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Received: 1 June 2017 Revised: 2 November 2017 Accepted: 3 November 2017
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How the dual process model of human cognition can inform efforts to de‐implement ineffective and harmful clinical practices: A preliminary model of unlearning and substitution
Christian D. Helfrich MPH, PhD, Core Investigator, Research Associate Professor1,2 |
Adam J. Rose MD, MSc, Associate Professor3 |
Christine W. Hartmann PhD, Supervisory Research Health Scientist, Research Associate
Professor4,5 | Leti van Bodegom‐Vos PhD, Assistant Professor6 |
Ian D. Graham PhD, Professor, Senior Scientist7,8 |
Suzanne J. Wood PhD, Assistant Professor9 |
Barbara R. Majerczyk MPH, Program Manager1 |
Chester B. Good MD, MPH, Core Investigator, Chair, Professor of Medicine and
Pharmacy10,11,12 | Leonard M. Pogach MD, MBA, National Director of Medicine13,14 |
Sherry L. Ball PhD, Research Health Scientist15 |
David H. Au MD, MS, Director, Professor of Medicine and Pharmacy1,16 |
David C. Aron MD, MS, Professor of Medicine and Epidemiology and Biostatistics17,18
itions (https://onlinelibrary.w iley.com
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1VA Puget Sound Health Care System, Department of Veterans Affairs, Seattle, USA
2Department of Health Services, University of Washington School of Public Health, Seattle, USA
3Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, USA
4Center for Healthcare Organization and Implementation Research (CHOIR) Bedford VA Medical Center, Bedford, USA
5Boston University School of Public Health, Boston, USA
6Department of Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands
7School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
8Centre for Practice‐Changing Research, Ottawa Hospital Research Institute, Ottawa, Canada
9Graduate Program in Health Services Administration, Department of Health Sciences, School of Public Health, University of Washington, Seattle, USA
10Center for Health Equity Research and Promotion, VA Pittsburgh healthcare System, Department of Veterans Affairs, Pittsburgh, USA
11Medical Advisory Panel for Pharmacy Benefits Management, Department of Veterans Affairs, Washington, USA
12University of Pittsburgh School of Medicine, Pittsburgh, USA
13Office of Specialty Care, Veterans Health Administration, Washington, USA
14VA New Jersey Health Care System, East Orange, USA
15Louis Stokes Cleveland VA Medical Center, Department of Veterans Affairs, Cleveland, USA
16Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Washington, Seattle, USA
17Department of Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, USA
18Division of Clinical and Molecular Endocrinology and Adjunct Professor Dept. of Organizational Behavior, Weatherhead School of Management, Case Western
Reserve University, Cleveland, USA
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© 2018 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons, Ltd.
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Correspondence
Christian D. Helfrich, Seattle‐Denver Center of
Innovation for Veteran‐Centered and Value‐ Driven Care, US Department of Veterans
Affairs, 1660 Columbian Way, S‐152, Seattle, Washington 98108, USA.
Email: [email protected]
Funding information
United States Department of Veterans Affairs,
Grant/Award Number: QUE 15‐271; The Canadian Institutes of Health Research funded
Integrated Knowledge Translation Research
Network (FDN# 143237) provided partial
funding for the article's open access costs
List of abbreviations: COPD, chronic obstructiv
Describe, Investigate, Create, Evaluate.
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Abstract
Rationale and objectives: One way to understand medical overuse at the clinician level is in
terms of clinical decision‐making processes that are normally adaptive but become maladaptive.
In psychology, dual process models of cognition propose 2 decision‐making processes. Reflective
cognition is a conscious process of evaluating options based on some combination of utility, risk,
capabilities, and/or social influences. Automatic cognition is a largely unconscious process occur-
ring in response to environmental or emotive cues based on previously learned, ingrained heuris-
tics. De‐implementation strategies directed at clinicians may be conceptualized as corresponding
to cognition: (1) a process of unlearning based on reflective cognition and (2) a process of substi-
tution based on automatic cognition.
Results: We define unlearning as a process in which clinicians consciously change their knowl-
edge, beliefs, and intentions about an ineffective practice and alter their behaviour accordingly.
Unlearning has been described as “the questioning of established knowledge, habits, beliefs
and assumptions as a prerequisite to identifying inappropriate or obsolete knowledge underpin-
ning and/or embedded in existing practices and routines.” We hypothesize that as an unintended
consequence of unlearning strategies clinicians may experience “reactance,” ie, feel their profes-
sional prerogative is being violated and, consequently, increase their commitment to the ineffec-
tive practice.
We define substitution as replacing the ineffective practice with one or more alternatives. A sub-
stitute is a specific alternative action or decision that either precludes the ineffective practice or
makes it less likely to occur.
Both approaches may work independently, eg, a substitute could displace an ineffective practice
without changing clinicians' knowledge, and unlearning could occur even if no alternative exists.
For some clinical practice, unlearning and substitution strategies may be most effectively used
together.
Conclusions: By taking into account the dual process model of cognition, we may be able to
design de‐implementation strategies matched to clinicians' decision‐making processes and avoid
unintended consequence.
KEYWORDS
de‐implementation, dual process cognition, medical overuse, quality improvement, substitution,
unlearning
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1 | INTRODUCTION
Medical overuse is the provision of care that causes harms, is either
ineffective, or where the harms outweigh the benefits.1 One way to
understand medical overuse at the clinician level is in terms of clinical
decision‐making processes that are normally adaptive but become mal-
adaptive because of bias,2,3 or practices that were at one time appro-
priate become outmoded but are retained out of habit.4,5 By clinical
decision making, we mean the process of choosing from among alter-
native courses of action in the diagnosis and treatment of a patient.6
For example, a patient with chronic obstructive pulmonary disease
(COPD) and mild‐to‐moderate airflow obstruction complains of short-
ness of breath. The primary care physician prescribes an inhaled corti-
costeroid, which is effective for treating breathing exacerbations
brought on by asthma—another condition that presents with shortness
of breath—or for COPD patients with severe airflow obstruction.
e pulmonary disease; DICE,
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However, for patients with COPD without reversible airway obstruc-
tion, inhaled corticosteroids have limited or no benefits7 and may
increase their risk of complications including pneumonia.8-10 The
patient's primary care physician is unfamiliar with current guidelines
for managing mild‐to‐moderate COPD, which call for use long‐acting
beta or muscarinic agonists and not inhaled steroids. Instead, the phy-
sician conflates treatment of one condition with that of another that
shares symptoms and the patient receives a medication that does them
little good and instead puts them at risk.
Medical overuse is not driven exclusively or necessarily primarily
by clinicians. For example, at the patient level, patients often lack
awareness of harms; at the organizational level, quality metrics
addressing underuse of care outnumber those addressing overuse; at
the policy level, reimbursement policies often incentivize liberal use
of diagnostics and therapeutics; and at the level of the profession,
medical culture generally esteems thoroughness over restraint.11 How-
ever, we believe there is an opportunity to develop strategies to
address overuse at the clinician level that builds on the great strides
made in past decades in cognitive psychology understanding how
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decision making occurs and why and under what conditions people are
susceptible to bias. And we believe that current efforts to address
medical overuse—and the few conceptual models that have been
applied specifically to overuse—do not adequately consider an impor-
tant aspect of cognition.
In the present paper, we describe a model of cognition broadly
referred to as the dual process model and the distinctive features of
2 types of cognitive processes that appear to guide people's deci-
sion‐making, one intuitive and automatic, and one reflective and delib-
erative. We then propose a conceptual model for promoting de‐
implementation at the individual level based on the idea that clini-
cian‐level de‐implementation strategies can be designed to correspond
to the type of cognition, as well as designed to anticipate the potential
for psychological reactance, a response to perceived threat to freedom
that we hypothesize is a particularly highly elevated unintended conse-
quence of de‐implementation efforts. Finally, we conclude with a dis-
cussion of where this model fits with prior research on the dual
model of cognition and on other models that have been applied to
de‐implementation.
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1.1 | Dual process model of cognition
Clinical decision making,12,13 like all human decision making, appears to
result from 2 distinct modes of cognitive processing14-16: reflective cog-
nition, called Type 2 by cognitive psychologists, a conscious process of
evaluating options based on some combination of utility, risk, capabil-
ities, and/or social influences, then forming and acting on an intention;
and automatic cognition, or Type 1, a set of largely unconscious pro-
cesses that occur in response to environmental or emotive cues and
rely on previously learned, ingrained heuristics.
Automatic cognition is rapid, nimble, and capable of coping with
vast amounts of information through ingrained heuristics and learned
associations, but takes time to develop through practice and experi-
ence. Automatic cognition is exemplified by an experienced automo-
bile driver driving to their local grocery store. It entails rapidly
processing vast amounts information (speed, direction, traffic signals,
other drivers, pedestrians etc, all while retaining a mental model of
the route to their destination) and producing split‐second decisions,
yet human drivers can do this so effortlessly that they may arrive at
the store with no conscious awareness of driving. At the same time,
automatic cognition has important limitations. It is skill specific and
context dependent. The heuristics developed in one setting may trans-
late poorly to a different setting (eg, see Kahneman's account of Lon-
don cab drivers' highly developed spatial and navigation abilities
when tested outside London).17 In addition, when a skill or decision‐
making process becomes automatic, our performance stops improving
and may even decline over time unless we engage in a process of
active learning.18,19
Reflective cognition is the intentional process that is effortful,
slow, and limited in terms of the volume or scope of information it
can process at one time. An example of reflective cognition in action
is multiplying two 3‐digit numbers in one's head. This requires several
calculations while retaining incremental results in your working mem-
ory. For most people, it is not possible to do while multitasking and
is easily disrupted by interruptions, stress, or fatigue. But reflective
cognition is capable of self‐correction, and it is reflective cognition that
leads to the development of the heuristics underpinning automatic
cognition.19 Reflective cognition can continue to improve these heuris-
tics when individuals get detailed feedback on specific aspects of per-
formance and have an opportunity to repeatedly practice the same or
similar tasks.20
1.2 | Viewing medical overuse through the lens of clinician cognition
The way these different forms of cognition determine clinicians' deci-
sions and behaviours has important implications for how we address
medical overuse, that is, the provision of care that either has no benefit
(eg, noninvasive preoperative screening for coronary disease in
patients undergoing noncardiac surgery) or where the harms of care
outweigh its benefits (eg, routine prostate cancer screening for men
over 70).11
Many efforts to systematically curtail, or de‐implement, medical
overuse can be characterized as either directed at (1) engaging
clinicians' reflective cognition to consciously evaluate and correct
overuse; or (2) circumventing clinicians' decision‐making altogether.
Examples of engaging clinicians' reflective cognition include shared
decision making, where clinicians engage with patients to evaluate care
options based on patient's priorities; physician education, where clini-
cians are presented the current evidence‐based guidelines to adjust
their clinical practice; and audit and feedback, where they receive
information on their current clinical practice relative to some bench-
mark, such as peer practices or guideline‐recommended practice. In
each of these examples, clinician decision making is expected to
change as a function of conscious intention to change. Examples of
circumventing clinicians' decision making include prior authorization,
where clinicians must receive permission before a clinical decision
can be acted upon; reimbursement policies unfavourable to certain
clinical decisions; and precluding some ordering options (ie, hard‐stops)
in electronic medical records. In each of these examples, clinicians'
decision making ability is abridged in some way.
The problem with focusing on strategies that target clinicians'
reflective cognition is twofold. First, it largely ignores the dominant
role and corresponding opportunity represented by automatic cogni-
tion in clinical decision making. Many clinical settings where overuse
occurs are inimical to reflective cognition. While it may be feasible to
engage clinicians in reflective cognition outside the clinical setting,
eg, via audit and feedback sessions on cancer screening, behaviour‐
change intentions are only effective when they are retained in active
memory. Intentions are rapidly forgotten, particularly when individuals
multitask, are fatigued and stressed, or are interrupted,21 which often
characterizes settings where clinicians have to make decisions about
care. Moreover, reflective cognition is also susceptible to bias. One
study induced expert physicians to engage in reflective cognition in
making a diagnosis. They found that after reflecting on an initial deci-
sion during an opportunity to revisit and amend their diagnosis, physi-
cians are likely to make the diagnosis less accurate.22 This may be
because expert clinicians develop higher‐order concepts to represent
clinical information and related diagnostic choices—a type of automatic
cognition. These higher‐order concepts come to replace the laborious
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biomedical reasoning used by novices.23 Moreover, the reflective sys-
tem is ineffective if underlying knowledge is absent. If physicians are
enjoined to spend extra time on reaching a diagnosis in complex cases,
diagnostic accuracy improves for experienced physicians but declines
for inexperienced physicians.22 Finally, reflective cognition cannot be
used endlessly; an individual's ability to exert self‐control and engage
in reflective cognition (eg, problem solving, self‐regulating behaviour,
or processing new information) is a finite resource; the more they
engage in reflective cognition to make a decision or guide behaviour,
the greater the difficulty in exercising reflective cognition for a new
task, a condition referred to as ego depletion.24 This is congruent with
research that has highlighted the importance of providing alternatives
to clinicians when taking away ineffective practices.25,26
The second problem with current de‐implementation efforts is
they may fail to take into account the way all people react when their
prerogative is abridged, particularly in a way that seems to impugn
their judgement or character. This loss of freedom can manifest in a
state of elevated arousal termed psychological reactance.27-29 Reac-
tance comprises 2 responses: anger and negative cognition.28 While
anger is an affective state, negative cognition, or counter arguing, is a
cognitive state in which individuals question the authority or the ratio-
nale of the entity they perceive as infringing on their freedom.
We hypothesize that there are 2 potential unintended conse-
quences of reactance for de‐implementation. Individuals may increase
their commitment to the threatened behaviour (eg, engage in it more,
express greater commitment to it), denigrate the source of the threat,
and/or exercise their prerogative in an alternative, undesirable behav-
iour to restore their freedom.30 The implication is that by engendering
reactance we may achieve the opposite of our intended result. There is
some evidence of reactance from research on audit and feedback. A
meta‐analysis of the literature found a significant decline in quality
performance in over a third of feedback interventions. The more feed-
back could be characterized as about the clinician and less about a spe-
cific clinical task, the more likely the feedback intervention was to have
a negative effect.31 The second unintended consequence of reactance
is that the greater the level of counter arguing, the less receptive a cli-
nician may be to reflect on evidence of overuse of their clinical practice
on this or similar issues in the future. They may be more likely to reject
data out of hand or rebuff efforts to engage with them on overuse of
the ineffective practice. A poorly designed or executed de‐implemen-
tation strategy could thus make it less likely that subsequent de‐imple-
mentation efforts will be effective.
FIGURE 1 The unlearning process
2 | A MODEL FOR DE‐ IMPLEMENTATION STRATEGIES BASED ON THE DUAL PROCESS MODEL OF COGNITION
We propose that de‐implementation strategies directed at clinicians
can be conceptualized as corresponding to the 2 types of cognition:
(1) a process of unlearning based on reflective cognition and (2) a pro-
cess of substitution based on automatic cognition. In the discussion,
we will contrast our approach with several other recent efforts to
understand the psychological underpinnings of de‐implementation
and their implications for more effective de‐implementation.
2.1 | Unlearning
We define unlearning as an active process in which clinicians con-
sciously change their knowledge, beliefs, and intentions about an inef-
fective practice and alter their behaviour accordingly.32 Cegarra‐
Navarro and colleagues33 describe unlearning as “the questioning of
established knowledge, habits, beliefs and assumptions as a prerequi-
site to identifying inappropriate or obsolete knowledge underpinning
and/or embedded in existing practices and routines.”We term this crit-
ical assessment of the evidence.
For example, antipsychotic medications are often used to address
agitation and aggression in patients with dementia,34 even though
antipsychotics are associated with side effects (eg, cognitive worsen-
ing, abnormal gait), increased stroke risk, and increased mortality,35
and there is limited evidence for their use.36 An unlearning approach
to reduce antipsychotic use could be operationalized in terms of an
audit and feedback program37 in which the prescribing clinicians were
presented with data on both the harms from antipsychotics in this
patient population and their prescribing among patients with dementia,
compared to peers, over some specified timeframe, with goal setting
for prescribing in the future (Figure 1). Whether the clinicians engaged
in a critical assessment of the evidence as a result could be measured
through surveys or interviews about the clinicians' knowledge and
opinions about the evidence and their intention to subsequently
change. The overall effectiveness of the strategy could then be
assessed in terms of a change in the average use of antipsychotics by
the clinician or from chart review of the clinician's patients with
dementia who were on antipsychotics to assess the appropriateness
of the medication.
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The unlearning process can occur across a spectrum, from simple
to deep unlearning.38 Simple unlearning occurs when new evidence
that a practice is undesirable can be evaluated and understood within
a clinician's existing mental models (ie, the way they understand the
world to work). The idea that antipsychotics could increase risks of falls
may fit within existing mental models for prescribing clinicians and only
simple unlearning would need to take place to unlearn the practice in
the context of reducing the risk of falls. Deep unlearning entails over-
coming a significant challenge to a mental model, particularly when the
assumptions inherent to the model may be so fundamental that they
are not even recognized as assumptions. For example, clinicians may
not understand why some events will seem to trigger patients with
dementia to act out violently. It may require deep unlearning to accept
the idea that a patient with dementia exhibits aggression or agitation
as a rational reaction to external stimuli, such as changes made to
the organization of their room, or someone reminding them that a
loved one is dead. The clinician may not perceive the stimulus or fail
to see how it could account for the ensuing behaviour.
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2.2 | Substitution
We define substitution as the promotion of one or more alternatives to
the ineffective practice, in which the substitutes replace or displace the
ineffective practice. We define a substitute practice as a specific alter-
native action or decision that either precludes the ineffective practice
or makes it less likely to occur.
For example, a substitution approach to reduce antipsychotic use
could be operationalized in terms of training frontline nursing aides
at a long‐term care facility in the Describe, Investigate, Create, Evalu-
ate (DICE) program (Figure 2). The DICE is a collaborative training pro-
gram to empower nursing aides and other frontline staff to address
patient agitation and aggression by targeting the behavioural symptom
and the context in which it is occurring, examining and addressing
likely precipitants, such as unexpressed pain, and iteratively evaluating
the effectiveness of solutions to the underlying causes.39 The DICE
represents substitution because frontline nursing staff are the ones
who first and most frequently encounter patients' agitation and
aggression and are the ones who most often bring it to the attention
of prescribing staff.
To be effective, the substitution strategy would likely require a
period where external support from a trainer or other expert rein-
forced use of DICE until it became an ingrained part of nursing care,
ie, until nursing aids and frontline staff developed new heuristics and
routines related to DICE. Whether nursing aides use DICE could be
measured through direct observation, surveys, or interviews. As with
unlearning, the overall effectiveness of the strategy could then be
assessed in terms of changes in antipsychotic use.
While we focus on introducing a substitute in the model, we
hypothesize that in some situations it may be possible to engage auto-
matic cognition in other ways, such as removing cues that trigger the
initial decision to use an ineffective practice, changing choice architec-
tures to discourage an ineffective practice, or introducing behavioural
nudges. For example, an electronic order set for upper respiratory tract
infection can be designed to discourage the use of antibiotics by pre-
senting over‐the‐counter treatments first, and grouping all prescription
options after.40
2.3 | The relationship between the two
We expect that both approaches may work independently. The DICE
could displace antipsychotic use without causing prescribing clinicians
to question their former knowledge or assumptions about the risks and
benefits of antipsychotic use. Conversely, the audit and feedback
intervention could cause prescribing clinicians to eschew prescribing
antipsychotics more frequently, even if no true alternative for address-
ing aggression and agitation exists. Unlearning and substitution strate-
gies may work best, however, when used together rather than
separately, such as using both audit and feedback and training in the
DICE program.
A single de‐implementation strategy might incorporate both
unlearning and substitution approaches simultaneously. Taking the
prior example of inhaled corticosteroids prescribed to patients with
COPD, in an integrated delivery system with an electronic health
record, a team of pulmonologists could proactively conduct a chart
review of all patients with COPD who have an inhaled corticosteroids
prescription to determine if there is an indication for inhaled cortico-
steroids. If not, the pulmonologist could write an unsigned order to
cancel the inhaled steroid order and change the patient's therapy to
a more suitable agent (usually a long‐acting beta agonist or a long‐
FIGURE 2 The substitution process
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acting anticholinergic bronchodilator), providing an explanation for the
change and a link the guidelines. The primary care provider would see
the unsigned order with the explanation for the change from the pul-
monologist, with plans for communicating the change to the patient.
The unsigned order could operate through substitution approach
because it gives the primary care provider an immediate alternative,
ie, sign the order leading to the patient transitioning to a standalone
long‐acting agent. Furthermore, through repeated exposure to the
unsigned order, the provider might internalize the association (ie, to
take patients with mild‐to‐moderate COPD off inhaled corticosteroids
and put them exclusively on long‐acting beta or muscarinic agonist) on
the level of automatic cognition. The unsigned order also embodies
unlearning as it includes an explanation for the change and link to
the guidelines; persuasion through presentation of evidence and for-
mation of intention to change could be a key mechanism by which
the provider de‐implements inhaled corticosteroids. As with the exam-
ple of antipsychotics, the effect of the unsigned orders in terms of
unlearning could be assessed through interviews or surveys with pri-
mary care providers about their knowledge of options about inhaled
corticosteroids use in this patient population. This could occur before
and after the de‐implementation strategy was used. The effect of the
unsigned order in terms of substitution could be assessed by
discontinuing the unsigned orders after a period and measuring
whether inhaled corticosteroids use declined while the unsigned
orders were in place and returned to baseline levels when they were
discontinued.
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3 | DISCUSSION
The model for de‐implementation strategies described above is
focused on how provider cognition might be considered in the design
of provider‐level de‐implementation strategies. We believe it makes
an important contribution to the literature on medical overuse and cli-
nician decision making by proposing how de‐implementation strategies
can be designed to correspond to a specific type of cognition and
designed to anticipate the potential for unintended consequences.
Specifically, this model introduces the idea of substitution as a strategy
for addressing overuse at the clinician level; it specifies substitution as
a strategy distinct from but complementary to unlearning strategies;
and it hypothesizes psychological reactance as an unintended conse-
quence of de‐implementation efforts, particularly from unlearning
approaches, that could have long‐term consequences.
Others have applied dual process models to learning, unlearning,
and implementation of evidence‐based practices.41-45 For example,
Nilsen et al,45 drawing from learning and habit theory, propose 2 types
of learning—adaptive and developmental. The former involves a grad-
ual shift from slower, deliberate behaviours to faster, smoother, and
more efficient behaviours while the latter is conceptualized as a pro-
cess in the opposite direction, whereby more or less automatically
enacted behaviours become deliberate and conscious. They see adap-
tive and developmental learning as continuous, iterative processes that
allow new evidence‐based knowledge to be incorporated into clinical
care. The process they term developmental learning is unlearning
in our model. In contrast, we differentiate between unlearning and
substitution. Substitution is more analogous to human‐factor
approaches to change behaviour by redesigning the work environ-
ment,46-48 or behavioural economic approaches that introduce nudges
and change the way options are presented to clinicians (choice archi-
tecture).40,49-52 These approaches, in contrast to unlearning, do not
seek to engage clinicians in consciously correcting the underlying bias
driving medical overuse.3 We believe this is a novel addition to the
application of dual process models to evidence‐based care.
In terms of conceptual models of de‐implementation, we are
aware of three. Niven and colleagues,53 as part of their literature syn-
thesis on de‐implementation, propose adapting the Knowledge‐to‐
Action model, which is a continuous cycle of knowledge inquiry, syn-
thesis, and adaptation in the clinical setting, in which implementation
strategies are selected and tailored on the basis of the barriers to
knowledge use and informed by monitoring and evaluation. Parchman
and colleagues54 have proposed a planned‐action model that articu-
lates steps in engaging clinical practices in practice transformation to
address overuse. The steps include creating conditions for change,
engaging in sense‐making conversations with care teams and allowing
them to take a sense of ownership of the issue to make their own deci-
sion about where the balance of benefit and harm lies. Finally, several
studies have adapted the Theoretical Domains Framework to guide or
interpret de‐implementation findings.55-58 The Theoretical Domains
Framework synthesizes psychological theories of behaviour change
to help develop theoretically grounded behaviour‐change interven-
tions. It encompasses 84 constructs organized in 14 domains, such as
knowledge, intention, and beliefs about consequences.59 All 3 models
are valuable contributions to the de‐implementation literature that
articulate a process for systematic unlearning at multiple levels (in the
first two) and keys domains that can be targeted (in the last). All 3 of
these implicitly focus on engaging frontline clinicians in unlearning.
Our model adds to this literature by juxtaposing the idea of substitution
as a distinct, potentially complementary strategy for de‐implementa-
tion at the provider level. Furthermore, our model identifies psycholog-
ical reactance as a particular risk from de‐implementation efforts.
Finally, effective de‐implementation efforts—as with effective
implementation efforts—need to consider multiple levels synergisti-
cally: individuals, teams, organizations, and environments.60 We will
need to consider how de‐implementation strategies at different levels
can be designed synergistically with unlearning and/or substitution
strategies. Patient education and reimbursement policies, for example,
might be incorporated with audit and feedback to create a synergistic
intervention to reduce the use of active follow‐up of incidental lung
nodules found during nonlung cancer chest CT scans. This may require
developing collaborations outside the existing research model in which
researchers engage with advocacy groups, patients and caregivers,
policy makers, payers, and others.
4 | CONCLUSION
Under certain circumstances, normally adaptive clinician decision
making is susceptible to bias and becomes maladaptive. This may
be one important contributor to medical overuse. By taking into
account the dual process model of cognition, we may be able to design
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de‐implementation strategies that take into account clinicians'
decision‐making processes and avoid the unintended consequence of
psychological reactance.
ACKNOWLEDGEMENTS
Dr Steven B. Zeliadt, Dr George S. Sayre and Ms Kathryn Todd of VA
Puget Sound have provided feedback on early drafts of the conceptual
model.
This project was conducted as part of VHA Health Care Opera-
tions in accordance with VHA Handbooks 1605.1 and 1605.2. The
project team had full responsibility for the design and conduct of this
project; and preparation of the manuscript. The project was reviewed
through normal administrative channels. The views expressed in this
article are those of the authors and do not necessarily reflect the posi-
tion or policy of the Department of Veterans Affairs or the United
States Government.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
FUNDING
This work was supported by grant no. QUE 15‐271 from the United
States Department of Veterans Affairs Quality Enhancement Research
Initiative (QUERI) Program. Dr. Graham is a recipient of the Canadian
Institutes of Health Research (CIHR) Foundation Grant #143237. The
CIHR funded Integrated Knowledge Translation Research Network
(FDN# 143237) provided partial funding for the article's open access
costs.
ETHICAL APPROVAL
No ethical approval required for this study.
CONSENT FOR PUBLICATION
No data are reported.
AVAILABILITY OF DATA AND MATERIALS
No data are reported.
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ibrary for rules of use; O A
articles are governed by the applicable C reative C
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ons L ice
AUTHORS ' CONTRIBUTIONS
C.D.H. drafted the original model and wrote the manuscript. A.J.R., C.
W.H., L.B.V., I.D.G., S.J.W., B.R.M., C.B.G., L.M.P., S.B., D.H.A., and
D.C.A. provided feedback and revisions on the conceptual model and
reviewed and revised the manuscript. All authors reviewed and
approved the final manuscript.
ORCID
Christian D. Helfrich http://orcid.org/0000-0002-9827-4768
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How to cite this article: Helfrich CD, Rose AJ, Hartmann CW,
et al. How the dual process model of human cognition can
inform efforts to de‐implement ineffective and harmful clinical
practices: A preliminary model of unlearning and substitution. J
Eval Clin Pract. 2018;24:198–205. https://doi.org/10.1111/
jep.12855
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