Internet of Things Future Vision and Nursing

InvolvementThomas R. Clancy, PhD, MBA, RN, FAANClinical Professor and Associate Dean

Faculty Practice, Partnerships, Professional DevelopmentSchool of Nursing

The University of Minnesota

Objectives

• Describe the relationship between growth in information, complexity and the challenges of big data.

• Define machine learning and discuss the emergence of augmented intelligence.

• Describe the information value loop and how the Intranet of Things enables it.

• Provide current and future examples of how big data, The Intranet of Things and artificial intelligence will change the role of nurses.

Information

Information Theory

• Information is the ordered arrangement of matter that represents something.

• Meaning is not embodied in it. Ordered matter or packets of

information in solids

Humans have the Unique Capacity to:

• To create, recombine, store and process complex ordered states or information in our minds.

• Disembody information into useful artifacts such as books, buildings, roads and other “things” that improve our survival and quality of life.

Ordered matter or packets of information in solids

Information: Bits & Bytes

• Information can also be represented in binary symbols or as bits.

• The ordered patterns of binary symbols represent information.

Ordered matter or packets of information in solids

How Information Grows

Information begets more information:

• Evolution & human cognitive capacity.

• Emergence of networks to share individual packets of information where gaps occur.

• Information technology enables machine aided intelligence

• Super IntelligenceUnique Information is growing

66% per yearVarian, H. and P. Lyman (2003). How Information Grows @

http://kk.org/thetechnium/the-speed-of-in/

Long Term World Growth in Gross Domestic Product/Per Capita

Moores Law & Technology

The Perfect Storm: Mobile Technology, Sensors,

The Intranet & Artificial Intelligence

2020 X Artificial Intelligence

2010 X X X Intranet

2000 X X X X X Mobile Tech

1990 X X X X X

1980 X X X

1970 X X X

Year 1960 X X X X Sensors

1950 X X X

1940 X X X

1930 X

1920 X

1910 X X

1900 X X X X

Mobile

Technology

Mobile

Phone Call

Pager Cordless

Phone

Hand held Mobile

Phone

Commercial Mobile

Phone

Apple PDA Bluetooth Camera

Phone

Blackberry

Smart Phone

Apple

Smart

Phone

Android

Smart

Phone

Sensor

Technology

Temperat

ure Wind Humidity Biosensor Radiation Acoustic Velocity Force Light Motion Position Chemical

Internet ARPANETFTP/TCP/IP/Et

hernet Internet

WWW/Java/Net

scape

Amazon/Google

/eBay

iTunes/Faceb

ook/Napster

Skype/YouT

ube Twitter Instagram Snapchat

Artificial

Intelligence Birth of AI

Rules Based

Systems AI Winter Expert Systems Neural Networks

Evolutionary

Algorithms

Baysian

Networks

Deep

Learning

https://en.wikipedia.org/wiki/Moore's_law#/media/File:Transistor_Count_and_Moore%27s_Law_-_2011.svg

Sources of Data in Healthcare • Electronic Health Record

• Health Insurance Claims

• Sensor Data (2.9 billion)

• Geo-spatial Data (GPS mapping)

• Intranet of Things (IoT)

• Social Media (1.8 billion subscribers – top 5)

• Patient Reported Outcomes (quantified self movement)

• Human Genome (6 billion/pair)

• Financial Systems (credit cards, bank accounts)

• Environmental and Weather Data

Information as it Grows Becomes Complex• Information and

complexity are closely associated.

• As we move along the continuum from randomness to complete order, it is not only the amount of matter present, but the specific arrangement of it that increases information and its complexity.

Randomness OrderComplexity

Low Information

Low Information

HighInformation

Gas Crystal

Information Complexity: Nine Factor Binary Matrix

One Person = 1,024 Combinations

ID Age Ht. Wt. Pul. Inc. Edu. Occ. Lab Xray

Age N Y N Y N Y N Y

Ht. Y Y Y Y N Y N Y

Wt. Y Y N N Y Y Y Y

Pul. N N Y N Y N Y N

Inc. Y N Y N N N Y N

Edu N N N N Y N N Y

Occ Y Y Y Y Y N Y N

Lab N N N Y Y Y Y N

Xray N N Y Y N N Y N

Finding Hidden Patterns

•What is the probability of solving a Rubik’s cube by randomly turning the cubes? 1 in 43,252,003,274,489,856,000

(4.3 x 1019)

324 sides

Machine Learning

• The science and technology of systems that learn from data.

• Used to solve complex problems and describe the structure of the data generating processes.

Data Methods: Machine Learning

Artificial Intelligence:

Machine Learning

• Decision Trees

• Neural Networks

• Bayesian Methods

• Evolutionary Computation

• The goal is to predict rather than explain.

• There are many predictor variables (25+).

• There are many complex variable interactions.

• The predictors have non-linear relationships to the target variable.

DemographicsDiagnosis codesProcedural codesProviderUnitBSNMedicationsNursing HoursLength of Stay

Diagnosis Diagnosis + Procedure CodeDiagnosis + Procedure Code + AgeDiagnosis + Procedure Code + Age + LOSDiagnosis + Procedure Code + Age + LOS + ICUDiagnosis +Procedure Code + Age + LOS + BSN

1. Select concepts

2. Remove irrelevantfactors

3. Search for positiveconjunctive conjectures

Diagnosis + Procedure CodeDiagnosis + Procedure Code + AgeDiagnosis + Procedure Code + Age + LOSDiagnosis + Procedure Code + Age + LOS + ICU

4. Remove negativeconjunctive conjectures

5. Build Algorithmicrules around positive

conjunctive conjectures

• Data Scientist• Informatician• Statistician• Domain Experts• (nurse scientist)

Decision Tree: Predictive Model

CAUTI

Garter Hype Curve:Machine Learning

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

AI Algorithms -> Augmented IntelligenceEmbedded in Devices

Park, J. (2016). Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract Infections Using Electronic Health Records and Nurse Staffing Data. Dissertation. University of Minnesota

The Information Value Loop

• Create• Sensors (generate data)

• Communicate• Network (transmit data)

• Aggregate• Standards (gather data)

• Analyze• Augmented intelligence

(patterns and signals)

• Act• Augment (change)

behavior

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

Create

Communicate

Aggregate

Analyze

Act

The Intranet of Things

“The integration of people, processes and technology with connectable devices and sensors to enable remote monitoring, status, manipulation and evaluation of trends of such devices.”

https://www.youtube.com/watch?v=sGQeWRpmglU

Peter Lewis. First use of the term given at a presentation toU.S. Federal Communications Commission (FCC) in 1985.

The Intranet of Things

Smart PhonesRobotics• Surgical• Care-assist• Gero-tech

Desktop Computers• EHR

Implantable Devices• Diabetes• Cardiac

Wearable Devices• Exercise• Sleep• Stress

Drones

MobileTablets

In-homeSensors• Motion• Alarms• Medication

Smart clothing

Medical Devices• ECG• Vital signs• Ultrasound

Telehealth• Virtual

visit

Nanotechnology

Lab/Radiology

Intranet of Things

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

Create: Sensors

Types: • Passive vs active

Growth Factors• Decreasing cost ($2-$.40)

• Improved computation (x2/3yr)

• Decreasing size (3 – 10/phone)

Challenges• Power consumption (battery)

• Security (ongoing issue)

• Interoperability (communication

std’s, proprietary)

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

Communicate: Networks

Types:• Wired vs Wireless

Growth Factors• Data rates (2Kbs analog, 1Gbps digital)

• Intranet transit prices ($120 - $.63 Mbps)

• Power efficiency (Bluetooth Low Energy 50%)

• IPv6 adoption (address conversion IPv4)

Challenges• High bandwidth network

penetration (conversion to 4G limited)

• Security (limited with IP connections)

• Power (increasing power demand)

• Interconnections (http/interoperability)

PAN (personal area network)Bluetooth, ZigBee,

Near Field Communication, Wi-FiLAN (Local area network)

Wi-Fi, WiMAXWAN (Wide area network)

WiMAX, weightless, cellular technologiessuch as 2G, 3G, 4G (LTE)

USB orEthernet

Wired

Wireless

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

Aggregate: Standards

Types of Standards:• Technology and regulatory

Growth Drivers• Network & communication

standards are emerging

• Converging standards

• Vendors & IEEE/ETSI

• Qualcom/Cisco

• Data aggregation standards

• Unstructured data (ETL)

• Security & PHI (HIPPA)

• Data mart standards

• Technical Skills & big datahttps://www.youtube.com/watch?v=qkW66bOlkBU

Network Protocols• Machine identification• Multiple protocols

Communication Protocols• Common language• HTTP & IoT

Data Aggregation• Streaming data• Relational databases• SQL vs NoSQL• Distributed Databases

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M.

Inside the Intranet of Things. A primer on the technologies

building the IoT. Deloitte University Press.

Challenges

A Connected Planet

https://www.semiwiki.com/forum/content/5559-quick-history-internet-things.html

Analyze: Augmented Intelligence

Types• Descriptive (describe)• Predictive (cause)• Prescriptive (recommend)

Growth Factors• Access to big data• Open access analytics &

crowdsourcing• Real-time data processing

• Complex event processing tools (CEP)• Parallel processing

Challenges (7V’s)• Veracity/validity of data• Legacy systems (unstructured &

real-time processing of data)

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

Act: Augmented Behavior

Types:

• Augmented intelligence drives informed action, while augmented behavior is an observable action in the real world.• Machine to machine

(M2M)

• Machine to human (M2H)

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

Intranet of Things: The Information Value Loop

• Augmented Behavior is the end of the information loop and results in an action:• Recommendation to a

provider from a BPG

• Text message to a diabetic patient to increase their insulin.

• Data Science Behavioral Science

Holdowsky, J., Mahto, M., Raynor, M. and Cotteleer, M. Inside the Intranet of Things. A primer on the technologies building

the IoT. Deloitte University Press.

1.Create(sensors)

2. Communicate(Networks)

3. Aggregate(AugmentedIntelligence)

4. Analyze(Integration)

5. Act(Augmented

Behavior)

Blood Glucose Monitoring

1. CreateBlood glucoselevels created

by a home monitor

2. CommunicateBlood glucose levels shared via

Bluetooth and the Intranet

3. AggregateBlood glucose levelstracked over time for

and individual or Population

5. ActPatient adjusts medications

and other factors(Augmented Behavior)

4. AnalyzeBlood glucose levelsare trended and care

plan created.(Augmented Intelligence)

Personal Analytics & Precision Medicine

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

Human Graphic Information System (GIS)

• Multiple layers of demographic, physiologic, anatomic, biologic and environmental data about a particular individual.

https://twitter.com/erictopol/status/449529091536863233

Topel, E. (2015). The Patient Will See You Now. Basic Books,New York.

Algorithmic Medicine

IBM Watson Health (cognitive computing-500G/S)

• Genomics

• Drug Discovery

• Value Based Care

• Patient Engagement

• Oncology

• Care Manager• https://www.youtube.com/watch?v=BYXIg1S7

nKk

Google DeepMind Health (open source)

• Founded in London (2010)

• Primary data source (NHS)

• Primarily unsupervised learning algorithms.

• Vision is to combine with neuroscience methods

https://www.youtube.com/watch?v=rXVoRyIGGhU

https://www.youtube.com/watch?v=wQU9wsFnO4k

Genomic – Precision Editing

Crisper-Cas9

• Molecular scissors target and snip out aberrant regions of genetic code, which can then be replaced with correct sequences.

http://www.yourgenome.org/facts/what-is-crispr-cas9

Face2Gene – Diagnostic Testing

• Deep learning algorithms classify photos into syndromes (for example Downs Syndrome).

• Software converts a patients photo into mathematical facial descriptors and compares them to database.

https://suite.face2gene.com/clinic-deep-phenotyping-of-genetic-disorder-dysmorphic-features/

Prevention: Asthma Attacks

Sensor Cluster:• Air quality

• Pollen

• Inhaler use

• Geo-location

• Breath nitric oxide

• Lung function –Smartphone app

• RR, Temp, O2 sat.

Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring. Ashraf Darwish and Aboul Ella Hassanien : Accessed at Sensors website: http://www.mdpi.com/1424-8220/11/6/5561/htm

Prevention: Heart Failure Events

Sensor Cluster

• Beat to beat variability

• Fluid status

• Sleep quality

• Apneic spells

• Vital signs

• Lab tests (via smart phone app)

• Med. adherence (via digitized pills)

https://www.youtube.com/watch?v=-uTsMCvT7X8

Care Coordination & Advanced Monitoring Devices

Implantable Continuous Glucose Monitoring

•Provides education and monitoring

=>

http://www.medtronicdiabetes.com/treatment-and-products/continuous-glucose-monitoring

Health Coaching Tools: Patient Engagement and The Quantified Self

Wearable Computing

• Activity monitors

• Diet & weight loss monitors

• Sleep and mood

• HealthIt.gov

http://www.ted.com/talks/gary_wolf_the_quantified_self?language=en

http://www.healthit.gov/patients-families/stay-well#devices

Health Maintenance and Home Monitoring Devices• Home sensing

devices • Weight scale • BP monitor • Mattress monitors• Baby monitors• Spirometer

medication monitoring

• Pedometer http://video.brookstone.com/v/34618/fitbit-aria-wi-fi-smart-scale-l2giftsprice-l3gifts200/

http://www.youtube.com/watch?v=R-ypgw03sy0

Smart Homes: The Hospital Beds of the Future

• Video monitoring

• Continuous VS

• Gait sensors

• Smartphone symptom checkers

• Handheld Xray, lab

• Medication adherence dispensers

• Emergency alerts

• Remote temp control & security alarms

Image: http://www.iotforreal.com/lowes-enters-the-smart-home-market/2827

Safe Homeshttps://www.youtube.com/watch?v=C3FS8-Ka7SU

Augmented/Virtual Reality

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

Augmented and Virtual Reality

• VR uses software to simulate 3D images, sounds and sensations to isolate and surround you.

• AR overlays views of the physical world with fabricated images that engage users.

https://www.youtube.com/watch?v=N3ywcoqR4Co

https://www.youtube.com/watch?v=ysnVRWaYjLc

https://theglobalhealthnews.com/augmented-reality-makes-presence-felt-healthcare-industry/

General Purpose Machine Intelligence

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

When Computers Outperform HumansNarrow AI

When tasks are easily broken down into sequential, smaller arithmetical components, computers outperform the human brain.

Year Machine Beat Reigning Champion

• Backgammon (1979)

• Chess (1997)

• Checkers (2002)

• Scrabble (2002)

• Bridge (2002)

• Jeopardy (2010)Bostrom, N. (2014). Super Intelligence:Paths, Dangers, Strategies. OxfordUniversity Press.

Strong vs Weak Artificial Intelligence

Weak AI (Narrow)

• Non-sentient AI

• Focused on a limited number of narrow tasks.

• Most AI today is “weak”.• Siri – hybrid AI combining

several weak AI techniques and big data.

Strong AI• General intelligence.

• Ability to apply intelligence to any problem rather than on one specific problem.• Brain Emulation and Mind

Uploading - scanning mental state (including long-term memory and "self") of a particular brain substrate and copying it to a computer.

Machine Level Human Intelligence (MLHI)

When will human level machine intelligence (strong AI) be attained?

Year Percent Chance

2022 10%

2040 50%

2075 90%Bostrom, N. (2014). Super Intelligence:Paths, Dangers, Strategies. OxfordUniversity Press.

Super-intelligence: 50% chance 30 years after human level intelligence

MLHI

Smart Robots

https://www.artificialintelligenceonline.com/16353/machine-learning-is-at-the-very-peak-of-its-hype-cycle/

Smart Robots

• Sense• LIDAR (point cloud)• https://www.youtube.com/watch?v=5M1toZ

swwbA

• Think• Big data/machine

learning & AI• https://www.youtube.com/watch?v=O1Z

hWv84eWE&index=1&list=PLus_8yixy1UBIpIcT67_wmB_Pxd4g0KrC

• Act• Automotive Industry

• https://www.youtube.com/watch?v=-7xvqQeoA8c

https://www.youtube.com/watch?v=aWxF_ZXs-_o

The Next 10 Years….

Top Technology Trends

• Nanotechnology

• Information Tech

• Networks

• Neurotechnology

• Biotechnology

• Robotics

• Quantum technology

Canton, J. (2015). Future Smart: Managing the Game-Changing Trends That Will Transform Your World. DeCapo Press.

The New Future for Nurses Will Be Driven By:• Accelerated change

• Fast innovation

• Smart technology

• Predictive systems

• Connected markets

• Digital everything

• Mobile commerce

Canton, J. (2015). Future Smart: Managing the Game-Changing Trends That Will Transform Your World. DeCapo Press.

Image: http://www.accelerationwatch.com/

Emerging Roles

• Expanded scope of practice - APRN

• Digital Care Coordination

• Personalized medicine health consultant

• Population management teams

• Nurse data scientist

• Nurse entrepreneurs

https://thenpmom.wordpress.com/2012/01/01/the-future-of-nursing-a-nurse-practitioners-perspective/

Questions?Thomas R. Clancy, Phd, MBA, RN, FAAN

clanc027@umn,edu