DBT at the Forefront of Clinical Care

Clinical Psychological Science | 2025, Vol. 13, No. 1, p. 69- 82.

How Idiographic Methodologies Can Move the Clinical-Science Field Forward to Integrate Personalized Treatment Into Everyday Clinical Care and Improve Treatment Outcomes

Cheri A. Levinson - Department of Psychological and Brain Sciences, University of Louisville

Caroline Christian - Department of Psychological and Brain Sciences, University of Louisville

Carolyn B. Becker - Department of Psychology, Trinity University

The research–practice gap refers to the fact that most evidence-based treatments created by researchers are not used in routine clinical care, which affects real-world treatment outcomes negatively. One key reason that evidence-based care is not used more frequently is its non-personalized format. For example, most evidence-based treatments are based on averages and are limited in addressing comorbidity, heterogeneity, and the needs of clients with minoritized identities. These limitations reduce therapist uptake of evidence-based treatment at large. As a result, most patients seeking treatment in community settings do not receive evidence-based care, which could more quickly and effectively reduce mental-health suffering. Furthermore, even clinicians who want to engage in evidence-based practice must still rely on their clinical judgment in decision-making when treatments fail to address client-specific needs. This reliance on decision-making can influence outcomes negatively. We propose that idiographic (i.e., one-person; N = 1)methodologies (data analysis of one person’s data) combined with digital mental-health technology could help reduce the research–practice gap and improve treatment outcomes. In this article, we outline the many issues contributing to these problems and how idiographic methods of personalization can address these issues. We provide an overview of idiographic methodologies and examples of how to use these methods to personalize existing evidence-based treatments with patients. Finally, we conclude with recommendations for future research and movement within the field that is needed to propel this type of personalization into routine clinical care to reduce the research–practice gap and improve treatment outcomes broadly.

research–practice gap, treatment personalization, idiographic methods, technology, network theory

“A large amount of research has been devoted to addressing the research–practice gap and to improve outcomes of psychotherapy. However, despite the increase in dissemination and implementation-focused research in recent decades, both problems remain…

Clinicians have very good and understandable reasons for finding existing evidence-based intervention limited— particularly for patients who present with a complex array of problems or deviate from the average. Although some work exists to reduce reliance on flawed clinical judgment on an individual basis, such solutions are not without limitations (inability to carefully select appropriate treatment targets, tailor treatment for minoritized needs using data, etc.)” (p. 70- 73).

“...We are unaware of any research that has used idiographic networks within a [Dialectical-behavior therapy] (DBT) context, although DBT seems a logical treatment approach to match with symptom-based problems. DBT is a treatment originally designed for treatment of borderline personality disorder but has been expanded across a wide variety of psychiatric disorders, including EDs. 

The DBT treatment manual consists of four primary modules (mindfulness, emotion-regulation skills, distress tolerance, interpersonal effectiveness), each with many subskills. Patients in DBT program generally complete all four modules in a standardized format, although there is a clear need to personalize when and for whom specific skills are used. In DBT, patients complete daily diary cards in which they track both symptoms and skills that they used. These data could easily be translated into an idiographic assessment, and network models could be built that show which daily symptoms are most problematic. Then, DBT skills can be matched to specific patient problems to address personalized problems. For example, patients could complete smartphone assessment of deficits either in broad DBT modules (e.g., emotion regulation, interpersonal effectiveness) or in specific skills within modules (e.g., shame, assertiveness) and then use these data to select which skills/modules should be most relevant for the particular patient. In this way, DBT could be applied using a data-based personalized method. It is possible that this type of personalization could not only improve outcomes but also reduce time in treatment” (p. 77).

“One major issue that arises from the use of idiographic modeling is the heavy emphasis on quantitative modeling. An initial reaction may be that clinicians do not have the skills or time to assess and then model symptoms for each of their patients… We envision (and have created) a HIPAA-compliant software system that automatically pairs with patients’ phones to regularly assess symptoms and then uploads and outputs treatment recommendations directly to the clinician… This type of system would require no advanced analytic knowledge on the part of the clinician or patient. Instead, the software (used on a patient’s smartphone paired with a clinician’s computer) would deliver personalized treatment recommendations that could shorten treatment and free up clinician time that would generally be spent on case consultation, decision-making, and consultation (e.g., treatment teams) on which symptoms to target in treatment. 

Treatment suggestions could be paired with brief, automatized trainings providing guidance in how to implement the specific treatment module if the treatment was unfamiliar to the clinician. This type of system can also incorporate clinician feedback in terms of both assessment and treatment recommendations, which could help bridge the research–practice gap by including the clinician. This incorporation may be important for clinicians, who have not infrequently balked at the ideas that manualized treatment ignores the art of psychotherapy and makes treatment too cookbook-like. Thus, inviting clinicians to be part of the process may enhance clinician acceptance and overall usability and integration of these types of tools into clinical practice. For assessment, clinicians could be asked to give recommendations on which symptoms they think should be assessed based on their clinical expertise and knowledge of their patients. Indeed, recent work has begun to create idiographic network algorithms that incorporate both clinician and patient outcomes” (p. 78).

“The ideas presented here represent a shift in the way in which clinical psychology thinks about, researches, and treats psychological disorders. Instead of relying on latent variables and categories to define the problems seen in the clinic, it shifts to a symptom- or problem-based dimensional and personalized perspective, personalized at the individual level. Our charge to researchers in the clinical-science field is to embrace and explore this new perspective. Embracing this new way of thinking means revising the field’s diagnostic, assessment, and treatment systems. The old approach has brought the field far, but patients deserve more than “good enough” treatments. Too many patients do not respond to existing treatments; clinicians do not embrace manualized treatments, widening the research-practice divide; and patients fall between the gaps of diagnostic criteria based on categories. An idiographic network approach places individual problems at the forefront of research and treatment and can lead to a new system designed with data and personalized treatment at the forefront of clinical care” (p.79).

“Ideally, this type of system could be paid for by insurance or state health systems, but this idea will need to be evaluated. In this way, we hope that our research methods can create a product that bridges the research–practice gap and improves treatment outcomes. We believe the idiographic methodologies combined with digital software technologies can help bridge the research–practice gap by providing an easy-to-use, clinician-friendly software tool that clinicians who may not have access or would be too busy to use traditional manualized treatments can implement in their practice, thereby increasing uptake of evidence-based treatments. Furthermore, the way in which this software is developed reflects the clinical reality that many clinicians draw from multiple evidence-based treatments and combine them in a nonstandard format. This technology maps onto clinical reality, which should affect positive implementation. In addition to potentially minimizing the research–practice gap, these methods should improve efficacy of standard evidence-based treatments because the primary problems will be pinpointed earlier and there would be less time spent on treatment of possibly superfluous issues” (p. 79).

“What is the best idiographic methodology/algorithm that should be used? 

In addition to the development of idiographic methods to identify most significant problem symptoms for the individual, additional research will be needed to adapt or develop treatment modules that can significantly and reliably target the problem. It is also possible there might be multiple modules that could target the problem. For example, there may be multiple interventions for “shame.” Future research is needed to develop both the most efficient idiographic models and matched treatment modules. It is possible that multiple idiographic models could be run throughout the course of treatment to update the treatment plan as primary problems change. More research is needed to identify the optimal timing and analytic application of idiographic models throughout treatment. Finally, there is increasing recognition that social determinants of health, such as food insecurity, weight stigma, and racism, affect mental health outcomes” (p. 79).

As always, please join the discussion below if you have thoughts or comments to add!

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