Personalization and Precision: A New Paradigm
There is a sense of excitement and change occurring in mainstream medicine. President
Obama, in his State of the Union address on January 30, 2015, announced a national
Precision Medicine Initiative (The White House 2015). More recently, the United Kingdom’s
government innovation agency started a Precision Medicine Catapult designed to enhance
the development of precision medicine in the UK (Precision Medicine Catapult 2015).
Precision medicine is defined by the National Research Council as “the tailoring of
medical treatment to the individual characteristics of each patient” (National Research
Council (US) Committee on A Framework for Developing a New Taxonomy of Disease 2011).
This builds on an increasing interest in personalized medicine and, indeed, the terms
“precision medicine” and “personalized medicine” are sometimes used interchangeably
(Avitabile 2015). Common to both is an emphasis on tailoring treatment to individual
needs and, increasingly, on the role of technology to support that goal (Carney 2014;
Sacchi et al. 2015).
Although much of the focus of medicine to date has been on biomarkers and genetics
(McCarty et al. 2011), the concept is not limited to those factors. Just as critical,
but less widely elaborated, are psychosocial variables that also fit under the umbrella
of precision and personalized medicine. Increasing discussion has also focused on
the relevance of precision medicine to mental health. Thomas Insel, a former Director
of the National Institute of Mental Health (NIMH), has argued that the basic tenets
of precision medicine are reflected in the NIMH Research Domain Criteria (RDoC) project,
“which aims to develop more precise diagnostic categories based on biological, psychological,
and socio-cultural variables” (Insel 2015). He adds that
“…precision medicine for mental disorders will not come from a single genomic glitch.
Rather, like many other areas of medicine, many genes each contribute only a small
amount of vulnerability as part of an overall risk profile that includes life experiences,
neurodevelopment, and social and cultural factors. RDoC assumes that we will need
many kinds of data to reach precision, more like triangulating to find your position
on a map. These data will draw from many sources, including symptoms, genotype, physiology,
cognitive assessment, family dynamics, environmental exposures, and cultural background.”
The NIMH’s emphasis on including a wide variety of assessment data in the pursuit
of precision recognizes that mental health must move beyond genetic factors as the
sole focus of RDoC-facilitated precision.
Precision Mental Health: Definitions and Requirements
For precision medicine to become a reality in mental health, it is necessary to have
precise assessment, monitoring, and feedback information. We define precision mental
health as an approach to prevention and intervention that focuses on obtaining an
accurate understanding of the needs, preferences, and prognostic possibilities for
any given individual, based on close attention to initial assessment, ongoing monitoring,
and individualized feedback information, and which tailors interventions and support
accordingly in line with the most up-to-date scientific evidence. In particular, this
data-driven approach to clinical decision-making should include seven types of psychosocial
data, which are described below (and summarized in Table 1):
Personal data relevant to understanding the nature of presenting problems and how
they might be addressed may include description of the presenting problem and/or psychiatric
diagnoses, but also consideration of other factors, including genetic, developmental,
social, and cultural variables. We anticipate that this will go beyond more than just
symptoms, to include prominent and systematic consideration of information that may
inform intervention choices, including motivation to change, personality traits, and
demographics.
Aims and risks data Clarifying the focus and expected outcomes of treatment as well
as any risks or likely side effects is a key issue for mental health and one that
currently is all too often hazy or ill-defined. This does not mean that service users
get to choose any aim or goal to work on and the service provider has to comply; rather,
this is about capturing what has been mutually agreed as the focus for treatment to
allow precision in terms of tailoring the intervention to the aim, along with any
acknowledged risks, and ensuring progress toward this end. Service recipients with
identical symptom profiles and case formulations often have different aims, and these
aims may further diverge from those of their care provider. Precision mental health
tailors activity to the specific agreed-upon aims.
Service preference data relevant to understanding patient/client choices at key decision
points regarding services. Similar to aims data, service recipients with identical
symptom profiles and case formulations may have divergent preferences for different
interventions. In situations where the evidence for two different interventions is
relatively equally balanced, then preference data are crucial to help guide intervention
selection, to ensure personalization and precision (Jacob et al. 2015), and to prevent
misdiagnosis of preference (Mulley et al. 2012).
Intervention data that capture aspects of the services delivered over the course of
treatment, including their dose/intensity, duration, cost, and timing. This includes
precision as to different interventions and aspects of interventions, and may benefit
from taxonomies that are not just modality based, using the TIDieR framework to capture
details of interventions (Hoffmann et al. 2014). These include the behavioral taxonomy
developed by Michie et al. (2014) and the “common elements” of evidence-based treatments
suggested by Chorpita et al. (2005), alongside more traditional “common factors” identified
in the literature (Bickman 2005). Aspects of intervention integrity/fidelity (i.e.,
adherence, competence, differentiation, and relational elements; Southam-Gerow and
McLeod 2013) also represent key aspects of intervention data.
Progress data relevant to understanding movement toward the intended and agreed aims
of any intervention and against identified benchmarks (see #3 above). These data are
collected routinely over time using within-subjects comparisons and relevant metrics
as identified in #1 and #3 above.
Mechanisms data relevant to the hypothesized link between intervention and outcomes
(Kazdin, 2007). These are frequently the hypothesized mediators of treatment. For
example, therapeutic alliance would be included as an explanatory factor if it is
not considered to be an explicit component of the intervention (see #5), but this
might also include skills developed by the service recipient as part of the intervention,
such as increased coping skills or social skills.
Contextual data relevant to understanding the factors that moderate or mediate outcomes,
such as quality and amount of service available, or other data external to the individual
or the intervention delivered (which are captured in #4 and #6). These are data about
the environment in which the individual lives, in contrast to personal data (described
in #1).
Table 1
Types of psychosocial data relevant to precision mental health
Data type
Description
Personal data
Individual-level information that may inform intervention choice/selection (e.g.,
demographics; diagnoses; cultural variables; motivation to change)
Aims and risks data
The focus and expected outcomes of treatment as well as potential risks
Services preference data
Client choices/selections at key decision points regarding services
Intervention data
Aspects of the services delivered over the course of treatment (e.g., intervention
integrity; dose/intensity; duration; timing)
Progress data
Movement toward the intended and agreed aims of any intervention, and against identified
benchmarks
Mechanisms data
The hypothesized link between intervention and outcomes. May be mediators of treatment
(e.g., skills development or use, therapeutic alliance, etc.)
Contextual data
Factors external to the individual/intervention that moderate or mediate outcomes
(e.g., quality and amount of service available; family functioning data)
Precision mental health can be distinguished from current “best practice” in mental
health promotion and provision in the following ways. First, it involves careful,
ongoing consideration of the seven data elements above over the course of any intervention.
In this way, precision mental health should be “data driven” in a manner that extends
well beyond the growing contemporary emphasis on client outcome tracking. Second,
given the extensive data that will be required to make precision mental health a reality,
our conceptualization is committed to using relevant technology to manage information
and support precision in assessment monitoring and feedback. It should be acknowledged,
however, that precision mental health is currently an aspirational goal and that much
of the current data in mental health are largely flawed and proximate (Wolpert et
al., 2014). In light of this, those seeking to support precision mental health need
to take due account of the imprecision of current data sources.
Precision Mental Health, Measurement, and Feedback in Clinical Practice
This special issue marks a step toward considering current best practice in using
these data sources to support precision mental health across both the United States
and the United Kingdom. Although none of the authors in the present issue have conceptualized
their work in terms of precision mental health, we feel that all the contributors
are working toward this end. We advocate that, as a community of researchers and practitioners,
we should begin to frame the collection and use of patient-reported outcomes and other
measures in terms of precision mental health. We anticipate that doing so will not
only facilitate alignment between mental health and the broader healthcare agenda,
but also help to overcome some terminology differences that have emerged in the areas
of outcome monitoring and feedback, which we would like to redress.
In particular, there is a plethora of terms used across the literature to refer to
various components of precision mental health services. These include Measurement-Based
Care (MBC) (Scott and Lewis 2015), Outcome-Informed Therapy (Duncan et al. 2011),
Feedback Informed Therapy (FIT) (Miller et al. 2015), Routine Outcome Monitoring (ROM)
(Carlier et al. 2012), and Measurement Feedback Systems (MFS) (Bickman 2008). Among
these, ROM and MFS are the two most common shorthand terms that have come to be used
differently across the United States and United Kingdom to refer to the varied elements
of the assessment, monitoring, and feedback process. The former emphasizes the importance
of collecting data that inform an understanding of outcomes—with a focus particularly
on #1–3 and 5 above—and is widely used in the United Kingdom. The latter emphasizes
the use of systems to provide feedback from those accessing services, which also focuses
on data related to #1–3 and 5 above, but has additionally paid more attention to other
relevant data on a routine basis, including mechanism data (#6 above), and consideration
of the nature of interventions (#4 above). This includes natural language descriptions
of the content of treatment above (Kelly et al. this issue) and specific evidence-based
intervention components (Chorpita et al. this issue). In practice, the terms are often
used interchangeably, and those promoting ROM and MFS approaches share a common commitment
to systematically capturing data and supporting clinicians to make use of all the
elements listed above. Regardless of the terminology, this is a revolutionary perspective
given that traditional mental health intervention does not involve any systematic
data collection or considerations of outcomes from the user perspective (Garland et
al. 2003; Hatfield and Ogles 2004). Moreover, these are universal approaches to improving
outcomes that can be used regardless of the type of treatment or characteristics of
the client or clinician. We would advocate that they be increasingly subsumed under
the term precision mental health.
Precision Mental Health: Challenges and Opportunities
Relevant Data Components
We anticipate that the advancement of precision mental health will require greater
use of data sources not yet fully tapped by current approaches to mental health symptom
assessment, such as educational- or employment-related functioning, cognitive and
neurological testing, and other bio-social indicators (relevant to #1, 6, and 7 above).
There is no conceptual reason why these data elements cannot be increasingly integrated
into feedback systems, particularly as systems move to be largely digital and cloud-based
with rapid real-time reporting possible (Lyon et al. 2016). However, since many of
the measures developed in this area are laboratory-derived, a translational process
might be necessary to make them feasible in the real world. For example, Bickman and
colleagues developed a battery of measures that are designed for use in real-world
settings where time is short (Bickman and Athay 2012).
Moreover, precision mental health provides an opportunity for the field to move beyond
traditional self-report data. Almost all the data currently collected are based on
clients’ or others’ completion of questionnaires. Although such an approach provides
critical information about clients’ perceptions of their own difficulties, this mono-method
dependency is problematic. While we are aware that we still have much work to do to
integrate and understand self-report data (De Los Reyes 2011), we are missing new
and rapidly emerging sources of information. For instance, Torous and Baker (2016),
as well as many others, have noted that the new technologies based on smartphones
and wearable sensors offer access to data and events that are not possible with electronic
or paper-based questionnaires completed in the office or clinic. Although there are
numerous complex issues that need to be resolved with the use of these new technologies
(e.g., privacy, security, validity), there is significant potential to transform what
we know about mental health and mental health services.
Among the data sources captured in the list presented above, information about the
intervention itself (#4 above) is particularly underdeveloped. Physical medicine is
going through a major cultural shift from the practice of medicine as an art to medicine
that is evidence based and follows guidelines and standards. However, this has not
been a simple journey, and some of the problems encountered may be remedied by an
emphasis on precision medicine (Greenhalgh et al. 2014). Although many evidence-based
treatments exist in mental health, research indicates that these are not yet part
of the mainstream clinical culture (Becker et al. 2013). Moreover, there is currently
little incentive for providers to use these treatments and monitor their fidelity.
Thus, most care is described using the imprecise—and typically heterogeneous—term
“treatment as usual”. Many of the feedback studies to date have introduced feedback
practices into that “treatment as usual” context, which may not be optimal. This lack
of precision in describing treatment is a handicap for feedback systems, because is
it unclear not only what data to relay, but also what actions the clinician should
take based on the feedback. The use of frameworks to identify intervention components
(e.g., Chorpita et al. 2005; Michie et al. 2014) should continue to be advanced, but
they are not yet widely embedded in practice, as will be noted in many of the contributions
to this special issue.
Building Precision Mental Health Databases
The mental health field lacks high-quality, large databases that include linked data
from #1 to #7 above. Databases currently available to form the basis for precision
medicine are likely to be drawn from three sources: clinical trials, routine care,
and cohort studies. While we could find no systematic data on the sizes of clinical
trials, ClinicalTrials.gov, as of December 2015, lists 192,475 trials, 7366 (3.8 %)
of which deal with some aspect of mental health. Most of these include some elements
of #1–7, but not all. Furthermore, many will be limited in the populations covered.
Cohort studies including those developed by groups of volunteers are a potentially
useful source of data (Precision Medicine Initiative Working Group, 2015), but the
mental health aspects of such databases are typically limited. For the foreseeable
future, routine care is likely to be the key source of data for pursuing precision
mental health. However, these datasets are likely to be highly flawed and incomplete,
suffering from the challenges common to administrative datasets (e.g., missingness,
inadequate specification) and exacerbated by the fact that, in mental health, we will
have to depend on typical community-based treatment. Significant sources of data for
health care are hospital data systems and laboratory test results. Hospitals and laboratories
have a long history of collecting and maintaining relatively high-quality data, but
outpatient mental health services often do not share this tradition. Furthermore,
most existing data systems are not designed to “talk” to each other. This interoperability
problem exists in physical medicine, but there are financial incentives for providers
to develop such systems (e.g., Blumenthal and Tavenner 2010). Moreover, there are
large investments being made by governments to create solutions.
Presently, ROM and MFS are in the forefront of developing technologies suitable for
mental health to obtain the needed data. However, given the lack of similar incentives
and financial resources, and the lack of standardized and widespread measurement,
progress will be slow. The quality of mental health data from routinely collected
data sources is therefore likely to remain a problem for some time to come. Many of
the papers in this special issue deal with the problems inherent in collecting such
data in the real world.
Facilitating Ease of Data Capture and Use in Mental Health
One of the major challenges this field faces concerns the implementation of data capture
and use in the context of under-resourced and overstretched services. In many cases,
new measures must be developed because the existing measures were developed for research
projects without severe time restrictions for data collection. The resources available
in research settings stand in contrast to the conditions of service delivery in the
real world, where assessment is often seen as “stealing” time from treatment. Furthermore,
the focus on monitoring makes more relevant individualized (i.e., idiographic) assessment
approaches that are typically used for intra-individual comparisons (i.e., comparing
individuals with themselves over time), rather than comparing individuals with established
norms from a larger population (Haynes et al. 2009; Weisz et al. 2011). Many of the
articles in this issue address the issue of implementation and draw on implementation
science for suggested ways forward.
MFS and ROM Support Precision Mental Health
The current special issue contains two companion sections that showcase projects designed
to support the elements of precision mental health listed above. They address some
of the challenges previously identified via different technical (i.e., training, consultation,
learning collaborative) and technological (i.e., digital measurement feedback systems
and electronic health records) strategies. The special issue arose because of a range
of work going on across the United States, United Kingdom, and elsewhere (e.g., the
Netherlands) where researchers and practitioners were experiencing common challenges
and concerns. Originally designed as two separate contributions, the commonalities
between the groups became clear and therefore they were brought together in one issue
while treating each section with its own introduction and overview. For specific information
about the individual article author contributions, the reader is referred to the individual
special section introductory papers. Specifically, Edbrooke-Childs, Wolpert and Deighton
(this issue) have prepared a section focused on the use of patient-reported outcome
measures (PROMs), which includes consideration of training and support necessary to
allow for implementation. Lyon and Lewis (this issue) oversee a section that focuses
on the development and implementation of digital MFS technologies explicitly designed
to support ROM practice.
Papers in both sections stress that implementation and long-term sustainment of using
patient-reported outcomes and other data to inform practice can be fraught with challenges,
such as varying levels of organizational buy-in, long timelines, and mounting costs.
Nevertheless, they also demonstrate the potential payoffs of successfully installing
these innovations. Furthermore, the papers make it clear that the implementation of
feedback technologies involves many of the same issues as those involved in the implementation
of other evidence-based practice changes in behavioral health. Thus, they require
good design and packaging to make them accessible and useable for practitioners, and
to facilitate their uptake and long-term use. This may be accomplished by explicitly
incorporating stakeholders and stakeholder perspectives into structured processes
for the development, selection, and implementation of new innovations. Consistent
with the broader implementation literature (Beidas and Kendall 2010), effective training
and consultation procedures are necessary regardless of the type of innovation being
implemented. Furthermore, both sections make clear the value of qualitative, quantitative,
and mixed-methods approaches to (1) evaluate clinician and service recipient views
toward the technological and practice changes that characterize implementation of
feedback approaches, (2) tailor the practices or technologies to meet their needs,
and (3) determine their effectiveness in promoting positive service outcomes. With
this special issue, we hope to advance the science and practice of precision mental
health by considering the capture, feedback, and use of data in community service
settings, as well as the processes and strategies through which these innovations
are developed, implemented, and evaluated.