Current Topics inQuality Management

Topics

• 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• 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 QualityManagement

• 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

Line Workers

Organization

Strategic

(Future-oriented)

Daily Management

(Improving jobprocesses)

Level

Time Spent

Daily Work

(Doing your job)

Integration of QualityManagement

• 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

# Encounters / Death

Tot

al #

of

Dea

ths

Health Care

Bungee Jumping

1K10 100 10K 100K1 1M 10M

100K

1K

10

100

10K

1

Mtn Climbing

Driving

ChemicalManufacturing

CharteredFlights

NuclearPower

EuropeanRailroads

ScheduledAirlines

Dangerous Safe

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 self-interest 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 ComparisonCollective 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

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.

• “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

• 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

Examples

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 alla small shifta 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

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Level of AgreementDecisions will havevarying levels at which the entire system agree that a particular effect is desired.

The Degree of Certainty

Decisions are more certainwhen the cause and effectlinkages are well known.

Stacey Diagram

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Simple

Simple:

Straightforward

decision-making.

Rational

planning & control

Stacey Diagram

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Simple

Chaos:

Disintegration

patterns.

No discernible

& anarchy

Chaos

Stacey Diagram

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Simple

Complicated:

Negotiation &

Political

Compromise

Chaos

Complicateddecision-making.

Stacey Diagram

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Simple

Complicated:

Mission & Vision

Judgmental

based planning

Chaos

Complicateddecision-making.

Complicated

Stacey Diagram

Close toAgreement

Far fromAgreement

Close to certainty Far from certainty

Zone

of

Complexity

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

Human (adaptive)

Systems

Simple/Complex

Examples

Fan & thermostat/

757 Aircraft

Transcribe order/

Hospital

Predictability High Low

Surprising

BehaviorSmall 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.)