HIMSS Clinical & Business Intelligence Community CBI Community... · 2018. 1. 25. · Shelley...

January 2018 Event

HIMSS Clinical & Business IntelligenceCommunity

#PutData2Work | #PopHealthIT | #PrecisionHIT

http://en.wikipedia.org/wiki/File:Twitter_bird_logo_2012.svg

Shelley Price, MS, FHIMSS

C&BI Community Organizer

Director, Payer & Life Sciences

HIMSS | Arlington, VA

Arthur Panov, MPH, CPHIMS

C&BI Community Co-Chair

Healthcare and Life Sciences Architecture

IBM Watson Health

Mike Berger, PE, CPHIMS

C&BI Community Co-Chair

Vice President, Population Health Informatics &

Data Science, Mount Sinai Health System

Welcome

Joanne Bartley, CAE

C&BI Community Organizer

Manager, Health Business Solutions

HIMSS | Chicago, IL

• Welcome

• HIMSS C&BI Community Updates / Announcements

• Presentation & Discussion:

“Building and Deploying Healthcare IoT Solutions with

Personalized Learning ”

Deepak S. Turaga, MS, PhD, Distinguished Research Staff Member

| IBM Research AI

• Wrap-Up / Next Steps

Agenda

Updates / Announcements

March 5 – 9, 2018

Venetian – Palazzo – Sands Expo Center

Remember! Pattern change to Monday – Friday

Welcome to HIMSS18!

C&BI Activities & Events – Education

• Preconference

• General Education

• Specialty Education

• Specialty Exhibitions & Sessions

1. AMDIS/HIMSS Physician Executive

Symposium: Shifting from Computer

Care to Patient Care

2. Business of Healthcare Symposium:

Going from Good to Great in a Value-

Based World

3. Compliance Symposium: Mastering

Compliance - What You Must Know

4. Coordinated and Connected Care

Symposium: Tackling the Challenge of

Connected and Coordinated Care

5. Innovation Symposium: Innovation As

a Strategic Imperative

6. HIMSS/SHIEC Interoperability & HIE

Symposium: Facilitating Person-

Centered Interoperable HIE to Manage

Complex Populations

7. Long-Term and Post-Acute Care

Symposium: Digital Health in and with

LTPAC Settings

8. Nursing Informatics Symposium:

Demonstrating Nursing Value Through

Health IT

9. Population Health Symposium: Chasing

One Medicine – The Next Generation of

Consumer- and Community-Centric

Healthcare

10. Precision Medicine Symposium: Journey

to the Summit Using Clinical and Business

Intelligence

Preconference Plus Pass

CME or CNE credit not available for this program and is not part of Preconference Plus benefit

C&BI Activities & Events – Education

• Precision Medicine Forum: From Promise to Reality –

Integrating Precision Medicine into Clinical Care

Wednesday, March 7, 2018 | Galileo 901

– Session #98 | Separating Hype from Reality

– Session #114 | Applying Genomic Intelligence and

Decision Support at the Point of Care

– Session #130 | Genomics Nursing and the EHR

http://www.himssconference.org/education/specialty-education/forums/precision-medicine

C&BI Activities & Events – Networking

• C&BI and PopHealth Reception | Tuesday, March 6 | 6-7pm

– Solutions Lab | Casanova Meeting Room | 1st Floor

– Complimentary; by invite only

We would like to thank our supporter:

http://www.himssconference.org/session/clinical-business-intelligence-and-population-health-community-reception

https://www.initiatesolutions.com/

C&BI Activities & Events – Exhibition

Solution Lab

Population Health

Business Administration

• Where: Casanova Meeting Room | 1st Floor

• When: during Exhibit Hall Hours

– Tuesday, March 6, 9:30am-6:00pm

– Wednesday, March 7, 9:30am-6:00pm

– Thursday, March 8, 9:30am-4:00pm

• What: interactive, one-stop shop dedicated to showcasing practical solutions that use IT to improve population health and healthcare administration. Features include education sessions, fireside chats, Meeting Pods, Solutions Bar and a special session each day with experts answering attendees’ questions

C&BI Community PresentationGuest Speaker(s)

We will take

questions at the

end of the

presentation…

…Please type your

questions into the

Q&A box.


© 2018 IBM Corporation

Building and Deploying Healthcare IoT Solutions with Personalized Learning Sugar.IQ

C. Aggarwal, L. Cao, Y.-C Chang, T. Dinger, M. Ng, R. Pavuluri, R. Saccone, S. Sathe, D. Sow, D. S. Turaga, L. Vu


17

Outline

Motivation

– Remote Diabetes Monitoring

– Initial Data Analysis

Building the Sugar.IQ Solution

– Application Design

– Distributed Stream Processing

Personalized Machine Learning

– Operationalizing Adaptive Modeling

– ML Lifecycle Management

– Need for Automation – CADS

Discussion


Complexity of Diabetes Disease Management

>95% of diabetes care comes from patients themselvesSignificant benefits and cost savings can be realized

A reduction of 5 hypo episodes for a patient per year results in ~700$ in emergency services and 9000$ in in-patient services per year

PATIENT

HEALTHY EATING

BE ACTIVE

MONITOR

TAKE MEDICATION

SOLVE PROBLEM

REDUCE RISK

COPE WELL

1 HbA1c300 meals 700 MBGs

1000 alarms25,000 CGM’s

1 HbA1c300 meals 700 MBGs

1000 alarms25,000 CGM’s

DOCTOR’S VISITDOCTOR’S VISIT

18


HOW HAVE I DONE?Important glucose management information

HOW AM I DOING?Real-time blood glucose and insulin information

A personalized solution that combines patient data from multiple sources to provide insights and advice to patients and help them effortlessly stay ahead.

WHAT SHOULD I DO?Predictive recommendations to help avoid incidents and stay ahead

Activity

Pump &

CGM

CGM Only

Food & Nutrition

Other(bio)metric

sensors

Contextual Data

FUTURE

PRESENT

PAST

Personalized Assistant for Diabetes Management

19


Goal: Can Watson predict Hypoglycemia in patients, post bolus?Base prediction rate – across all patients ~80% accuracy

Approach: Identify “insulin profiles” and patient groups to improve prediction

Data (100 patients)

Sensor Glucose

Bolus Wizard Settings (Insulin

Sensitivity, Carb Ratio, Insulin

Action Curve)

Bolus Wizard Entries

(Carbs, Correction BG, Active

Insulin)

Blood Glucose Bolus Delivered Basal Rate Setting

Hypos determined mainly by

Why is this important?

• “Personalized” prediction with significantly improved accuracy• Improved outcomes with real-time insulin and behavior recommendations

IBM Analytics – feature selection and clustering to generate patient groups

Sensor Glucose

+ Bolus Wizard

Settings + Blood

Glucose

Sensor

Glucose +

Carbs

Sensor

Glucose +

Bolus Wizard

Settings

Sensor Glucose

+ Bolus Wizard

Settings + Bolus

Wizard Entries

Sensor Glucose +

Day-of-the-week

Bolus Type +

Sensor

Glucose

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6

Hypoglycemic event prediction for each group ~90%* accurate with 3 hour lead timeImproves significantly over base prediction rate

*Results preliminary – based on very small dataset

Initial PoC Results: Predicting Hypoglycemia with MLMedtronic Watson Health Engagement

20


Prediction Results: Example of PersonalizationBased on Usage of Device

21

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Sensor, Wizard, BolusAmount, Type

HYPO HYPER NORM UNORM

HYPO 172 13 9 0

HYPER 15 46 0 0

NORM 1 0 262 0

UNORM 0 0 0 0

Classified as

List of Attributes

List of Attributes

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Sensor, Day of the Week

HYPO HYPER NORM UNORM

HYPO 944 41 0 0

HYPER 29 106 0 0

NORM 3 0 23 0

UNORM 0 0 0 1


Need for Personalized Machine Learning

Unlike monitoring hurricanes or tsunami, our monitoring and early warning

actually changes the behavior of the monitored subject

–Different users behave differently to these alerts

Such behavior change leads to fewer and fewer predictive signals

–Not all behavior changes explicitly observed

22


23

Outline

Motivation










Discussion


Sugar.IQ: Remote Diabetes Monitoring

24

MiniMed Connect

Watson Health Cloud

Develop models of Patient insulin regimen, Activity and wellness, and Patient Context

Apply models against streaming data to create real-time insights and provide the user with: Insulin Recommendations, Behavior Recommendations, and identified deviations and patterns

Doctor prescriptions and

Updates

Medtronic Store(Supplies, Accessories…)

Capture live streaming data and context from the devices

© 2018 IBM Corporation2

5

Search and log meals -Keep diabetes data in one

place

Review device data and glucose metrics from the past

to learn how to act in the future

Remembers patterns and displays marked food or activity

FOODDIARY

CGM & INSULINDATA REVIEW

Reminds users for better adherence and outcome

ADHERENCEINSIGHTS

GLUCOSEINSIGHTS

MOTIVATIONALINSIGHTS

Uncover behaviors associated with glucose

patterns

Encouragement to sustain positive outcomes

GLYCEMIC ASSIST

Comprehensive Platform Real Time Insights

Sugar.IQ Features for Generation 1

25


Sugar.IQ R1.0 Data Flow

26

Site-to-Site VPN DB2-to-DB2 CDC Replication

CRMGE

Centricity SalesForce SAP

WHC

Connect

Connect+

Sensor, Pump and Minimed Connect

Medtronic Infrastructure

DB2 for IBM

AWS

Mobile First Backend

NutrinoSensor and CRM Data

Streaming Analytics

Offline Analytics

Insights and Alerts

Nutrino

Other Sources of Data (e.g. Weather)

This is a generalizable template for AI based IoT Applications

http://images.google.com/imgres?imgurl=http://core.ecu.edu/psyc/wuenschk/Images/PASW_Icon.png&imgrefurl=http://core.ecu.edu/psyc/wuenschk/spss.htm&docid=Gdqy974mfCdoLM&tbnid=tAPgYyS7AH9xpM&w=60&h=77&ei=Q0_hUfXeFeXDygGypoGQCw&ved=0CAkQxiAwBw&iact=c

http://images.google.com/imgres?imgurl=http://img221.imageshack.us/img221/3478/spss4gt.png&imgrefurl=http://forums.wincustomize.com/96380/page/4/&docid=-CBhhuzBmdrtvM&tbnid=AFowqcy4S5i20M:&w=256&h=256&ei=Q0_hUfXeFeXDygGypoGQCw&ved=0CAIQxiAwAA&iact=c


Low Latency, Personalized Analytics

App users are accustomed to “instant” gratification

–Glucose threshold change needs to be reflected on screen quickly

–Need to deliver insights in a “timely” manner, such as when a user is

having chicken alfredo, what happened previously with eating alfredo

–Degree of personalization goes beyond Google, Facebook

Live data arrives with every 5 mins

–Round trip latency of calculating and delivering insights, < 5 mins

–1 KB per user every 5 mins, ~500 KB per user per day

– In-memory data kept for at least 3 months, ~50 MB per user

–Historical data retained for multiple years

Active users ramps from 300 to reach 10,000 by 2018 year end

27


Sugar.IQ Application:Functional Blocks for Pattern based Insights

28

Live Data

Data Cleaning

MyData

Glycemic Features

Motivational Insights

Trigger Logic

Glycemic Insights

Insight Prioritization

Batch Data

Meal Data

User Feedback


Happening Behind the Scenes

29

Mobile First Backend Streams (cached histogram) DB2 MQ

Mobile First Backend

MQ

4-6 seconds round-trip latency


30

Outline

Motivation










Discussion


Requirements on Streaming Analysis

Velocity: Streaming Data and Analysis

– Range of data rates: low decision latency

Velocity: Loss Tolerant Analysis

– Highly noisy and lossy data, graceful degradation under failure

Variety: Multimodal Data Analysis

– Correlated primary sources, Structured or Unstructured

Variety: Dynamic and Adaptive Analysis

– Source, data, analysis and resource variability

Volume: Distributed Analysis

– Decomposable into flowgraphs, distributed data, processing

Volume: High Performance

– Real-time, Low-latency, high throughput, scaling

31


The Evolution of Stream Computing

32

Persistent Data

StreamingData

TraditionalData

Non-TraditionalData

Stream Processing

Map-reduce

Databases, Data Warehouses

Complex Event Processing

Active Databases

Continuous Query

Publish-Subscribe

Velocity

Va

rie

ty

In-memory Cluster Computing


Introduction to Stream Processing

Incremental tuple by tuple processing

FX rate

Internal Crossing

Weather

Exchange

Value

Added

Feed

Stream

Tuple

Operator


Introduction to Stream Processing

FX rate

Internal Crossing

Weather

Exchange

Value

Added

Feed

Supports Naturally Distributed Applications

Stream

Tuple

Operator


Streaming System: Streams Overview

35

Integrated Development Environment

Scale-Out Runtime Analytic Toolkits

Development and Management Functional and OptimizedFlexibility and Scalability

Cloud and on premise available for flexible deployment


Development Environment

36


Development and Management

Streams Processing Language

Visual Composition Tools

Wrappers for legacy code written in Java, C++, Python, R, and Matlab


Monitoring and Debugging Support

37


Development and Management

Web based Monitoring Console


Runtime

38

Scale-Out Runtime

Flexibility and Scalability

•High-performance clustered runtime

•Large scale deployment

•RHEL, CentOS, SUSE Linux Enterprise Server

•X86 and Power multicore hardware

•InfiniBand support

•Ethernet support


Runtime: Advanced Features

39

Scale-Out Runtime

Flexibility and Scalability User Defined ParallelismApplication Resiliency

System High Availability


Runtime Scaling

40

Scale-Out Runtime

Flexibility and Scalability

~200B records/day

Application for Service Quality Monitoring


41

Streams Processing for Sugar.IQ

Glycemic Features

Motivational Insights

Over 40000 lines of Streams SPL code

Glycemic Insights

MyData

Data Ingest, Parsing

Data Cleaning


42

Streams Live Graph for Sugar.IQ (scaled up/out to 10x parallel)

Continuous scaling to 2000 users at 100x real-time (200,000 equivalent users)3496 Streams operators, 4796 data streams


43

Outline

Motivation










Discussion


Hypoglycemia Prediction: Modeling and Data Analysis

44

Dataset: 10000 patients spanning 4 years– Limited to sensor-only readings, for broad applicability across sensor

only as well as pump + sensor and sensor only patients

Novel Modeling Approach: – Users grouped based on their observed temporal behavior, characterized

by several first of a kind features. Per-group predictive models

developed.

– Individualized predictions achieved with user-specific ensembles across

per-group models

Results: 85-89% accuracy# at multiple horizons (2hr and 3hr)– Significantly higher than any other reported results in literature

– Multiple patent disclosures, and publications

81.8

87.1

72.6

83.2

74.3

80.8

84.5

88.5

80.4

84.7

79.681.9

88.289.8

85.9 86.4 85.882.8

70

75

80

85

90

95

2Hr,2K(old) 2Hr,10K(new) 3Hr,2K(old) 3Hr,10K(new) 4Hr,2K(old) 4Hr,10K(new)

MinAUC

MeanAUC

MaxAUC

First of a kind features* that capture short-term, long-term, glucose and insulin measurements

*Invention disclosures filed#Measured as Area under the curve (AUC)

Groupings capture patient temporal behavior

81.8

87.1

72.6

83.2

74.3

80.8

84.5

88.5

80.4

84.7

79.681.9

88.289.8

85.9 86.4 85.882.8

70

75

80

85

90

95

2Hr,2K(old) 2Hr,10K(new) 3Hr,2K(old) 3Hr,10K(new) 4Hr,2K(old) 4Hr,10K(new)

MinAUC

MeanAUC

MaxAUC

Prediction Horizon, Patient Population Size

AU

C (

%) Low-variance, high quality models that are

temporally stable


Analytic EcosystemOperationalizing Analytic Models: From Offline to Online

45

Data mining

Business logic

• Data representation

• Raw data preprocessing

• Aggregation, filtering

Stream mining

Scoring

operator

SPSS, SAS

Hadoop, Big Insights

Connect

Connect+



Hypoglycemia Prediction: Need for self-learning models

Unlike monitoring hurricanes or tsunami, our monitoring and early

warning actually changes the behavior of the monitored subject

–Such behavior change leads to fewer and fewer predictive signals

–We have evidence showing negative correlation between pump’s

low glucose alert and hypoglycemia for some users

Three layer predictive model allows for continuous learning and

adaptation

–Bottom layer: Bolus driven segments can be refreshed periodically

–Middle layer: Predictive model per segment built adaptively based

on measured model quality

–User personalized model: Ensemble across segment models with

weights specific to user

46


Business logic

Data mining

Trigger generation

of new model

Ensemble of models

Model

Scoring

operatorModel

Scoring

operator

ii




SPSS, SAS

Hadoop, Big Insights

Analytic EcosystemOperationalizing Analytic Models: From Offline to Online Learning

Connect

Connect+


47


Operationalizing Self-Learning Models at ScaleHow do we retrain 100s of thousands of personalized models

48

Business logic

Trigger generation

of new model

Ensemble of models

Model

Scoring

operatorModel

Scoring

operator

ii




Connect

Connect+


Integrate CADS into Machine Learning Model Lifecycle


49

Outline

Overview

– Business Case

– Timeline

Show and Tell: Sugar.IQ Solution

– Behavioral Insights

– Hypoglycemia Prediction

– Demo

Glimpse of Analytics behind Pretty Pictures

Close the Loop - Analytic Lifecycle Management

– From Data Analysis to Operational Models


Discussion


50

Questions?

Deepak S. Turaga, MS, PhD

Distinguished Research Staff Member

IBM Research AI

• Want to get involved?

Topic or Speaker ideas

Key Note Presenter

Blogger | Tweeting

• Community Website

Visit the ‘Session Recordings’ tab at

www.himss.org/ClinBusIntelCommunity for

a copy of this or previous presentations

Wrap-Up / Next Steps

Contact

Shelley Price or

Joanne Bartley


http://www.himss.org/ClinBusIntelCommunity


Earn CAHIMS & CPHIMS credit!

The HIMSS C&BI Community is pleased to offer webinar attendees

up to one continuing education (CE) hour for use in fulfilling the CE

requirements of the Certified Professional in Healthcare Information &

Management Systems (CPHIMS) and the Certified Associate in

Healthcare Information & Management Systems (CAHIMS) programs.

Visit the C&BI Community website for more information.


http://www.himss.org/clinical-business-intelligence-webinar-series


JOIN US!

Next meeting: Tuesday, February 6, 2018

SPECIAL JOINT EVENT WITH THE HIMSS

INNOVATION COMMUNITY!

Innovative uses of analytics to improve

outpatient primary care delivery

Speaker(s):

Dr. Danielle Oryn, DO, MPH, Chief Medical

Informatics Officer | Petaluma Health Center

Shaun Nelson, MPH, Senior Data Analyst |

Petaluma Health Center



Co-Chairs:

Mike Berger, PE, CPHIMS Vice President, Population Health Informatics & Data Science Mount Sinai Health [email protected]

Arthur Panov, MPH, CPHIMS Healthcare and Life Sciences ArchitectureIBM Watson [email protected]

HIMSS Community Organizers:

Shelley Price, MS, FHIMSS Joanne Bartley, CAEDirector, Payer and Life Sciences Manager, Health Business SolutionsHIMSS [email protected] | @SPriceHIMSS [email protected]

FY18 Leadership & Contact Information


mailto:[email protected]



https://twitter.com/spricehimss



Thank you!!!



APPENDIX

Ellen Harper, DNP, RN-BC, MBA, FAAN*FY18 C&BI Committee Chair

Adjunct Faculty, U of Minnesota School of Nursing

President, CEO, Blue Water Informatics, LLC

Claudine Beron, PMPFY18 C&BI Committee Vice-Chair

CEO

Initiate Government Solutions, LLC

Meg Broderick, MBA, MPA, CPHIMS*Consultant

Sharon Davis, CPHIMS, PMPSr. Project Manager

VIZIENT

Jeff Fuller, MS, FACHE*

Executive Director, Analytical Solutions

U of North Carolina (UNC) Healthcare System

Kevin Gormley, PhD, MEPrincipal Data Scientist

MITRE

Mitch KwiatkowskiSr. Director, Informatics

Gateway Health

Raj Lakhanpal, MD, FACEP*CEO

Spectramedix

Anne Park, MPH, MSMIS*Sr. Business Systems Analyst

MD Anderson Cancer Center

Anthony C Villanueva, CPHIMSCIO, Vice President of IT/IS

Neighborhood Health

Anthony T. Williams, MBA*Chief Information Officer

American Family Care, Inc.

Amber Zimmermann, BSN, MBA, RN-BCSr Manager, Health Data Analytics

Philips Healthcare

* Indicates a returning

committee member

About Us: FY2018 C&BI Committee Members

Community Co-Chairs

Mike Berger, PE, CPHIMS

Vice President, Population Health

Informatics and Data Science

Mount Sinai Health

Arthur Panov, MPH, CPHIMS

Healthcare and Life Sciences Architecture

IBM Watson Health

C&BI for Population Health

Task Force Co-Chairs

Marius Petruc, MD, MS

President

Informatics Solutions, LLC

Michelle Vislosky, M.B.A., FACHE

Director of Health System

Partnerships

Syapse

About Us: FY18 C&BI Community, Task Force, Workgroup Leadership

Precision Medicine Workgroup ChairBG Jones

SVP - Business Development

PierianDX

About Us: C&BI Community

Looking to better leverage clinical & business intelligence tools, technologies,

and strategies to help you and your organization meet patient care delivery,

clinical and health outcome, and business operation goals? Our community

supports activities that promote peer-to-peer networking, problem solving,

solution sharing, and education.

HIMSS C&BI Community webinars let members dig into pertinent subjects

throughout the year. Our ongoing web-based programs cover topics such as:

Data aggregation and access through EDW and cloud solutions

Data management, validation, quality, and integration

Descriptive, retrospective, predictive, and prescriptive analytics

Governance – data and program

Population health management – including attribution and risk stratification

Precision medicine

Predictive modeling

Reporting, dashboards, and visualization techniques

Resource management

Skills sets and staffing

www.himss.org/ClinBusIntelCommunity

http://www.himss.org/ClinBusIntelCommunity

About Us: C&BI Community

• Open to all HIMSS members (current membership: approx 7,800 people). Join the

C&BI Community today:

1. Login into your HIMSS account in the Member Center

2. Click the tab "My Involvement" | select "Participation" dropdown

3. Click on "Edit Participations"

4. Check the “Clinical & Business Intelligence Community” box

5. Click "Save"

• Will meet virtually 6 times/year

• Agenda for the meetings may include:

News you can use! New content and events

2-Minute Drills presented by various Community members

Topical discussion with key note presenter

The ‘2-Minute Drill’ is based loosely on the sports analogy, and in this case

is a fast-paced (short in length) presentation on a hot, emerging, or timely topic, news event (e.g. research paper, game-changing market or technology news), or recent and relevant event (e.g., federal public meeting, legislative/federal/judicial news, critical conference or educational event).

2-Minute Drills foster greater peer-to-peer networking, member engagement, problem solving, solution sharing, and education. If you are interested in presenting any drills,

please contact Shelley Price.

http://marketplace.himss.org/My-Account/Participation

http://www.clker.com/clipart-73060.html

javascript:edit(13830)

About Us: Task Force

C&BI for Population Health Task Force

CO-CHAIR: Marius Petruc, MD, MS | President | Informatics Solutions, LLC

CO-CHAIR: Michelle Vislosky, MBA, FACHE | Director of Health System Partnerships

| Syapse

This group creates resources and tools that employ practical guidance and

unbiased information to help healthcare organizations (providers, hospitals,

integrated delivery networks, health plans and other stakeholders) use C&BI to

harness, use and analyze data captured in the healthcare setting to execute

population health management initiatives and improve care and health

outcomes.

Meeting times: 3rd Tuesday of the month, 4:00-5:00pm ET

HIMSS Clinical & Business Intelligence Community CBI Community... · 2018. 1. 25. · Shelley...

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