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Tailoring treatment to a patient's unique circumstances and needs is a central tenet of mental health care. In routine practice, this tailoring process is most often based on clinical theory and intuition, with known biases such as confirmation bias (i.e., the tendency to interpret information in a way that supports one's prior beliefs) and the use of heuristics (such as representativeness heuristic, i.e. estimating the probability of an event by comparing it to a stereotype that one already has in mind), which limits its reliability. A new research paradigm has emerged in the last decade, aiming to achieve evidence-based personalization and data-informed decision making1. The core idea is that it is possible to predict a patient's treatment outcome by observing how other similar patients have responded to this treatment in the past. This approach, therefore, involves collecting comprehensive data from patients at the beginning and throughout treatment to support personalized treatment selection and continuous adaptation. Before treatment begins, multidomain patient data can be used to inform treatment selection. Using algorithms from the field of machine learning, which can analyze complex and non-linear relationships in big data, it is possible to generate predictions about a patient's probable treatment outcome. If outcome predictions are available for different treatment approaches or strategies, the most promising approach for an individual patient can be recommended. Data sources that are commonly used for this endeavor include sociodemographic and psychopathological variables, personality traits, and sometimes digital phenotypes2. In recent years, prediction algorithms have also focused on matching patients to therapists, and selecting specific treatment strategies and target processes in the context of modularized and personalized therapies1. Several model-development studies have emerged in the field of psychotherapy. These studies typically develop clinical prediction algorithms using cross-validation methods which involve partitioning clinical datasets into training, validation and test samples to develop, fine-tune and evaluate the generalizability of these models2. A recent meta-analysis of clinical trials that apply clinical prediction models indicates that algorithm-driven personalized psychological interventions are more effective than standard psychological treatments3. However, despite such promising results, the practical implementation and generalizability of such prediction algorithms is still a matter of investigation3. Only a few studies have attempted to validate treatment selection algorithms in statistically independent external datasets, and these attempts have yet to be convincing4. Important challenges include understanding the necessary sample sizes and data sources (i.e., number and types of variables) required to optimize prediction accuracy, and to identify and correct potential biases. Therefore, despite the advanced statistical approaches applying corrections against overfitting (such as shrinkage and cross-validation), the generalizability to new data and samples as well as real-life implementation require further refinement and testing. To demonstrate a prediction algorithm's external validity, prospective studies must be conducted in which trained prediction models are applied to new incoming patients, which are then treated with the recommended strategy to test whether data- and algorithm-informed treatment selection can actually improve outcomes. Only a few of these prospective studies have been conducted to date, with promising results indicating that data-informed treatment selection improves clinical outcomes by comparison to usual psychological care1, 5. Furthermore, a shift towards personalization of psychological interventions requires trial designs that allow for a better understanding of variance at the individual level, for example pragmatic or adaptive trials. Pragmatic trials allow a better understanding of the real-world effects of data-informed psychological therapy by assessing the effectiveness of personalized systems within naturalistic, heterogeneous routine care settings. Adaptive trials (e.g., sequential multiple assignment randomized trials, SMART) use multiple treatment groups and interim outcome assessments, which are utilized as triggers to start or terminate specific subtrials. Such subtrials are integrated in the adaptive trial framework to simultaneously evaluate different interventions and reduce required sample sizes in multi-intervention studies6. Besides the need for suitable designs to test treatment selection capabilities, new data layers and assessment strategies at the beginning of as well as during treatment have also recently been applied to further refine and improve the personalization of psychological therapies. For example, routine outcome monitoring in combination with feedback has been a key component of evidence-based treatment personalization, particularly for patients at risk of adverse outcomes. Algorithm-based visual feedback is provided to therapists (and patients) to guide clinical decision-making during treatment6. This is especially important as long as predictive treatment variables and recommendations are limited. Strong empirical support from multiple meta-analyses confirms outcome monitoring and feedback to be an effective, resource-efficient enhancement to psychological therapy7. Such personalization during treatment allows therapists to identify patient-specific trends, deviations, or stagnation in patient progress. If significant deviations occur, alerts are generated that signal a potential risk of treatment failure. This enables early adaptations, especially when combined with personalized clinical recommendations that guide therapists in overcoming specific challenges. Most recently, to advance the personalization and precision of psychological therapies, treatment selection and monitoring concepts have been combined in comprehensive decision-support systems1. Such systems include treatment strategy recommendations at the beginning of the intervention, and treatment progress recommendations to facilitate adaptive clinical decisions throughout treatment. A recent randomized clinical trial that prospectively evaluated such a system in a large outpatient sample found that outcomes improved when therapists followed the recommended treatment strategy, and that therapist-rated usefulness of the recommendations moderated the decision support system's effect. The combination of accurate treatment selection and monitoring led to improved outcomes, particularly for therapists who found it useful, suggesting that the use of such systems requires adequate therapist training6. Despite these positive developments, there is a need to further improve measurement and to address implementation and generalizability issues to make treatment personalization more feasible in clinical practice. For example, integrating novel assessment methods such as ecological momentary assessments (including behavioral markers such as physical activity levels, sleep patterns, or physiological signals) and video or audio recordings, and evaluating these novel data layers with time series analysis, emotion detection algorithms, and large language models (LLMs) holds considerable potential to enhance measurement precision1. For example, speech-to-text models enable the automatic transcription of session recordings, providing a rich data source for analysis. LLMs, with their advanced natural language processing capabilities, can process these transcripts at scale and extract linguistic and/or psychological markers to predict outcome or dropout8. The key advantages of these technologies include the reduction of retrospective biases, the identification of micro patterns and subtle therapy processes, and the integration of multimodal data sources. For example, rather than receiving generic intervention recommendations, therapists could receive personalized insights through LLMs that highlight the most relevant treatment adjustments based on the patient's expressed needs. However, the validity and reliability of these new measures and the best practices to implement them need further investigation, including the trustworthiness and interpretability of outputs9. Overall, we can be optimistic about the opportunities and benefits associated with advances in the personalization of psychological interventions, especially given the new technological possibilities. There is great potential in developing this paradigm through the analysis of big data from electronic health records, especially in heterogeneous populations that are typical of routine care. Infrastructure must be established for research centers to implement and test standard and novel assessment tools and personalization options with trained therapists in diverse samples. However, further developments require suitable designs and larger and more diverse databases.
Lutz et al. (Mon,) studied this question.