1. Medical Devices and Clinical Informatics
James E. Tcheng, MD, FACC, FSCAI
Professor of Medicine
Professor of Informatics
Duke University Health System
I have no relevant RWI to report

2. Disclosure Statement of Financial Interest
I, James Tcheng, DO NOT have a financial interest/arrangement or affiliation with one or more organizations that could be perceived as a real or apparent conflict of interest in the context of the subject of this presentation.

3. If you don’t know where you are going,
chances are you will end up somewhere else.
-- Yogi Berra ( )

4. Medical Devices and Informatics
Aka “Where Do We Need To Go?”
First Principles
Unique Device Identification and GUDID data
Creating the “river of data”

5. The Clinical Informatics Model
Clinical lexicon as the foundation of controlled vocabularies  rich clinical data + transactions
A Common Data Model (and referential integrity)
High quality data enables data exchange – “collect once, use many times”
Documentation  team-based data capture at the point of care, via structured / semi-structured reporting
Data privacy and security
In theory there is no difference between theory and practice.
In practice there is. Multiple attributions

6. Why a “Controlled Vocabulary”?
Unified clinical lexicons applicable across all clinical documentation, from POC to registries to analytics
Key role of professional societies

7. Referential Integrity
… is a property of data which, when satisfied, requires every value of one attribute (column) of a relation (table) to exist as a value of another attribute (column) in a different relation (table) Wikipedia
This underscores the concept of the single source(s) of truth – and is a requirement of data models  data (or analysis) aggregation

8. Implantable Devices: UDI adoption in EHI
UDI = DI (Device Identifier) + PI (Production Identifiers)
Where do implantable devices appear in EHI?
Supply chain
Clinical care: procedure / OR documentation, EHR
Medical device interrogation, output
Billing and claims data
Registries
How to capture in all EHI?
What does the UDI enable?

9. Learning UDI Community - Data Quality Focus Clinically Relevant Size
GUDID
Repository for device identifier linked to device attributes – single source of truth for all EHI?
Learning UDI Community - Data Quality Focus
Clinically Relevant Size
GUDID field Mfg 1 Mfg 2 Mfg3
SizeType Text Length null
SizeUnit [blank] mm null
SizeValue [blank] 18.0 null
SizeText 18mm length [blank] null

10. Clinician Documentation 2017
Dictation recorder
Pen

11. Clinician Documentation 2017
Mired in ancient paradigms
Authoring of descriptive play-by-play novella encouraged (starts in med school)
Demonstration of physician prowess, justification of actions
(Misbelief) that it will be a good defense in case of malpractice
75% is garbage
E&M coding requirements, EHR MU
Team-based documentation actively discouraged
By regulation, job description, EHR systems

12. What Has To Change?
Document paradigm of the 20th century  informatics data model of the 21st century
Transformation to structured and semi-structured reporting
Understanding the destination: not just “Big Data”, it is “Deep Learning”

13. What is Structured Reporting?
Team-based documentation: specific data captured by the person closest to that data in the clinical workflow (e.g. MA, tech, RN)
Use of universal, well-defined common data elements
Data model parallels clinical care model
Data is compiled to produce vast majority of report
MD: focuses on data quality, cognitive interpretation
Output: the structured report
ROI:  data quality /quantity,  redundancy / repetition, time to final reports, FTE requirements  augmented knowledge, financial gains

14. What is Deep Learning?
… a branch of machine learning based on sets of algorithms that attempt to model high level abstractions in data
You are following a patient with aortic stenosis. When should she undergo aortic valve replacement?
When the risk of procedural morbidity and mortality will be the lowest, before the onset of end-organ consequences, and that will produce the best outcomes!

15. Where Are We Going?
Informatics: common data elements (CDE)  controlled vocabulary; common data model (CDM); data interchange (CDA, IHE)
-- from clinical use to CVIS to EHR to registry
Clinical industrial (process) engineering to describe, model, implement best-practice workflows
-- who does what when, where, and how
New MD, staff professionalism standards
-- conversion from dictation to information model
-- implementation science, change management
Partnership with IT vendors
-- informatics and structured reporting