microsoft: a waking giant in healthcare analytics and big data

© 2015 Health Catalyst

www.healthcatalyst.com

April 2015

Dale Sanders

Microsoft: A Waking Giant In

Healthcare Analytics and Big Data

Creative Commons Copyright – Attribution Required

“In today’s world, business

moves at the speed of

software.” -- Dale Sanders

Great people and great facilities are not enough

anymore.

Software is the enabling or disabling factor to

success in today’s business world.2

Why should C-levels care about

topics like this one?

For ExampleThink about the impact that good and bad software is having on the

speed of these critical business transformations in healthcare, alone

• Healthcare.gov

• Population Health Management

• Accountable Care & Value Based Reimbursement

• EHR interoperability

• Detailed cost accounting

• Patient engagement

• Analytics at the point of care that can help raise quality of care, lower cost of care, and speed translational research

3

On a scale of 1-5, what is your overall perception of

Microsoft as a company– its people, products, and

culture? 307 respondents

1. Very negative – 3%

2. Negative – 10%

3. Neutral – 32%

4. Positive – 45%

5. Very positive – 11%

4

Poll Question

Agenda

• My up and down experiences with Microsoft

• Microsoft’s cultural and technological transformation

• Microsoft’s analytics options

• SQLServer, PDW, APS

• Microsoft Azure

• PowerX product line

• Data visualization and manipulation tools

5


www.healthcatalyst.comProprietary and Confidential

My Life On Microsoft

6

A few times, I wanted to poke my eye out

7Thank you for the cartoon, Darren Merinuk



Analytic Roadmaps for

Healthcare

9

*-- Sanders D, Protti, D, Electronic Healthcare, 11(2) 2012: e5-e6

Poll Question

At what Level does your organization consistently

and broadly operate in the Analytics Adoption Model?

248 respondents

0 – 14%

1-2 – 34%

3-4 – 33%

5-6 – 14%

7-8 – 5%

10

“Closed Loop Analytics”

“Presenting data in the workflow of decision making,

such that the data optimizes the outcome of the

decision.” (Sanders, HIMSS 2015 )

Physicians are 15x more likely to modify their

decisions about patient orders and protocols if

presented with data at the point of care, as opposed

to presenting data in “offline” clinical quality

improvement meetings. (Komomoto, BMJ, 2007)

11

Embedded best practices, decision support & care team coordination that support the Triple Aim

EHR embedded popula-tion analytics tailored for personalized medicine at the point of care

EHRClinical Decision Support

EDWClinical Quality Analytics

Define clinical best practices & requirements for embed-ded decision support & care team coordination

Use aggregate views of clinical data for case mix & protocol optimization

Derive population-based health system models for predicting demand

5

8

11

6

9

12

i ii

Executive & Clinical Leadership

Create a cultural expectation for evidence based medicine and use of clinical pathways & standard protocols

10

Enterprise Clinical Teams

Act on process & outcome data using protocol-based practice standards

Identify new cohorts & gauge practice variations

Clinical, EHR & Analytical Teams

Generate comparative & outcomes data, implement order sets, protocols and decision support rules. Develop & validate clinical models 4

7

Start Here

‘Closing the Loops’ on Clinical Outcomes to Optimize QualityUsing an Electronic Health Record, Enterprise Data Warehouse & Clinical Analytics to Generate Local Evidence

Information SystemsSupporting DataDecisions & Actions

© 2015 Contributing authors, listed alphabetically: Eggert C, Moselle K, Protti D, Sanders

D.

Align practice informed by analytics

Tailor protocols using better data

Man

age

Serv

ices

Op

tim

ize

Cap

acit

yD

eliv

er C

are

Loop A: Patients

Loop B: Protocols

External EvidenceLiterature, Research

Other Data SourcesExternal, Financial

iv

Internal Evidence

Internal Evidence

EHR: Electronic Health RecordEDW: Enterprise Data WarehouseMTTI: Mean time to improvementSOPA: Span of providers affected

COptimize system on quality & cost

Loop C: Populations

Internal Evidence

iii

Confidential Draft

Mar 21, 2015

Previous Box 6: Assess data quality, cohorts & interventions linked to outcomes

Include socio-economic determinants of health in clinical care management best practices

CLINICAL QUALITY GOVERNANCESet improvement priorities 1

“Closed Loop Analytics”Mean Time To Improvement and Span of Population Affected

Loop C: Populations

● MTTI: Years, decades

● SPA: Millions, several hundred thousand

● Analytic consumers: Board of Directors, executive leadership team,

Strategic plans and policy

Loop B: Protocols

● MTTI: Weeks, months

● SPA: Subsets of patients– hundreds, thousands

● Analytic consumers: Care improvement teams, clinical service lines

Loop A: Patients

● MTTI: Minutes, hours

● SPA: Individual patients

● Analytic consumers: Physicians and patients at the point of care

13

Big vs. Small DataThe ROI of data to Population Health

14

Volume, Ability, ActThe volume of data far outpaces our ability to

analyze and act on data… but we think otherwise

15

Finding optimal data volume

16



Microsoft’s Cultural and Technological Transformation

17

The Innovator’s Dilemma

Microsoft has shown the repeated ability to overcome this…

19

Sacrificing the Sacred Cows

20

Microsoft Openness

21

• Over the last three years, the single largest contributor of code to Open Source

• 20% of Azure infrastructure runs on Linux

• .Net is now in the Open Source community

• Tight integration with Hadoop through Hortonworks

• Support for Dockers containers

• Microsoft owns 310 Android patents

• Supports Facebook’s Open Compute data center project

• Third largest contributor to the Linux kernel

Steve Ballmer, 2001: “Linux is a cancer”

Satya Nadella, 2014: “Microsoft loves Linux”



Microsoft’s Analytics Options In The New World

23

The Transition Period• Too early to go all-in on Hadoop & NoSQL

• Too late to go all-in on relational databases

• You have to straddle both and this is where Microsoft’s products and

strategy excel

24

Microsoft’s Analytics Product Lines

Lots of good, familiar patterns and integration across these products

PowerBI

Excel

PowerView

PowerM

PowerQ&A

PowerQuery

PowerPivot

The Future of Computing:

Azure

Hybrid Architecture:

Analytics Platform

Services (APS)

Old Reliable on Steroids:

Parallel Data Warehouse

(PDW)

Old Reliable: SQLServer

25

Price-Performance Numbers*

• EMC Greenplum

• IBM PureData

• Microsoft PDW

• Oracle Exadata

• Teradata Data Warehouse Appliance

26

* -- Thank you, Value Prism Consulting, Oct 2013



Hybrid Architecture:

Analytics Platform System

(APS)

29

Microsoft APS (Analytics Platform System)

A brilliant hybrid architecture

30

Polybase Bridges The Skills Gap

31

Thank you John Kreisa, Hortonworks

HDInsight = Hortonworks in Microsoft APS

32

Thank you, James Serra

Interactive Analytics Delivered To

Any Device



Azure: The Future of Computing

What is Azure?

One of the key missing concepts in this definition is that Azure is a

hybrid cloud, meaning you can bridge data and applications between

on-premise and the Azure cloud.

As of April 11, 2015, there are 3,019 applications in the Azure

Marketplace. These are overwhelmingly business-level apps, not

consumer apps as we are accustomed to in the Apple and Google app

stores.

Azure is Big and Mature

37

Huge Azure Infrastructure

100+ datacenters

One of the top 3 networks in the world (coverage, speed, connections)

2x AWS and 6x Google number of offered regionsOperational Announced

Central USIowa

West USCalifornia

North EuropeIreland

East USVirginia

East US 2Virginia

US GovVirginia

North Central US

Illinois

US GovIowa

South Central US

Texas

Brazil South

Sao Paulo

West EuropeNetherlands

China NorthBeijing

China SouthShanghai

Japan East

Saitama

Japan WestOsaka

India West

India East

East AsiaHong Kong

SE AsiaSingapore

Australia West

Melbourne

Australia East

Sydney

38

Azure Security, Privacy, Compliance

45

• ISO 27001/27002 Audit and Certification

• SOC 1/SSAE 16/ISAE 3402 and SOC 2

Attestations

• Cloud Security Alliance (CSA) Cloud Controls Matrix (CCM)

• Federal Risk and Authorization Management Program

(FedRAMP)

• Federal Information Security Management Act (FISMA)

• Federal Bureau of Investigation (FBI) Criminal Justice

Information Services (CJIS)

• Payment Card Industry (PCI) Data Security Standards

(DSS) Level 1

• United Kingdom G-Cloud OFFICIAL Accreditation

• Australian Government Information Security Registered

Assessors Program (IRAP)

• Multi-Tier Cloud Security Standard for Singapore

(MTCS SS 584:2013)

• HIPAA Business Associate Agreement (BAA)

• EU Model Clauses

• Food and Drug Administration 21 CFR Part 11

• Family Educational Rights and Privacy Act (FERPA)

• Federal Information Processing Standard (FIPS)

• Trusted Cloud Service Certification developed by China Cloud

Computing Promotion and Policy Forum (CCCPPF)

• Multi-Level Protection Scheme (MLPS)

The Visualization and Analysis Layer

Part desktop, part cloud, part mobile

46

Natural Language Queries

47

Closing Thoughts

• Business moves at the speed of software

• Older C-levels don’t generally grasp this… I’m old so I can say

that

• I don’t impress easily when it comes to IT vendors,

especially Microsoft

• History will show that the new Microsoft is one of the

biggest cultural and technological re-toolings of all time

• Their hybrid “data lake” analytics and big data vision and

execution are unmatched

• Azure is the future of computing

• It’s going to completely disrupt organizational IT strategies and

the role of the CIO, in a good way

48

microsoft: a waking giant in healthcare analytics and big data

Healthcare

health catalystwww

quality of care

healthcare analytics

lower cost of care

clevels care

speed of software

electronic healthcare

sanders d