturers of 3D devices including 3D display and HMD systems
Medical 3D model developers
Software programmers for 3D volume imaging
Medical device developers and manufacturers
Healthcare data managers
Medical researchers
Technical experts
Clinical practitioners
Additive manufacturing companies
Other parties that have a material interest in developing these standards
​IEEE P3333.2
IEEE P ™, Standard for Three-Dimensional (3D) Medical Visualization
IEEE P ™, Standard for Three-Dimensional (3D) Medical Data Management
IEEE P ™, Standard for Three-Dimensional (3D) Medical Simulation
IEEE P ™, Standard for Bio-CAD File Format for Medical Three-Dimensional (3D) Printing
DaSH October 2017

11. White paper(s) that frame the problems and identify issues
Governance and metadata management poses unique challenges with regard to the Big Data paradigm shift. The governance lifecycle needs to be sustainable from creation, maintenance, depreciation, archiving, and deletion due to volume, velocity, and variety of big data changes and can be accumulated whether the data is at rest, in motion, or in transactions. Furthermore, metadata management must also consider the issues of security and privacy at the individual, organizational, and national levels.
From the new global Internet, Big Data economy opportunity in Internet of Things, Smart Cities, and other emerging technical and market trends, it is critical to have a standard reference architecture for Big Data Governance and Metadata Management that is scalable and can enable the Findability, Accessibility, Interoperability, and Reusability between heterogeneous datasets from various domains without worrying about data source and structure.
Desired Outcomes:
Workshops to collect, analyze, and identify relevant use cases, requirements, and potential solutions, including documentation on the findings
White paper(s) that frame the problems and identify issues
Reference architecture(s) concepts and solutions from relevant best practices in big data governance and metadata management to formulate data interoperable infrastructure to enable data integration/mashup between diversified domain repositories, including those maintained by participating entities and IEEE Dataport
A proof-of-concept reference implementation would be welcomed
Identification and initiation of potential new standards
DaSH October 2017

12. What is IEEE doing? Societal implications with data and technology
Ethics
Health and safety implications of technology
Professional responsibility
DaSH October 2017

13. bigdata.ieee.org web portal
Contact me:
bigdata.ieee.org web portal
@ieeebigdata
DaSH October 2017