Lies CMIOs Tell- Dr. David Allard, Henry Ford Health System

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EMR Innovation R. David Allard, MD Chief Medical Information Officer Henry Ford Health System mHealth Israel: January 10, 2018

Transcript of Lies CMIOs Tell- Dr. David Allard, Henry Ford Health System

EMR Innovation

R. David Allard, MD

Chief Medical Information Officer

Henry Ford Health System

mHealth Israel: January 10, 2018

Goals

Discuss Why EMRs lag other innovation in Healthcare and other industries

Propose an approach to innovation

Discuss common issues with which CMIOs contend

Give a few examples of why there is hope

Medicine is the Highest Tech Low Tech Industry

Care techniques are advancing at a phenomenal

rate

– Gamma knife

– Precision Medicine

– DaVinci Surgery

– Et Cetera

The mechanisms we use to learn about these best

practices and deliver the techniques have

changed very slowly with healthcare and medicine

still acting like a cottage industry

Cottage Industry vs. Standards Driven High Reliability

Industry

Cottage Industry

Emphasis on craftsmanship

– Great doctor/provider

– “Centers of Excellence”

Highly individualized care

Highly variable in cost and outcome

Optimized for the craftsman

Hard to measure or predict outcomes levels

Standards Driven Industry

Emphasis on process and reliability

– Create processes that anyone (within reason) can do

Consistency, highly reliable

Transparency

Optimized for the end recipient

– (consider the airline industry)

Medicine is the Highest Tech Low Tech Industry

13% of physicians in the USA are still on paper

charts in 20171

53% of practices describe their EMR as “basic”

83% of residential care communities are on paper

(2010)

32% of EHRs can exchange information with other

provider’s systems (2014)

1https://www.cdc.gov/nchs/fastats/electronic-medical-records.htm

Medical Education and Dissemination of Clinical

Innovation

Today

Medical Student learns from

Senior Providers…

Becomes a Resident who goes to

a new place to learn and get

experience…

Sets up a practice as a senior

provider and (sometimes) trains

new medical students

Ca. 650 AD

Apprentice Learns from a

Master…

Becomes a Journeyman who

travels to learn more and get

experience…

Sets up as new master, practices

and starts training new apprentices

Why is This a Problem?

CMIO Lie #1 – Medicine is becoming a retail business

Is medicine a service industry or a consulting industry at its heart?

– Service industry

• We certainly do things to and for people

• The literature is full of articles about the retailization of healthcare

– Consulting industry

• At the core of Medicine is knowing what to do

• Population Health, Big data, machine learning,

• The management of information such that it is represented of knowledge and applying that

knowledge to benefit the health of populations and individual patients

Bytes → Data → Information → Knowledge → Wisdom

Back to the antiquated system of sharing best

practices and innovation

Today

Medical Student learns from

Senior Providers…

Becomes a Resident who goes to

a new place to learn and get

experience…

Sets up a practice as a senior

provider and (sometimes) trains

new medical students

Ca. 650 AD

Apprentice Learns from a

Master…

Becomes a Journeyman who

travels to learn more and get

experience…

Sets up as new master, practices

and starts training new apprentices

If the Core of Medicine is Knowledge Management,

Why Has That Been So Slow to Change?

Technology capability

– Medical Records are more than a database –

• Innovation requires facilitated workflow – implies standardization, connectivity, data standards

• Information should feed Clinical Decision Support

Technology availability

– The cost of devices to all required users was prohibitive

Technology Utility

– Are there changes in outcomes to justify the cost (both IT cost and human cost?)

Why is EMR Innovation happening now?

Ca. 1800 New England Pre-Industrial

Revolution

Demand exceeds supply

Quality enormously variable given

the “cottage industry” state of the

operations

New technology enabling greater

supply and services with greater

standardization

US Statistics

In 2016, median age is 37.9 years

– 29.5 in 1960

In 2016, total USA population 326.4 M

– 179.3 M in 1960

In 2016, Healthcare spending per

capita $10,345

– $146 in 1960

In 2007, physicians/1000 2.4

– About 1 in 1960Source: www.census.gov

Why is EMR Innovation Happening Now?

Changes in the support for the hierarchy of information - EMR Utility catching

up to the needs of a knowledge based industry

– Better connectivity with sources of information – more availability of data

• Monitoring device integration with IT systems, wearables, HIEs

– Better codified standards to turn data into information

– Better reporting and data manipulation to turn information into better representations of

reality (knowledge)

• Better graphing and visualization tools

– More availability of digitized algorithms for clinical decision support – digital wisdom

• Guideline clearinghouses beginning to be codified

So Why do Doctors Hate EMRs?

So Why Do Doctors Hate EMRs

Data Entry is burdensome

New capabilities have created new work streams

– Regulatory requirements

– Expansion of the patient visit

– Incorporation of more data sources

Redistribution of work

Clerical perspective of the EMR

– Providers rarely view EMRs as a clinical competency – neither does almost anyone

else - but I would argue it is

Organization focus in change and innovation

CMIO Lie #2 – We want to be on the cutting edge of technology

Organizations must decide who they want to be from a digital standpoint (and

other innovations as well)

– Disruption

– Leading as a key differentiator

– Early adopter or follower

– Trailer

Is technology who we are or a tool we use?

Organization Focus in Change – More Ideas Than

Time

Device integration

Data Mining

New EMR modules

Machine learning

Enabling best practices (screening, risk scoring etc)

Supply chain management

Interaction with regulatory agencies

Decision support tools

Patient communication tools

Organization focus in change and innovation

Problems with leading innovation/disruption

– Higher risk

– Labor intensive, iterative work

– Opportunity cost

– May require a high focus in a small area of change

– May be difficult to scale

Most organization want to be leaders or adopters –

– Allows scalability

– Predictability of future state

Value of a change based on many domains

Domains of Value

Safety and Quality

User Experience

Patient Experience

System Stability

ROI

Information Portability

Regulatory Compliance

Domain relationships

Improving one area often leads to a

degradation in another

The Net Total Experience needs to

be positive

Projects need to coexist peacefully

and productively

Innovation in using tools vs. Innovation in making

more tools

New Tools

May be easier to control or

customize

Often more industry friendly since

each may represent separate

products

Creates new integration challenges

from data to scale to workflow

Using tools in new ways

More integrated but harder to build

Less highly customized to a specific

use case

Usually more cost controlled

Often more scalable

Desirable Projects

Highly defined scope

Specific goals related to organizational strategy

Fits into the landscape of the organization

Includes longer term support and planning

Approach to Innovation

Workflow integration

– Avoid creating separate parallel work streams

– Designs that are accessed when a clinician is already thinking about that patient

and – even better- that aspect of a patient

Recognize the skills of the team

– Have all members of a team operate to the top of their abilities

Data presentation

– Vetter visualization and digestion of data

– Data → information → knowledge → wisdom

Pay attention to User interfaces – simple and in line with other work

Pay attention to scalability -

Reasons for Hope

Video visits from providers to

smart phones

Real time delivery of patient

results – increasing patient

partnership in care

Wearable monitors populating

EMR data

Consolidating data from

multiple health systems

Big data use in

– Sepsis prediction

– Readmission risk

– Assessing social determinants of

care

Decision Support

– Better imaging test ordering

– Drug-drug and drug-disease

interaction