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AMDIS/HIMSS Physician’s Executive IT SymposiumFebruary 19, 2017

Unlocking Value and Embracing Change

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AMDIS/HIMSS Planning CommitteeName Title Organization

Richard

Gibson Executive Director Health Record Banking Alliance

John Lee Chief Medical Information Officer Edward Hospital and Health Services

Howard Landa Chief Medical Information Officer Alameda Health System

Kevin McEnery

Director of Innovation in Imaging

Informatics and a Professor of

Radiology

University of Texas M.D. Anderson

Cancer Center

David

Danhauer Chief Medical Information Officer Owensboro Health

Milisa Rizer Chief Medical Information Officer

Ohio State University, Columbus, Ohio,

Academic Medical Center

Cait Cusack Physician Informaticist Insight Informatics

Lisa M Masson Medical Director Cedars-Sinai Health System

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Thank You To Our Conference Supporter

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Thank You to Our Conference Endorsers

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HIMSS17 Events of InterestEvent Date/time Location

Physician Community Reception Sunday, February 19th

4:30 -5:30 pm

Hyatt Regency

Ballroom R

AMDIS Meeting Monday, February 20th

10:00 am – 12:00 pm

Room W340B

AMDIS Endorsed Session

Medical Informatics and the C-suite:

Aligning Forces to Positively Affect

Patient Care

Tuesday, February 21st

8:30 – 9:30 am

Room W206A

CMIO Round Table Tuesday, February 21st

10:00 – 11:00 am

Room W303AC

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Other Upcoming Events

Event Date Website

AMDIS 26th Annual Physician

Computer Connection Symposium

June 20-23, 2017 www.amdis.org

HIMSS Physician Community Webinar:

The Future of Precision Medicine

March 8, 2017

12:00-1:00 pm CT

www.himss.org/phys

ician

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HIMSS17 CE Related Information

• Each session will receive 1 credit hour of the following types of credit:

– CME - ABPM LLSA

– CPHIMS - CAHIMS

– ACHE

• CE system is open now and will close 6 months after conference

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HIMSS17 CE Related Information• You can claim CE credit two

different ways:

– Mobile app – Credit available at end of each session by going to the session in the app

– HIMSS conference website

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CE Related Questions

Jan Lugibihl

Associate Manager, Professional Development

Direct: (312) 915-9234

Email: jlugibihl@himss.org

Jan will also be available at HIMSS Spot throughout conference

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A CHANGING HEALTHCARE LANDSCAPE

Dale Sanders

Executive Vice President, Product Development

Health Catalyst

Session ID: PHY1

February 19, 2017 — 08:15AM EST - 09:15AM EST

Hyatt Regency Orlando

Regency Ballroom R

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Speaker Introduction

Dale SandersExecutive Vice President, Product Development

Health Catalyst

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Data, data, data… for decision support

My Background

1983 2016

B.S. Chemistry,

biology minor

US Air Force

Command, Control,

Communication,

Computers &

Intelligence (C4l)

Officer

Reagan/Gorbachev

Summits

TRW/National Security Agency

• START Treaty

• Nuclear Non-proliferation

• Nuclear command & control

system threat protection

Nuclear Warfare Planning

and Execution-- NEACP &

Looking Glass

Intel Corp,

Enterprise Data

Warehouse

• Chief Data Guy

• Regional Director of Medical

Informatics, Intermountain

Healthcare

• CIO,

Northwestern

• Chief Data

Warehousing Guy

CIO, Cayman Islands

National Health System

Product

Development,

Health Catalyst

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”To err is human. To forgive is not SAC policy.”

SAC was my career upbringing… not quite the same as the IOM

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Nuclear & Clinical Decision Making

• Subjective, Objective, Assessment, and Plan

– Observe, Orient, Decide, Act

• Time critical, life critical, incomplete data

• Consequences of false positives and false negatives are significant

• Smart, independent, confident decision makers

• Lots of protocols and plans that only partially apply to specific situations

• Oddly very similar

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Conflict of Interest

Dale Sanders has no real or apparent conflicts of interest to report.

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Agenda

• Why should we care about integrating data?

What should we be trying to achieve?

– Population Health

– The Softer, Human Side of Being “Data Driven”

not “Driven By Data”

– The New Era of Decision Support “Uhh…you want

to put all that in

here?”

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Learning Objectives

• Discuss how the changing health information

technology landscape affects you and your organization

• Identify insights into lessons learned from a decade of

rapid change

• Describe key impacts to your practice and organization

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• Satisfaction = Outcome

Cost

• Factor 15X

– The right data, to the right person, at the

right time, in the right modality

• Secure but accessible data

– “Nobody ever died because we

shared their data”

• Empowering patients, not engaging

them

– Give patients the data they need

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Concepts and Philosophies: Why and What Are We Trying To Do?

• The Data Requirements of Population Health

• The Softer, Human Issues of Becoming Data Driven

• Software and Decision Support

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The Essence of Population Health

Getting paid more for the maintenance of health and the prevention of

disease than you get paid for the treatment of disease

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80% of Factors Affecting Health Outcomes Fall Outside Traditional Healthcare Delivery

We must risk

adjust for social

determinants of

health

We cannot hold

physicians

accountable for

these social

factors. It’s not

realistic.

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Population Health doesn’t trickle down;

it trickles up, one patient at a time.

Personalized care is the key to population health, not the other way

around.

I see a shift of attention towards population health, at the troubling

expense of personalized, patient centric care, including all-cause

harm. We need to be careful about chasing the brass ring.

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"I can make a health optimization recommendation for you, informed not only by the latest clinical trials, but also by local and regional data about patients like you; the real-world health outcomes over time of every patient like you; and the level of your interest and ability to engage in your own care. In turn, I can tell you within a specified range of confidence, which treatment or health management plan is best suited for a patient specifically like you and how much that will cost.”*

* Inspired by the Learning Health Community,

http://www.learninghealth.org/

As Healthcare IT Professionals, Enabling This Conversation Between Physician and Patient Should Be Our Common Goal

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Predictive and suggestive analytics in the same

user interface

The efficacy and costs of antibiotic protocols

for inpatients

The Antibiotic Assistant at Intermountain Healthcare: The First Triple Aim

Antibiotic

ProtocolDosage Route Interval

Predicted

Efficacy

Average

Cost/Patient

Option 1 500mg IV Q12 98% $7,256

Option 2 300mg IV Q24 96% $1,236

Option 3 40mg IV Q6 90% $1,759

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Complications declined 50%

Avg # doses declined from 19 to 5.3

The replicable and bigger story

Antibiotic cost per

treated patient: $123 to

$52

By simply displaying

the

cost to physicians

The Antibiotic Assistant Impact

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Physicians are 15x more likely to change their

ordering and treatment protocols if presented with

substantiating data at the point of care vs.

presented with the same data in a clinical process

improvement meeting.

Kawamoto et al, University of Utah, BMJ, 2005

“Conference room analytics”

vs.

Point of decision cognitive support

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‘Closing the Loops’ on Clinical Outcomes to Optimize QualityUsing Information Technology, Local Data and Analytics to Generate Evidence for Improvement

EDWClinical Quality Analytics

CQA targets population

prevalence & health

disparities

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CQA addresses cohort

needs & risks

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CQA supports

personalized health &

care

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Optim

ize

Capacity

Manage

Serv

ices

Deliv

er

Care

EHRClinical Decision Support

Executive & Clinical

Leadership

Optimize population health & care

system on quality & cost

Enterprise Clinical Teams

Tailor protocols for cohorts using

local data

Local Clinical, HER &

Analytic Teams

Personalize care using evidence-

based practice standards

CDS highlights population

health determinants

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CDS highlights cohort

characteristics

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CDS highlights individual

health status & care plans

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Other Data Sources

Information System Supporting Data Decisions & Actions

DATA & CLINICAL QUALITY GOVERNANCE

Set & monitor improvement priorities1 External Evidence

HER: Electronic Health Record

EDW: Enterprise Data WarehouseMTTI: Mean time to improvement

SOPA: Span of population affected

MTTI

LoHi

SOPA

©2015 A

uth

ors

: C

ori

nne E

ggert

, K

enneth

Moselle, D

enis

Pro

tti, D

ale

Sanders

Loop C:

Populations

Loop B:

Protocols

Loop A:

Patients

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Closed Loop Analytics & Decision Support

Mean Time To Improvement (MTTI)

Span of Population Affected (SPA)

Populations

• MTTI: Years, decades

• SPA: Millions, several

hundred thousand

• Analytic consumers:

Board of Directors,

executive leadership

team, Strategic plans

and policy

Protocols

• MTTI: Weeks, months

• SPA: Subsets of

patients – hundreds,

thousands

• Analytic consumers:

Care improvement

teams, clinical service

lines

Patients

• MTTI: Minutes, hours

• SPA: Individual

patients

• Analytic consumers:

Physicians and patients

at the point of care

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But…Population Health Economics Are Not There Yet

“93% of our revenue is still associated with

fee-for-service medicine.”--CFO, Midwest 13 hospital system

“Over 90% of our revenue comes from fee-for-service care.”--CEO, Northwest 5 hospital system

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Transition to Value Based Care

Proceed cautiously but quickly…

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Human Nature and The Softer Side of Data

• Data… Measures...Metrics...Facts are the most politically hot and

contentious thing you’ll ever deal with because they challenge the

perception of truth, from the highest levels of the organization to the

lowest

• How you deliver data… measures... metrics... and facts, in the

human context is more important than the technology, by far

The human relations of data are more important

than the data relations of data

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The Human Health

Data Ecosystem

And, by the way, we don’t

have much of any data on

healthy patients

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We Are Not “Big Data” in Healthcare, Yet

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Thank you for the graphs, PreSonus

Healthcare and patients

are continuous flow,

analog process and

beings

But, if we sample that

analog process enough,

we can approximately

recreate it with digital data

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We are treating physicians and nurses as if they were digital sampling devices.

“Every new click of the mouse you guys ask me to do, all in the name of data, sucks another piece of my soul away.” --Beleaguered primary care physician

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Troubling Factoid

• Of the 1,958 quality metrics in the National Quality Measures

Clearinghouse, only 7% of those measure clinical outcomes and less

than 2% of those are based on patient reported outcomes

• How do you train pattern recognition algorithms without patient

outcomes? What are you training them to recognize?

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N Engl J Med 2016; 374:504-506, February 11, 2016

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The softer side of the data journey

boils down to three simple steps…

that organizations, especially the

government, constantly miss

Find

The Truth

Tell

The Truth

Face

The Truth

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Find, Tell, and Face the Truth

• Finding the truth in data takes time, and you better

include lots of people with you on that journey. The

outcome better be more than your version of the truth.

• Telling the truth better be handled with diplomacy and a

human-centered perspective because the truth in data,

when never seen before, can be very disturbing.

• When you’ve found the truth and you are telling the

truth, you need to help people face the truth about

themselves and the organization, and they need to

perceive this truth as helping them and their purpose in

life.

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Don’t tell me what to do and don’t blast me with

invasive pop up alerts, overloaded with false positives

Give me a trustful, data-driven

suggestion and then let me make the

final decision

Physicians (and military officers) prefer Suggestive Analytics over Prescriptive and “Invasive” Analytics

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Software and data

are the greatest

agents of change in

the world today– it’s

not authors, poets,

political leaders, or

songs anymore,

unfortunately.

• Cerner?

• Epic?

• Apple?

• Google?

• IBM?

• Someone else?

All industries, including healthcare, move

at the speed and agility of software and

data, for better or worse.

Which vendor is in the best position to

have the most positive impact?

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We must build

software that

deliberately

borrows lessons

from the software

that has changed

human behavior.

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Facebook as an EHRFrom a blog I wrote in 2010 • Patient’s evolving health

story at the center of the

record, not the encounter

• Embedded video and

images

• Text and discrete data

• Secure messaging

• Social support from family &

friends

• Flexible security, defined by

the patient

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Amazon as a Clinical Order Entry System

• Drug and device availability

• Pricing

• Home delivery

• Automatic refills

• Patient reported outcomes

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90% of the screen space is driven dynamically,

by context, through analytics and algorithms

in the background that are nudging your

decisions through suggestions based on the

data from collective intelligence

It’s not predictive analytics… it’s ambient,

suggestive analytics

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This is not an HIE, CDR or EDW. It’s a little of both and better than each.

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In Summary• Most– 80%?-- of the data we need for Population Health lies outside the

walls of our traditional healthcare delivery systems.

• Pop Health has a long, complex ROI. Don’t forget about the $646B of waste

and harm in the current healthcare system.

• The drive to be data driven must enhance Mastery, Autonomy, and

Purpose, otherwise it will fail.

• The best decision support is suggestive and ambient. It fuses

transaction data and analytics into the same user experience.

• Populations, Protocols, Patients

– We must infuse data and decision support into each closed loop

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Questions

• Dale Sanders

• dale.sanders@healthcatalyst.com

• @drsanders

• Please complete your online session evaluation