Transcript Recent Developments in Quality Management

Current Topics in
Quality Management
Topics
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What is “Quality”?
Accountability for Quality
Integration of Quality Management
Patient Safety
Professional Cultures
Complexity Theory
Evidence-Based Medicine
Implications for PI
What is Quality?
(in Healthcare)
What is Quality?
• Pornography definition
• IOM definition
• Functional components
IOM Definition
• Quality of care is the degree to which
health services for individuals and
populations increase the likelihood of
desired health outcomes and are
consistent with current professional
knowledge.
Functional Components
• Donabedian described 3 components:
– Structure
– Process
– Outcome
• Structure + Process = Outcome
Accountability for Quality
• The US healthcare system has always been
presumed to provide quality healthcare.
• Two general trends have challenged this
presumption:
– increasing evidence in the literature that
quality is not being provided,
– accelerated costs of healthcare.
• Increasing demands by the purchasers of
healthcare that quality be demonstrated.
Principle Systems
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HEDIS - outpatient managed care providers
Indicator Measurement System - JCAHO
ORYX – JCAHO
Sixth Scope of Work – Medicare
OASIS – home health providers
FMEA – failure mode and effect analysis –
engineering
• RCA – root cause analysis – performance
improvement
Integration of Quality
Management
• Managing quality is not something that is
done in addition to your other management
responsibilities.
• Managing quality is a critical part of all
your management responsibilities.
• If not consciously managing quality, then
your are neglecting these management
responsibilities.
Quality Activities
Top Management
Organization
Strategic
(Future-oriented)
Daily Management
(Improving job
processes)
Level
Daily Work
(Doing your job)
Line Workers
Time Spent
Integration of Quality
Management
• The three levels of quality activities
correspond to the quality trilogy of Juran:
– Quality planning – developing new
processes
– Quality Improvement – improving
current processes
– Quality Control – maintaining current
performance levels
Hoshin Planning
• There are multiple levels of planning within
an organization.
– Strategic planning, annual planning,
budgetary planning, QI priorities
• Aligning the various levels will yield the
greatest impact.
• Derived from “Hoshin Kanri” meaning the
point of the needle/compass.
• A process to maintain alignment.
Patient Safety
Patient Safety
• Patient safety has become an item on the
national agenda since the IOM report “To
Err is Human,” was released in 1999.
• The entire discussion has become
politicized.
• The Leapfrog Group has successfully
pushed the envelope, primarily because of
the financial weight represented.
How Hazardous is Health Care
Dangerous
Total # of Deaths
100K
Safe
Health Care
Driving
10K
1K
Scheduled
Airlines
100
Mtn Climbing
10
Chemical
Manufacturing
Bungee Jumping
1
1
10
100
Chartered
Flights
1K
10K
# Encounters / Death
100K
European
Railroads
Nuclear
Power
1M
10M
Patient Safety
• Improving patient safety requires a culture
and paradigm shift, organizational
commitment, resource allocation, as well as
system re-design.
• Measurement systems need to be developed
to reflect and monitor performance.
• How many persons died last year at your
organization related to a medical error?
• How many people know this number?
Professional Cultures
Physicians
Physicians must believe that everything
they do is as perfect as it can be.
Physicians
If what I am currently doing is perfect,
and you want me to change,
what are you asking me to do?
Expert Culture
• Healthcare providers exist in an
environment of personal
accountability.
• When aggregated into a community,
they naturally form an expert culture.
• Also common in engineering firms,
architectural firms, and multispecialty
law firms.
Expert Cultures
• Motivated primarily by self-interest,
accomplishment and power.
• Individuals are very competitive.
• Success and positive feedback is
primarily determined by individual
performance.
• Achievement in this social context
results in the development of an expert.
Expert Cultures
• For developing physicians, success
occurs by out-performing the
competition.
• There is no point where success results
from teamwork, consensus building,
interdependency, or sacrificing selfinterest for the greater good.
• Metaphors - "herding cats"
• Teamwork as a golf team
Collective Cultures
• Composed of persons who affiliate
together, generally around a common
mission, vision and values.
• Motivated by common interests, as
defined by the mission, etc.
• Success generally results from
collaboration, teamwork, and
interdependency.
Collective Cultures
• Collective cultures, at their best, have a
strong sense of trust and loyalty.
• When management literature is
speaking of culture, particularly in the
context of managing change, it is
generally referring to a collective
culture, but frequently does not work
when applied to expert cultures.
Culture Comparison
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Collective Culture
Thin skinned
Very sensitive to injury
Long memory for injury
Risk averse
Process versus outcome
Change causes "FUD"
High need for recognition
Conflict resolution motif:
– denial
– passive aggression
– explosion
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Expert Culture
Thick skinned
High risk
Must win or L-L
Insensitive to collectives
Fast "clear" decisions
Results versus process
Self-interest first
Like to lead
Conflict resolution motif:
– direct confrontation
Culture Comparison
• Neither culture is right or wrong.
• The distinctions are not absolute.
• The behaviors are intrinsic to the
nature of the respective individuals.
• Wishing they were different is useless.
• It is the responsibility of leaders to
recognize the differences, foster
alignment, and create an environment
that fosters change.
Changing Behavior
• ALL behavior is in support of the
perceived personal hierarchy of values
and needs, as guided by underlying
beliefs and attitudes.
• If you want people to change behavior,
you must change the underlying beliefs.
• Reward or punishment may generate
compliance, but it is superficial, and
behavior reverts when intervention stops
Changing Behavior
• Successful people have the hardest time
changing behavior (learning).
• To learn, you must be vulnerable. (Admit
that your knowledge is either incomplete or
inaccurate.)
• This is incompatible with the facade of
perfection.
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“Teaching Smart People how to Learn”
–Chris Argyris
Experts (Doctors) and Data
• If confronted with data that challenges
the facade of perfection –
– discredit the data
– justify the data
– "Shoot the messenger"
• Once data is accepted – fault lies with others
Changing Behavior
• Three levels of resistance to change – don't understand - logical; lack of info
– emotional - fear
– prejudicial - bias
• If resistance is based on fear, you can't
overcome resistance with more data/info
• Understanding the level of resistance is
necessary to generate a proper response
Complexity
• An emerging science which analyzes
organizations from multiple
dimensions, including biological
models, rather than simple machinistic
perspectives.
• Organizations act as complex adaptive
systems and are therefore less
predictable.
Complex Adaptive Systems
(CAS)
• A collection of individuals, each of
whom is autonomous and free to act in
unpredictable ways, and whose actions
are interconnected such that one's
actions change the context of the other
individuals.
Complex Adaptive System
Complex Adaptive Systems
Examples
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a flock of birds, swarming
a school of fish, avoiding a predator
a herd of buffalo, stampeding
the Internet
the weather
the economy
an ecosystem
improvisational jazz
CAS Attributes
• Each element can change themselves;
– i.e., they can adapt.
• Systems are embedded within systems &
their interdependency matters.
• System is nonlinear –
– small events may trigger huge effects.
– large events can have negligible effects.
• Complex behavior can emerge from a few
simple rules.
CAS Attributes
• Not predictable in detail; forecasting is an
inexact, yet boundable, art.
• Future is not just unknown, but
unknowable.
• System co-evolves through constant tension
and balance.
• Emergence of novelty and creativity is a
natural state.
• Order can emerge without central control.
• CAS are history-dependent.
Adaptive Components
• Individual components can change/adapt.
– change will occur in response to
alterations in the environment.
– behavioral adaptation is rapid-cycle
learning from local experience.
– dependent upon the presence of diversity.
– necessary to maintain existence in an
unpredictable environment.
– allows the entire system to adapt.
Complex Imbedded Systems
• Systems are imbedded within systems.
– complex systems are parts of larger
complex systems, and are made up of
smaller complex systems.
– no component, including leaders, can act
as though they are outside the system
– it is not the specific individuals that are
the most critical, but the relationships
between individuals
Non-linearity
• System is nonlinear
– effects of small or large changes are not
necessarily related to size or predictable
– large interventions may not achieve
desired outcomes and may yield nothing
or possibly the opposite outcome.
– there is a sensitive dependence to initial
conditions - the "butterfly effect."
Non-linear Systems
• Inverse power law: the frequency of
occurrence of a phenomenon is in inverse
relation to its size.
– small waves are common, large waves
are less frequent
• Common generative mechanisms
– both small and large waves are causes by
the same generative mechanisms
Non-linear Systems
• Self-organized criticality - there is
interdependence of agents in the system which
creates tension over time
– Per Bak's classic sand pile experiment
– As grains of sand self-organize into a pile, a
single new grain of sand can cause:
 nothing at all
 a small shift
 a large landslide
Non-linear Systems
• Confluence and concatenation
– BIG events, such as severe errors, often
occur as a result of a concatenation of
triggering events.
– these triggering events are the generative
mechanisms leading to small events.
– BIG events are impossible to predict
– analyzing the cause of small events is the
first step in preventing big events.
Non-linear Systems
• Tension and criticality – When there is sufficient tension and
criticality in the system, a seemingly
trivial event can serve as a powder keg
– e.g., Rosa Parks' refusal to yield her seat
– The same event, in the absence of
preexistent criticality would have no
significant impact on the system
Simple Rules
• Complex behavior emerge from a few
simple rules:
– complex plans are not needed, and may
be detrimental
– simple rules are frequently unspoken, yet
self-perpetuating within the system
– simple rules can be clarified by searching
for subtle patterns and asking "Why?"
five times.
Computer Simulation
• Create "Boids" - autonomous agents
• Define simple rules:
– Try to maintain a minimum distance from
all other boids & objects.
– Try to match speeds with neighboring
boids.
– Try to move towards the center of mass
of the boids in your neighborhood.
• Boids will flock, though not told to do so.
Limited Predictability
• Not predictable in detail; forecasting is an
inexact, yet boundable, art.
• Future is unknown and unknowable
– need to analyze - trying to identify
recurring patterns, the underlying simple
rules and attractors
– forecasting tries to foretell how these
patterns will yield outcomes into
perceived future environment/conditions
Tension & Paradox
• System co-evolves through constant tension
and paradox
– in CAS, tension and paradox are natural.
– both sides of apparent contradictions are
true and necessary.
– we may not need to resolve all the
dilemmas of organizations.
– resolution of these dilemmas may be
detrimental to long-term survival.
Emergence
• Novelty and creativity naturally emerge
– CAS exist and thrive at the edge of chaos
– in an environment of uncertainty and
rapid change, novelty and creativity are
necessary for survival
– CAS have the adaptability which allow
for the emergence of novelty and
creativity.
– Dependent upon the presence of diversity
– Standardization smothers creativity.
Emergence
• Order can emerge without central control
– CAS achieve order by reaching
equilibrium, not stability.
– attempts to impose central control can
have undesirable consequences.
– equilibrium is determined by the simple
rules & attractors and the environment.
– changing the rules, attractors or
environment may yield a new
equilibrium.
Dependent on History
• CAS are history dependent
– CAS are shaped and influenced by where
they have been.
– what has worked in one organization may
not work in another organization.
– understanding the organization's history
is key to understanding its current
position as well as the system's rules &
attractors.
Stacey Diagram
Far from
Agreement
Level of Agreement
Decisions will have
varying levels at
which the entire
system agree that a
particular effect is
desired.
The Degree of Certainty
Decisions are more certain
when the cause and effect
linkages are well known.
Close to
Agreement
Close to certainty
Far from certainty
Stacey Diagram
Far from
Agreement
Simple:
Rational
decision-making.
Straightforward
planning & control
Close to
Agreement
Simple
Close to certainty
Far from certainty
Stacey Diagram
Far from
Agreement
Close to
Agreement
Chaos
Simple
Close to certainty
Far from certainty
Chaos:
No discernible
patterns.
Disintegration
& anarchy
Stacey Diagram
Far from
Agreement
Chaos
Complicated
Close to
Agreement
Simple
Close to certainty
Far from certainty
Complicated:
Political
decision-making.
Negotiation &
Compromise
Stacey Diagram
Far from
Agreement
Chaos
Complicated
Close to
Agreement
Simple
Complicated
Close to certainty
Far from certainty
Complicated:
Judgmental
decision-making.
Mission & Vision
based planning
Stacey Diagram
Far from
Agreement
Zone
of
Complexity
Close to
Agreement
Close to certainty
Far from certainty
Stacey Diagram
• Many organizations are existing in all areas
of the matrix at different times.
• Traditional management methods are
effective in the Simple area.
• Management methods needs to be altered in
the Complicated areas – negotiation / compromise
– mission & values based
Stacey Diagram
• If you try to use traditional management
methods (plan & control) in the Zone of
Complexity, you usually get unintended and
unpredictable consequences.
• Complex Adaptive Systems can exist and
thrive in the Zone of Complexity.
• PDCA cycle is an example of management
in the complex zone allowing tuning,
experimenting, and good-enough planning.
Different Systems
Mechanical
Systems
Simple/Complex Fan & thermostat/
Examples
757 Aircraft
Human (adaptive)
Systems
Transcribe order/
Hospital
Predictability
High
Low
Surprising
Behavior
Small probability
A real possibility
System Design
• The distinction between these systems is
obvious, but frequently not taken into
account when system is designed.
• When the human components respond in an
unpredictable manner, they are labeled as
being unreasonable or “resistant to change.”
• The designer then specifies behavior in
greater detail via rules, guidelines, etc.
Evidence-Based Medicine
• Evidence-Based Medicine is the application
of the strongest clinical information along
with patient preferences and values to guide
clinical decision-making.
• It allows for recognition that the evidence is
relatively weak, and that decision-making
may need to be guided by experience.
Evidence-Based Medicine
• Many decisions relating to health care are
not supported by strong clinical evidence.
• Strong clinical evidence would place the
specific issue in the Simple area.
• Customized Standardization and “Plan &
Control” would be reasonable when the
clinical evidence is strong.
• Unexplained variation may be related to
being in the Zone of Complexity.
Implications for PI
• Standardization and reduction of variation is
not always desirable.
• Negative outcomes generally have multiple
generative events.
• Improve by exploring the cause and
lowering the frequency of small events.
• Don't assume that a "best practice export"
will work in your unique context.
Implications for PI
• Simple rules (as proposed by IOM) are not
simply imposed upon the system by its
leaders.
• The current simple rules needs to be defined
at various levels in the system.
• Ask “Why?” five times.
• (Note similarity to Root Cause Analysis.)