healthcare data: visual discovery and governance on any device

© 2015 Health Catalystwww.healthcatalyst.com

July 7, 2015

Hosted by Dale Sanders with guest speaker Donald Farmer of Qlik

Healthcare Data: Visual Discovery and Governance on Any Device

Creative Commons Copyright – Attribution Required

2

Agenda

• What do Qlik and Donald Farmer have to say about visual discovery and governance of data?

• The Three Truths of next generation healthcare analytics

• What’s my reaction to these truths?

• What’s the future for visual data discovery in healthcare look like?

© 2015 Health Catalystwww.healthcatalyst.comProprietary and Confidential

Proven global market leader with over 33,000 customers in over 105 countries.

Extensive partner network over 1,700 partners strong.

Active community leveraging the expertise of more than 100,000 members.

More than 1,000 healthcare organizations globally have chosen to deploy Qlik

Qlik data analytic solutions accelerate the adoption of data-driven analysis by fostering natural exploration and discovery of insights.

An introduction to Qlik


Visual analytics at the point of decision

Agility through Innovation

Collaboration and Conversation for better

decisions

Our Core BeliefsHarness the collective human intelligence of an organization for better decision-making

Deliver naturally intuitive solutions that bring the best out of the people that use them

Innovate to allow people to constantly adapt to changing environments

3 Truths Of Next Generation Healthcare Analytics

The “intelligent organization” derives value by

analyzing data from multiple sources,

and multiple views on multiple devices, at the point of

decision.



The power of discovery comes from new insights

that were not even contemplated in the original

design process.



Line of business users always need flexibility to add data, add

visualizations, and "what-if" to their hearts content.

This requires not only powerful self-service, but governed self-

service.


Qlik Products

Proven, market leading data

discovery platform

Used to develop and deploy rich

guided analytics applications

for exploration

and discovery

Next-generation self-service

data visualization application

Empowers everyone to

create and explore flexible,

interactive visualizations

using their intuition

Associative Data Indexing Engine enables users to naturally follow their intuition and explore data relationships across many sources that

would be hidden in hierarchical or query-based approaches.

© 2015 Health Catalystwww.healthcatalyst.comCreative Commons Copyright – Attribution Required

Truth #1: The “intelligent organization” derives value by analyzing data from multiple sources, and multiple views on multiple devices, at the point of decision.

10

Imagine if your physician could say this to you…

I can make a health optimization recommendation to you, informed not only by the latest clinical trials, but also by our local and regional data about patients like you; the real-world health outcomes over time of every patient like you who has had your illness; and the level of your interest and ability to engage in your own care -- and in turn I can tell you within a specified range of confidence, which treatment has the greatest chance of success for a patient specifically like you and how much that treatment will cost.

This is Health Catalyst’s aspirational statement for our products and services, inspired by the Learning Healthcare System

Consortium, but modified by Dale Sanders

11

Healthcare data ecosystem

Health Catalyst has a library of connectors to 94 different source systems of data in healthcare

On average, mid-to-large size health systems can expect to integrate over 150 different sources of data in their enterprise data warehouse, within five years.

Complex, very diverse, and expanding

12

Data at the point of decision making

• Physicians are 15x more likely to change their protocol for a patient if presented with substantiating data at the point of care vs. presented with the same data in a clinical quality meeting (BMJ, 2007)

• 50M smart meters installed in the US– no change in consumer consumption of electricity

• Where’s the decision support data at the light switch and thermostat– i.e., the point of decision making?

• Lifestyle determines 70-80% of healthcare outcomes

• Where’s the decision support data at the restaurant, grocery store, and other lifestyle decision points?


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

Closed Loop Analytics

Information Systems

Supporting Data

Decisions & 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

Opti

miz

e Ca

paci

tyD

eliv

er C

are

Loop A: Patients

Loop B: Protocols

External Evidence Literature, 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 DraftMar 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 AnalyticsMean Time To Improvement (MTTI) and Span of Population Affected (SPA)

Loop C: Populations

● MTTI: Years, decades

● SPA: Millions, several hundred thousand

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

plans and policy

‒ Heat maps, bar charts, histograms, network diagrams, etc.

Loop B: Protocols

● MTTI: Weeks, months

● SPA: Subsets of patients– hundreds, thousands

● Analytic consumers: Care improvement teams, clinical service lines

‒ Control charts, line charts, scatter plots, etc.

Loop A: Patients

● MTTI: Seconds, minutes, hours

● SPA: Individual patients

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

‒ Algorithms with minimal, precise data

14

15

Simple visualization, complex algorithms

• Institute for Clinical Systems Improvement• Reduce overuse of inappropriate imaging in Minnesota• $84M in savings

16

Ambient, suggestive analytics

Substitutable Medical Apps and Reusable Technology (SMART)This is going to enable closed loop analytics with EHRs

Two major goals:• A user interface that allows “iPhone-like” substitutability for medical apps• A set of services that enables efficient data capture, storage, and effective data retrieval and analytics, which will be scalable to the national level but nonetheless respectful of institutional autonomy and patient privacy.


Truth #2:The power of discovery comes from new insights that were not even contemplated in the original design process.

19

Design forward, not backward

Sanders’ Third Postulate

● In the design of analytic and big data systems, aka, enterprise data warehouses, the thickness of a requirements document is inversely proportional to the success of the system

When was the last time we asked a librarian to write a requirements document that encompassed all the possible use cases of the books and periodicals in a library?

● You stock the shelves with as many books on as many topics as you can afford and store, then evolve from there

Current data emphasis in healthcare… • Healthcare providers are scrambling to meet current, basic

reporting requirements

• To payers, federal and state government, industry accreditation, professional affiliations… US News and World Report Hospital Ratings

• Vendors are designing data warehouses and data models to meet current needs… the known requirements

• Need a balanced design and strategy… address the known and prepare capacity for the unknown

• Allow for local data exploration… hire staff and vendors that allow for this!

20

Surprises of data

“When we designed Navstar [aka, GPS], we were mainly concerned about a precise understanding of current location– where are we now? What we didn’t expect is how it has been used to explore the unknown with greater confidence and safety.”

--Roger L. Easton, principal inventor and designer of the Global Positioning System (GPS)

21


Truth #3:Line of business users always need flexibility to add data, add visualizations, and "what-if" to their hearts content. This requires not only powerful self-service, but governed self-service.

23

As in a democratic, free society

Personal freedom does not usurp personal responsibility to the common good

Same applies to the future of data governance

As we release and decentralize more and more data and we all become participants in the healthcare decision making process, we must become data governors, and encourage a culture of data literacy, data quality, and data utilization

24

Surprise… you are now data governors

“Data governance refers to the plans, processes and

principles that are proactively applied to ensure that

an organization’s data is managed in such a way to

maximize the value of that data to the mission of the

organization.”

• The Triple Aim of Data Governance

• Data Quality

• Data Literacy

• Data Utilization


What’s the Future Look Like for Healthcare Data Discovery and Visualization?

26

What makes for good visualization of data?

• Hanspeter Pfister, Wang Professor of Computer Science, Harvard; Michelle Borkin and Caroline Perry, Harvard School of Engineering and Applied Sciences

• 2013 IEEE Information Visualization Conference

“A visualization will be instantly and overwhelmingly more memorable if it

incorporates an image of a human-recognizable object—if it includes a

photograph, people, cartoons, logos—any component that is not just an abstract

data visualization,” says Pfister. “We learned that any time you have a graphic

with one of those components, that’s the most dominant thing that affects the

memorability.”

“You’d think the types of charts you’d remember best are the ones you learned in

school—the bar charts, pie charts, scatter plots, and so on,” Borkin says. “But it

was the opposite.” Unusual types of charts, like tree diagrams, network diagrams,

and grid matrices, were actually more memorable.

27

What’s this mean for the future?Appeal to the foundations of human cognition• Infographics for your data

• The Advisory Board Company is a great role model for merging memorable graphics with meaningful data

• What would your corporate dashboard look like as an infographic?

• “Unusual” diagrams and charts

• Next slides

28Thank you, Harshit Pandey, at YeCode

29

Word trees & text data*This word tree depicts a tree of phrases, with the size of the words proportional to their usage. In this set of phrases, "cats eat mice" occurs four times, and "cats eat" occurs six times (four times with "mice", and twice with "kibble").

*Thank you, Google Charts

30

OECD quality of care indicators vs. Canadian province

Thank you, Canadian Institute for Health Information

31Thank you, Washington Post

Incredibly cool project

32

Examples on following side

Patient & clinician friendly dataA sampling of 54 chart types, research-validated as effective

33

For communicating likelihood of side effects, severity of side effects, changes in side effect risks over time, based on treatment protocols

37

Becoming a “mobile first” worldSmartphones are becoming the first choice for computing

38

Device choice depends on time

You have to support multiple tools and platforms

39

Additional, good references• Health Intelligence

• http://healthintelligence.drupalgardens.com/content/resources/data-and-visualization-sites

• Jenn Underwood

• http://www.jenunderwood.com/

• “Worth a Thousand Words: How to Display Healthcare Data”, California Healthcare Foundation

• http://www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/W/PDF%20WorthThousandWordsDataViz.pdf

• Visually

• http://visual.ly/

• Chart Porn

• http://chartporn.org/

• Google Charts

• https://developers.google.com/chart/interactive/docs/

• Data Wrapper

• https://datawrapper.de/

40

Healthcare Analytics Summit 15

Here’s a sneak preview …Industry-leading Speakers

Jim CollinsBest-selling author of Good to Great, Great by Choice, Built to Last, and How the Mighty Fall

Ed CatmullCo-founder of PixarPresident of Pixar and Walt Disney Animation Studios

Daryl MoreyHouston RocketsGeneral Manager and Managing Director of Basketball Operations

Amir RubinStanford Health CarePresident and CEO

Timothy G. Ferris, MD, MPHPartners HealthCareSenior Vice President ofPopulation Health Management

Timothy Sielaff, MD, PhD, FACSAllina HealthChief Medical Officer

Summit highlights3-day AgendaWe’ve increased the time of this year’s summit to allow for more sessions, topics, and networking.

CME Accreditation for CliniciansThis activity has been approved for AMA PRA Category 1 Credits™.

More Case Study SessionsHealth system case studies addressing even more clinical, technical, operational, and financial examples.

Hands-On Experiences Examples, vignettes, and audience-based activities demonstrate principles in fun and memorable ways.

Analytics-Driven EngagementReal-time polling, networking, Q&A, and gamification experiences; plus,i-beacon location technology.

NetworkingExperience networking options that use analytics creatively to help you find and connect with others.

Pre-Summit Classes and TrainingAn early half-day of pre-session classes and training options specifically for Health Catalyst clients.

3X the sessions8 keynotes, 25 breakouts, 25-40 analytics walkabout mini-sessionsf

Early Registration Pricing, Optimized For Teams

Buy 1(save $300)

$395/Pass(through May 31)

Buy 3(save $1,098)


Buy 5(save $2,000)


Thank you!Please complete your satisfaction

survey for this webinar.

healthcare data: visual discovery and governance on any device

Healthcare

visual data discovery

health catalyst

governance of data

adoption of data

data relationships

visual discovery

confidential visual

data visualization application