M-CERSI Workshop – Envisioning the Future of Mobile Informatics Division of Postmarket...

M-CERSI Workshop – Envisioning the Future of Mobile Informatics

Division of Postmarket Surveillance

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Overview

I. What DPS does…

II. The Brick Model…

III. Where Social Media Fits…

Isaac Chang, Ph.D.Director Division of Postmarket SurveillanceOffice of Surveillance and BiometricsCenter for Devices and Radiological Health



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What does DPS do?Purpose:• To conduct global post-market surveillance of medical devices and related emerging technologies

through the regulation, collection, integration, and dissemination of adverse event data to identify and communicate actual and/or potential public health risks to FDA and public stakeholders.

How we achieve our goal:• Establish, interpret, and enforce requirements for medical device reporting (21 CFR Part 803-

Medical Device Reporting); and provide outreach and education to FDA and public stakeholders to help them understand the requirements of the medical device reporting regulation.

• Monitor, analyze and synthesize adverse event data to identify signals (of new device issues) and trends (of known device issues); and to ensure that CDRH effectively uses adverse event report data in premarket review, postmarket evaluation studies, compliance and enforcement matters, and timely public health communications in support of the CDRH mission.

• Lead the continuous refinement and maintenance of the IT postmarket infrastructure to efficiently receive, process, retrieve , and analyze adverse event data in support of CDRH and FDA offices.



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Medical Device Landscape



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Medical Device Landscape



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Best Use of MDRs

• Qualitative profile of adverse events for a device or device type– Device use in real world– Types of malfunctions/clinical events.– Vulnerable populations (i.e. Pediatrics, etc.)

• Monitor device performance – Time to event and failure modes– New types of issues with approved devices– Monitoring long term device use

• Signal detection– Rare, serious, or unexpected events– Change in severity of expected events– Use error/human factors issues



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Context is Important…

9/11

US losesAAA CreditRating

US Govtunveiled

rescue of AIG

TensionsBetweenN. andS.

Korea

TARP

Stimulusplan

HurricaneKatrina

SuccessiveInterest Rate

Drops

War concernsand bad corporate outlook

Data Source: Timelines of History Database

Dow Jones Industrial Average



The Brick Model

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EVENT

TRENDS

BEHAVIORS

PERCEPTIONS

Signals

Patterns/Mechanism

Incentives

InterpretationsOrientation

Models

Forecasts

Resource Allocation

OUTPUT OUTCOME

Social MediaSocial Media



New approaches for Social Media

• Focus on the right target…– What is happening ----> Why is it happening– Confirmation of signals ----> Trend drivers– What are we seeing ----> What should we be seeing

• Weather analogy– College Park 86– Dallas 94– Chicago 67

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Summary

• Big Data Analytics is always built around what you are trying to find.

• The goal of analyzing Big Data is to re-focus efforts on the right problems.

• Play to the strengths of social media – explaining behavior and understanding how people think about issues.

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M-CERSI Workshop – Envisioning the Future of Mobile Informatics Division of Postmarket...

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