Changing how we think about healthcare improvement

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thebmj 
BMJ
2018;361:k2014 | doi: 10.1136/bmj.k2014 1
QUALITY IMPROVEMENT
Changing how we think about healthcare
improvement
Complexity science oers ways to change our collective mindset about healthcare systems,
enabling us to improve performance that is otherwise stagnant, argues Jerey Braithwaite
KEY MESSAGES
•
The key measures of health system
performance have frozen for dec-
ades—60% of care is based on evi-
dence or guidelines; the system wastes
about 30% of all health expenditure;
and some 10% of patients experience
an adverse event
•
Proponents of change too often use
top down tools such as issuing more
policy, prescribing more regulation,
restructuring, and introducing more
stringent performance indicators
•
We must move instead towards a learn-
ing system that applies more nuanced
systems thinking and provides stronger
feedback loops to nudge systems
behaviour out of equilibrium, thereby
building momentum for change
•
Eective change will need to factor in
knowledge about the system’s com-
plexity rather than perpetuate the
current improvement paradigm, which
applies linear thinking in blunt ways
•
Yet we should recognise how truly
hard this is in the messy, real world
of complexcare
F
or all the talk about quality health-
care, systems performance has
frozen in time. Only 50-60% of
care has been delivered in line
with level 1 evidence or consensus
based guidelines for at least a decade and a
half1-5; around a third of medicine is waste,
with no measurable eects or justication
for the considerable expenditure
6-9
; and the
rate of adverse events across healthcare has
remained at about one in 10 patients for 25
years.
10-13
Dealing with this stagnation has
proved remarkably dicult—so how do we
tackle it in a new, eective way?
We need to understand why system-wide
progress has been so elusive and to identify
the kinds of initiatives that have made
positive contributions to date. Then we
can ask what new solutions are emerging
that may make a dierence in the future
and start to change our thinking about
healthcare systems.
Why change is hard
The overarching challenge lies in the nature
of health systems. Healthcare is a complex
adaptive system, meaning that the system’s
performance and behaviour changes over
time and cannot be completely understood
by simply knowing about the individual
components. No other system is more
complex: not banking, education, manu-
facturing, or the military. No other indus-
try or sector has the equivalent range and
breadth—such intricate funding models,
the multiple moving parts, the complicated
clients with diverse needs, and so many
options and interventions for any one per-
son’s needs. Patient presentation is uncer-
tain, and many clinical processes need to be
individualised to each patient. Healthcare
has numerous stakeholders, with dierent
roles and interests, and uneven regulations
that tightly control some matters and barely
touch others. The various combinations of
care, activities, events, interactions, and
outcomes are, for all intents and purposes,
innite.
When advocates for improvement seek
to implement change, health systems do
not react predictably; they respond in
dierent ways to the same inputs (sta,
funding, presenting patients, buildings,
and equipment). In the language of
complexity science, this is “non-linearity.”
The sheer number of variables and the
unpredictability of their interactions
make it hard to impose order. And health
systems are indeterministic—meaning
that the future cannot be predicted by
extrapolating from the past. They are also
fractal and self similar, often looking alike
in, for example, organisational culture in
different places and at different points
intime.
How then is a system as complex
and seemingly dynamic as healthcare
typically in a steady state, with entrenched
behaviours, cultures, and politics? Because
the total of the negotiations, trade-os, and
positioning of stakeholders pulls strongly
towards inertia.
14 15
No one person or group
is to blame; but a complex system clearly
does not change merely because someone
devises and then mandates a purpose
designed solution. Studies of concerted
improvement eorts, for example in North
Carolina, USA,16 and in the NHS,17 show
this. Instead, the system alters over time
and to its own rhythm (idiosyncratically
and locally).18
This raises further questions: what
circumstances can precipitate changes
in complex health systems, and what
circumstances frustrate progress? Box 1
summarises selected initiatives. Attractors
enable or create sucient change for the
system to be nudged before it settles into a
Box1: Selected attractors and repellents of change
Systems can change when:
• Stimulated by medical progress—eg, new diagnostic tests and treatments, imaging
technology, or surgical advances
• Incontrovertible evidence shows public benet—eg, immunising infants or reducing
smoking rates in developed countries
• New models of care emerge—eg, the shift to day only surgery or providing GP advice
remotely via apps, teleconferences, or telemedicine
• Clinical practices alter by necessity or because of professional acceptance—eg, lapa-
roscopic techniques
• Sources: Thimbleby, 201319; Farmanova et al, 201620; Westerlund et al, 201521; Watt
et al, 201722
Systems can reject change when:
• The primary or sole strategy is to mandate solutions from the top down
• The change is not supported by parties with power to resist or reject, such as the
medical profession or the media
• The initiative encounters entrenched bureaucracy, particularly in organisations such
as public hospitals
• More policies and procedures are issued on top of a multiplicity of existing policies
and procedures
• Attempts to alter deep seated politics or cultures are supercial
• Sources: Coiera, 201115; Braithwaite et al, 201723; Khalifa, 201324

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2018;361:k2014 | thebmj
QUALITY IMPROVEMENT
new state. Resisters or repellents hold the
status quo or reject change.
A key message from the examples in
box 1 is that change is accepted when
people are involved in the decisions and
activities that aect them, but they resist
when change is imposed by others. Policy
mandated change is never given the same
weight as clinically driven change.
Systems hardware and software
Much has been written about the many
eorts to initiate change in health systems
around the world, most of which seems to
presuppose two familiar pathways. One is
to alter the system’s “hardware” by restruc-
turing the organisation chart, upgrading the
infrastructure, or changing nancial models
or targets, for example (box 2). The NHS and
other systems have invested heavily in many
such eorts. But the gains have been mod-
est, and the extent to which such changes
have contributed to better patient care is
unclear. The other approach is to change
the “software” of the system by tackling the
culture of clinical settings (and the qual-
ity of leadership oered by managers and
policy makers) and using implementation
and improvement methods (box 3).
Changing our collective mindset
Instead of using the metaphor of hardware
and software, we could change our think-
ing. We need to recognise three problems.
Firstly, implementing and securing accept-
ance of new solutions is difficult, even
when armed with level 1 or other persua-
sive evidence—this is the take-up problem.
Secondly, disseminating knowledge of an
intervention’s benefits across the entire
system is hard—this is the diusion prob-
lem. Thirdly, even if a new model of care,
technology, or practice is successfully taken
up and widely spread, its shelf life will be
short—this is the sustainability problem.
The pace at which new ideas are being
generated, and previous ones discarded, is
accelerating, particularly so over the past
20 years.
So paradoxically, although nothing
lasts, genuine transformational
improvement remains frustratingly
elusive. Adding to the challenge, as
Contandriopoulos and colleagues remind
us, knowledge (even level 1 evidence) is
unevenly distributed, poorly understood,
and always contested.38
Accepting this reality is uncomfortable
for those promoting improvement. “Agents
of change” tend to prefer optimism or
even the delusion that their new policies
or initiatives are widely adopted.14 This
dichotomy has been described as “work-as-
imagined” by policy makers and managers
and as “work-as-done” by the clinicians at
the coalface.39 Policy makers and managers
try to instigate change remotely; clinicians
try to deliver care proximally. This leads to
much antagonism—or merely ignorance of
the other’s role.
Understanding emergence and resilience
How do we move forward? Whatever solu-
tions we choose must reect the complex-
ity of the system and respect its resilient
features.
40
We must change our approach
to understanding health systems and their
intricacies.41 42
One way is to break with the
NHS’s pattern of attempting systems
improvement from the top down.
Complex adaptive systems have multiple
interacting agents with degrees of
discretion to repel, ignore, modify, or
selectively adopt top down mandates.
Clinicians behave how they think they
should, learning from and influencing
each other, rather than by responding to
managers’ or policy makers’ admonitions.
Frontline clinicians in complex adaptive
systems accept new ideas based on their
own logic, not that of those in the upper
echelons. Healthcare is governed far
more by local organisational cultures
and politics than by what the secretary of
state for health or a remote policy maker
or manager wants.
Change, when it does occur, is always
emergent. This is when features of
the system, and behaviours, appear
unexpectedly, arising from the interactions
of smaller or simpler entities; thus, unique
team behaviours emerge from individuals
and their interactions.
Those on the frontline of care (clinicians,
sta, patients) navigate change through
their small part of the system, adjusting to
their local circumstances, and responding
to their own interests rather than to top
down instructions. Thus, healthcare is
naturally resilient, always buering itself
against change that does not make sense
to those who are on the ground, delivering
care.
Towards a nuanced appreciation of change?
Here are six principles on which a new
approach to change might be built. Firstly,
we must pay much more attention to how
care is delivered at the coalface. Bureau-
crats and managers, among others, will not
improve the system or make patients safer
by issuing swathes more policy, regulating
more avidly, introducing more clunky IT
systems, or striking o doctors.43
Box2: Initiatives to change the system’s hardware
•
Restructuring organisations
—The boxes on the NHS organisation chart have regularly
been redrawn to little benet. Although such reorganisations do produce structural
change, they do not greatly alter entrenched cultures, much less downstream clini-
cal outcomes.
25
Two studies assessing structural change showed that merging NHS
trusts26 and restructuring Australian hospitals27 produced no measurable gains and
put things back by 18 months or more.
•
Capital investments
—New buildings and new equipment or technology are necessary
changes that can contribute to better, more modernised models of caring. Technology
supporting new diagnoses and treatments, tests, and clinical techniques can instigate
important gains. These initiatives, however, are mostly left to research and develop-
ment departments, researchers, or clinicians, while politicians and managers focus
on organisational charts, opening new hospitals, and prescribing policy.
•
Financial models and targets
—Studies from the US Commonwealth Fund and inter
-
national experience indicate that no one nancial model is better than any other,28
29 and perverse outcomes and gaming often result from imposed targets and key
performance indicators.30
Box3: Initiatives to change the system’s software
•
Enhancing organisational and workplace culture
—A systematic review found a con-
sistent association in over 62 studies between organisational and workplace cultures
and patient outcomes across multiple settings.
31
Encouraging positive organisational
cultures to promote better patient outcomes seems time well spent. But these are
localised solutions.
•
Implementation science and improvement studies
—Studies have tested models for
creating implementable interventions and for getting more research evidence into
routine clinical practice.
32 33
Ideas have emerged—such as the PARiHS framework
34
and models that take a more system-wide view
32
— that identify important ingredients
in change such as context, persuasiveness of the evidence, and active facilitation. But
applying such models to systems has shown the limits of progress. For any interven-
tion, the eect size that can be secured when successful (and many interventions yield
no or little benet) is modest; perhaps around 16% on average.35-37

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QUALITY IMPROVEMENT
Secondly, all meaningful improvement
is local, centred on natural networks of
clinicians and patients.44 One size fits
all templates of change, represented by
standardisation and generic strategies,
too often fail. We must encourage ideas
from many sources; care processes and
outcomes will vary whatever we do.
Thirdly, we must acknowledge that
clinicians doing complex everyday work
get things right far more than they get them
wrong. We focus on the 10% of adverse
events while mostly overlooking the 90%
of care that has no harm.40 Understanding
errors is critical, as is seeking to stop
outmoded, wasteful, or excessive care. But,
if we also better appreciate how clinicians
handle dynamic situations throughout the
day, constantly adapting, and getting so
much right, we can begin to identify the
factors and conditions that underpin that
success.
This leads to a fourth, related, point. A
recent book45 looking at achievements in
healthcare delivery across 60 low, middle,
and high income countries showed us that
every system can tell multiple success
stories. These range from organ donation
and transplantation in Spain to early
warning systems for deteriorating patients
in Australia and Qatar, implementing
minimum required standards in
Afghanistan, making improvements in
information technology in Taiwan, and
embracing community based health
insurance in Rwanda. These apparently
disparate achievements have four common
factors: begin with small scale initiatives
and build up; convert data and information
into intelligence and give this openly to the
appropriate decision makers; remember
the lone hero model does not work and
that collaboration underpins all productive
change; and always start with the patient
at the centre of any reform measure.46 Such
inspiring ideas reect complexity thinking
and are not necessarily predicated on
reductionist, cause-eect logic.
Fifthly, we could simply be more humble
in our aspirations. Putting the myth of
inevitable progress aside, we should
recognise that big, at-scale interventions
sometimes have little or no effects and
that small initiatives can sometimes
yield unanticipated outcomes.
47
We must
admit to ourselves that we cannot know in
advance which will occur.
Sixthly, and most importantly, we
might adopt a new mental model that
appreciates the complexity of care systems
and understands that change is always
unpredictable, hard won, and takes time,
it is often tortuous, and always needs to
be tailored to the setting. Table 1 shows
20 ways to exploit these principles. These
enablers and insights need practice but
can be used by anyone, including patients.
For ease of application, they have been
separated into complexity approaches for
policy makers, managers and improvement
teams, and frontline clinicians.
Conclusion
We need to turn healthcare into a learn-
ing system, with participants attuned to
systems features and with strong feedback
loops to try to build momentum for change.
If we construct a shared outlook and draw
on new thinking paradigms, perhaps we
can move beyond today’s frozen systems
performance. A nal note of caution goes
to the proponents of today’s most popular
strategies: it’s time to stop thickening the
rule book, reorganising the boxes on the
organisation chart, and introducing more
key performance indicators—and to do
something more sophisticated.
Contributorship statement: JB is the sole contributor
and author.
Competing interests: I have read and understood
BMJ policy on declaration of interests and declare
there are no competing interests in association with
this manuscript.
Provenance and peer review: Commissioned;
externally peer reviewed.
This article is one of a series commissioned byThe
BMJbased on ideas generated by a joint editorial
group with members from the Health Foundation
andThe BMJ, including a patient/carer.The
Table1 | Twenty complexity oriented enablers and insights41 47-56
Enabler (what to do) Insight (why to do it)
For policy makers:
Take multiple evaluations of what’s going on Different stakeholders have distinguishable views on what’s happening in complex systems
Use system tools to uncover the system’s features Causal loop diagrams, social network analyses, role plays, and simulation can provide insights into a system’s
characteristics
Customise change to local contexts Culture is unique to the context: tailoring change to the circumstances is crucial
Work with, not against, trends Going against the currents of change is possible, but is fraught with frustration and risk—the trend is your friend
Balance standardisation and variety There is constant tension between the push for uniformity and the need for local initiatives
Use the informal system, not just the formal system Organisational chart thinking only gets people so far; use the informal system and its cultural and political attributes
Take every opportunity to bolster communication, trust, and
interpersonal relations
Care is delivered as a system of systems, with multiple interacting networks of people at its heart—communication, trust,
and relationships are key to any progress
For managers and improvement teams:
Model the system’s properties Systems diagrams and models, computer based or hand drawn, can illuminate the dynamics of the system
Use multimethod research and improvement techniques Randomised controlled trials or single method data gathering approaches rarely expose sufficient dimensions of complex
problems
Appreciate less is more in interventions Resist aiming to control the system through improvement strategies, projects, and change initiatives: spend more time
learning about the effects of interventions than obsessing about intricate designs
Leverage complexity thinking Immerse local teams in complexity science and systems thinking
Focus less on the individual and more on the system It’s much harder to change individuals—seek instead to nudge or perturb the system
Develop and apply feedback to people involved at every
opportunity
Change and improvement is a set of feedback loops, not an event or a linear process
Look for things going right as well as those going wrong This promotes a more balanced view of the system
For frontline clinicians:
Adopt a new problem solving focus based on systems thinking
rather than obsessing with finding “a” way forward
Search for interconnections rather than getting stuck on any one solution
Look for behavioural patterns in the system and listen to the
language people use
The rich behaviours and practices of others, and the signals and messages they convey, are full of beneficial cultural and
systems information
Beware excessively causal logic Take care in attributing cause and effect—overgeneralising causation is a common error
Trade-off between constant turmoil and implementing changes
before they are ready
All systems sit not far from the edge of chaos: ride the boundary, and remember the old lesson that much in clinical
practice and systems is uncertain
Understand that adaptation is almost always micro and granular Big picture transformational change is rare and is expressed differently in different settings when it does occur
Appreciate that humans have a social brain Organisational participants are perennially tuned in to the behavioural repertoires of others: use this expertise, and be
attentive to others’ needs and motivations

4 doi: 10.1136/bmj.k2014 |
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2018;361:k2014 | thebmj
QUALITY IMPROVEMENT
BMJretained full editorial control over external peer
review, editing, and publication. Open access fees
andThe BMJ’s quality improvement editor post are
funded by the Health Foundation.
This is an Open Access article distributed in
accordance with the terms of the Creative Commons
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to distribute, remix, adapt and build upon this work,
for commercial use, provided the original work is
properly cited. See: http://creativecommons.org/
licenses/by/4.0/.
Jerey Braithwaite, professor
1Macquarie University, Australian Institute of Health
Innovation, Level 6, 75 Talavera Road North Ryde,
NSW 2109, Australia
Correspondence to: J Braithwaite
jerey.braithwaite@mq.edu.au
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