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Patient Safety Movement

• Goals of Patient Safety Movement

– No preventable deaths in hospitals by by 2020

– Unify the healthcare ecosystem

– Commitment Based Model-Hospitals Commit by Implementing Process

APSS). MedTech Companies Commit by Sharing their Data

– 1,6000 hospitals had implemented the APSS in January

– In January, 24,643 lives were reported saved annually by these hospitals

– 90+ companies have pledged to share their data, creating an ecosystem for

predictive algorithm development


Evolution of Clinical Practice Data Storage and Retrieval


Benefits

• Continuous monitoring enables temporal view of physiological change1

• Improved accuracy due to automated collection, transfer, and storage2

• Ability to predict and respond to impending critical events3, 4

• Reduction in length of stay5

• Increased survival6

• Improved mortality6

1 Hendrich, A. et. al., The Permanente Journal (2008) V12:3 2 Taenzer et. al., STA Journal (2014) V118:2 3 Alvarez et al. BMC Medical Informatics and Decision Making (2013) 13:28 4 Duncan et. Al., Journal of Critical Care (2006) 21, 271– 279 5 Brown et. Al., The American Journal of Medicine (2014) 127, 226-232 6 Bellomo et. al., Crit Care Med (2012) 40:2349-2360


What is the current status?

• Clinical Support Systems – Existing scoring systems were designed during a time when the

amount of data was limited

– Current research includes advances in data mining and artificial

intelligence

• Statistical clustering models, neural network “deep learning”

• Moore’s law, e.g., GPU technology, storage capacity

improvements, and other “big data” advances

• These systems require large amountsof sample data in order

to produce useful models

• Now that the Ecosystem is born-the Work can begin


Deep Learning Analysis

Researchers have developed deep learning systems that are designed to:

• Generate predictive models to alert clinicians to events before they happen7

• Predict physiological conditions in critical patients8

• Explain why predictions were made9

• Interpret natural language processing (IBM Watson)10

7 Marlin et al, doi 10.1145/2110363.2110408

8 [Che et al, doi 10.1145/2783258.2783365]

9 Luo [doi 10.1186/s13755-016-0015-4]

10Healthcare Data Analytics: ISBN 978-1-4822-3212-7 p. 106


Clinical Research and Clinical Practice

• This conference is promoting open science for the acceleration

of discovery and development of evidence through secondary

use of clinical trial data

• The experience and methods developed in integrating clinical

practice data can be applied to the clinical research ecosystem

• First, get healthcare companies & researchers commit to share


Joe Kiani

• Founder, Chairman & CEO of Masimo

• Founder and Chairman of the Patient Safety Movement Foundation

• Founder and Chairman of the Masimo Foundation for Ethics, Innovation, and Competition in Healthcare

• Founder, Chairman & CEO of Cercacor

• Member of the Board of Directors at Children’s Hospital of Orange County (CHOC)

• President’s Cabinet, Chapman University


Clinical Trial Data vs Clinical Practice Data

• Data from clinical trials can come from many

sources, and take many forms.

• Standardization of data collection methods and

repository structure across studies will facilitate

ease of transfer and re-use.

• As in clinical trials, data from clinical practice comes

from many sources and takes many forms.

• So far 94 medical device companies have pledged

to put patients first by creating an ecosystem of

shared data


Bridging the Worlds

• The clinical trial landscape is not as evolved as

that in clinical practice.

• Manual interaction with clinical trial data can be

found at every step from collection, to storage

and retrieval, to analysis and eventually to

collaboration.

• Standardizing and automating the collection and

handling of clinical trial data will bring benefits

seen in clinical practice.


What we can Learn and Apply

• The evolution of in-hospital data storage and

retrieval is continuing. Data once stored in

paper records distributed across hospital

departments has become more centrally

accessible.

• Advances improving the clinical practice

ecosystem should be included in clinical

research collaboration and data repositories.


How do we move forward?

• Clinical Data Exchange Ecosystem will help

pave the way for Clinical Support

• Clinical trials conducted with the commercial

adoption of the clinical data exchange

ecosystem will lead to changes and

improvements in the system overall