{"title":"Measurement-based matching of patients to psychotherapists' strengths.","authors":"Michael J Constantino","doi":"10.1037/ccp0000897","DOIUrl":null,"url":null,"abstract":"<p><p>Treatment personalization has evolved into an important zeitgeist in psychotherapy research. To date, such efforts have principally embodied a unidirectional focus on personalizing interventions to the patient. For example, earlier work in this area attempted to determine whether, on average, certain patients with certain characteristics or needs would respond better to one treatment package versus others. To the extent such <i>aggregate</i> \"Aptitude × Treatment interactions\" emerged, they could help guide overarching treatment selection. More recently, and drawing on technological and statistical advancements (e.g., machine learning, dynamic modeling), predictive algorithms can help determine for which individual patients certain treatment packages (DeRubeis et al., 2014) or specific during-session interventions within them (Fisher & Boswell, 2016) confer the most advantage for clinical improvement. Again, such work can help guide treatment decisions, though now at multiple care points. Although the aforementioned innovations in personalized psychotherapy have been leading-edge, precision care need not remain unidirectional. Rather, it can be complemented by efforts to personalize treatment decisions <i>to the therapist</i>. Namely, we can harness therapist effectiveness data to help ensure that therapists treat the patients they are empirically most equipped to help and use the interventions with which they have had the most empirical success. Such threads have been the focus of our team's novel, evolving, and multimethod work on improving psychotherapy by leveraging therapists' own practice-based evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":15447,"journal":{"name":"Journal of consulting and clinical psychology","volume":"92 6","pages":"327-329"},"PeriodicalIF":4.5000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of consulting and clinical psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/ccp0000897","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Treatment personalization has evolved into an important zeitgeist in psychotherapy research. To date, such efforts have principally embodied a unidirectional focus on personalizing interventions to the patient. For example, earlier work in this area attempted to determine whether, on average, certain patients with certain characteristics or needs would respond better to one treatment package versus others. To the extent such aggregate "Aptitude × Treatment interactions" emerged, they could help guide overarching treatment selection. More recently, and drawing on technological and statistical advancements (e.g., machine learning, dynamic modeling), predictive algorithms can help determine for which individual patients certain treatment packages (DeRubeis et al., 2014) or specific during-session interventions within them (Fisher & Boswell, 2016) confer the most advantage for clinical improvement. Again, such work can help guide treatment decisions, though now at multiple care points. Although the aforementioned innovations in personalized psychotherapy have been leading-edge, precision care need not remain unidirectional. Rather, it can be complemented by efforts to personalize treatment decisions to the therapist. Namely, we can harness therapist effectiveness data to help ensure that therapists treat the patients they are empirically most equipped to help and use the interventions with which they have had the most empirical success. Such threads have been the focus of our team's novel, evolving, and multimethod work on improving psychotherapy by leveraging therapists' own practice-based evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍：
The Journal of Consulting and Clinical Psychology® (JCCP) publishes original contributions on the following topics: the development, validity, and use of techniques of diagnosis and treatment of disordered behaviorstudies of a variety of populations that have clinical interest, including but not limited to medical patients, ethnic minorities, persons with serious mental illness, and community samplesstudies that have a cross-cultural or demographic focus and are of interest for treating behavior disordersstudies of personality and of its assessment and development where these have a clear bearing on problems of clinical dysfunction and treatmentstudies of gender, ethnicity, or sexual orientation that have a clear bearing on diagnosis, assessment, and treatmentstudies of psychosocial aspects of health behaviors. Studies that focus on populations that fall anywhere within the lifespan are considered. JCCP welcomes submissions on treatment and prevention in all areas of clinical and clinical–health psychology and especially on topics that appeal to a broad clinical–scientist and practitioner audience. JCCP encourages the submission of theory–based interventions, studies that investigate mechanisms of change, and studies of the effectiveness of treatments in real-world settings. JCCP recommends that authors of clinical trials pre-register their studies with an appropriate clinical trial registry (e.g., ClinicalTrials.gov, ClinicalTrialsRegister.eu) though both registered and unregistered trials will continue to be considered at this time.
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