Administration and Policy in Mental Health and Mental Health Services Research最新文献

Based on patient-reported outcomes data analyzed at the provider level, there is evidence that psychotherapists can possess effectiveness strengths and weaknesses when treating patients with different presenting concerns. These within-therapist differences hold promise for personalizing care by prospectively matching patients to therapists’ historical effectiveness strengths. In a double-masked randomized controlled trial (RCT; NCT02990000), such matching outperformed pragmatically determined usual case assignment—which leaves personalized, measurement-based matching to chance—in naturalistic outpatient psychotherapy (Constantino et al., JAMA Psychiatry 78:960–969, 2021). Demonstrating that personalization can be even more precise, some research has demonstrated that the strength of this positive match effect was moderated by certain patient characteristics. Notably, though, it could also be that matching is especially important for some therapists to achieve more effective outcomes. Examining this novel question, the present study drew on the Constantino et al. (JAMA Psychiatry 78:960–969, 2021) trial data to explore three therapist-level moderators of matching: (a) effectiveness “spread” (i.e., greater performance variability across patients’ presenting problem domains), (b) overestimation of their measurement-based and problem-specific effectiveness, and (c) the frequency with which they use patient-reported routine outcomes monitoring in their practice. Patients were 206 adults, randomized to the match or control condition, treated by 40 therapists who were crossed over conditions. The therapist variables were assessed at the trial’s baseline and patients’ symptomatic/functional impairment and global distress were assessed regularly up to 16 weeks of treatment. Hierarchical linear models revealed that only therapist effectiveness spread significantly moderated the match effect for the global distress outcome; for therapists with more spread, the match effect was more pronounced, whereas the match effect was minimal for therapists with less effectiveness spread. Notably, two therapist-level covariates unexpectedly emerged as significant moderators for the symptomatic/functional impairment outcome; for clinicians who consistently treated patients with higher versus lower average severity levels and who relatedly treated a higher proportion of patients with primary presenting problems of substance misuse or violence, the beneficial match effect was even stronger. Thus, measurement-based matching may be especially potent for therapists with more variable effectiveness across problem domains, and who consistently treat patients with more severe presenting concerns or with particular primary problems, which provides further precision in conceptualizing personalized care.

We aim to use topic modeling, an approach for discovering clusters of related words (“topics”), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ((r)=0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ((r)=0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

Ecological momentary assessment (EMA) allows measuring intra-individual processes moment by moment, identifying and modeling, in a naturalistic way, individual levels and changes in different psychological processes. However, active EMA requires a high degree of adherence, as it implies a significant burden for patients. Moreover, there is still no consensus on standardized procedures for implementation. There have been few results in detecting desirable characteristics for the design and implementation of an EMA device. Studies that address these issues from the perspectives of participants in psychotherapeutic processes are needed. To analyze the perspectives of patients, therapists and supervisors on the implementation of an EMA device in a psychotherapeutic treatment for depression. The sample will include eight patients, eleven therapists and five supervisors, taken from a research project that implemented an EMA system for monitoring the dynamics of affectivity at the beginning of psychotherapies for depression. Semi-structured interviews specific to each group are being conducted and analyzed from a qualitative approach based on consensual qualitative research (CQR). Participants reported having a positive evaluation of the study's informational resources and implementation. Difficulties were expressed in responding in the morning hours and the importance of having a customized EMA that is tailored to the needs of the patients was expressed. Furthermore, patients and therapists agreed that the impact of the use of the monitoring system on treatment was neutral or positive. In contrast, patients considered the EMA to be positive for their daily life.

Disruptive behavior of patients in acute psychiatric care is a problem for both patients and staff. Preventing a patient’s impending disruption requires recognizing and understanding early signals. There are indications that a change in a patient’s global functioning may be such a signal. The global functioning of patients is a multidimensional view on their functioning. It captures a patient’s psychological symptoms, social skills, symptoms of violence, and activities in daily living. The aim of this study was to gain insight into the predictive value of global functioning on the risk of disruptive behavior of patients in acute psychiatric care. Also assessed was the time elapsed between the change in global functioning and a patient’s disruptive behavior, which is necessary to know for purposes of early intervention. In a longitudinal retrospective study, we used daily measurements with the Brøset Violence Checklist (BVC) and the Kennedy Axis V (K-As) of each patient admitted to two acute psychiatric units over a period of six years. Data from 931 patients for the first 28 days after their admission were used for survival analysis and cox regression analysis. Disruptive behavior was mostly observed during the first days of hospitalization. Global functioning predicted disruptive behavior from the very first day of hospitalization. A cut-off score of 48 or lower on the K-As on the first admission day predicted a higher risk of disruptive behavior. If functioning remained poor or deteriorated substantially over three days, this was an additional signal of increased risk of disruptive behavior. Improvement in global functioning was associated with a decreased risk of disruptive behavior. More attention is needed for early interventions on global functioning to prevent disruptive behavior.

The mental health treatment gap remains wide across the world despite mental illness being a significant cause of disability globally. Both end-user and healthcare provider perspectives are critical to understanding barriers to mental healthcare and developing interventions. However, the views of providers are relatively understudied. In this review, we synthesized findings from current literature regarding providers’ perspectives on barriers to mental healthcare in Canada. We searched Medline, PsycINFO, Embase, and CINAHL for eligible Canadian studies published since 2000. Analysis and quality assessment were conducted on the included studies. Of 4,773 reports screened, 29 moderate-high quality studies were reviewed. Five themes of barriers emerged: health systems availability and complexity (reported in 72% of the studies), work conditions (55%), training/education (52%), patient accessibility (41%), and identity-based sensitivity (17%). Common barriers included lack of resources, fragmented services, and gaps in continuing education. Interestingly, clinicians often cited confusion in determining the ideal service for patients due to an overwhelming number of potential services without clear descriptions. These five domains of barriers present a synthesized review of areas of improvement for mental healthcare spanning both patients and clinicians. Canadian mental health systems face a need to improve capacity, clinician training, and in particular service navigability and collaboration.

Health information exchange (HIE) is an effective way to coordinate care, but HIE between health and behavioral health providers is limited. Recent delivery reform models, including the Accountable Care Organization (ACO) and Patient Centered Medical Home (PCMH) prioritize interprofessional collaboration, but little is known about their impact on behavioral health HIE. This study explores whether delivery reform participation affects behavioral health HIE among ambulatory health providers using pooled 2015–2019 data from the National Electronic Health Record Survey, a nationally representative survey of ambulatory physicians’ technology use (n = 8,703). The independent variable in this analysis was provider participation in ACO, PCMH, Hybrid ACO-PCMH, or standard care. The dependent variable was HIE with behavioral health providers. Chi square analysis estimated unweighted rates of behavioral health HIE across reform models. Logistic regression estimated the impact of delivery reform participation on rates of behavioral health HIE. Unweighted estimates indicated that Hybrid ACO-PCMH providers had the highest rates of HIE (n = 330, 33%). In the fully adjust model, rates of HIE were higher among ACO (AOR = 2.66, p < .01), PCMH (AOR = 4.73, p < .001) and Hybrid ACO-PCMH participants (AOR = 5.55, p < .001) compared to standard care, but they did not significantly vary between delivery models. Physicians infrequently engage in HIE with behavioral health providers. Compared to standard care, higher rates of HIE were found across all models of delivery reform. More work is needed to identify common elements of delivery reform models that are most effective in supporting this behavior

Previous studies have shown that therapists’ performance varies, known as therapist effects, and have indicated that therapists who excel in one treatment outcome may not necessarily be effective in other outcomes. This observational naturalistic study aimed to enhance our understanding of therapist effects and the assessment of therapists’ performance in different areas. The study included 68 therapists and 5,582 clients from a large mental health facility. Information about their learning activities was available for a subsample of 49 therapists. Separate multilevel analyses were conducted for treatment outcomes, including case mix-corrected OQ-45 change scores, dropout rates, referrals to other facilities, treatment duration, and client satisfaction ratings. A hierarchical cluster analysis was performed to identify groups of therapists based on their performance across various treatment outcomes. Additionally, differences in therapist characteristics among the clusters identified were examined. Therapist effects varied across different outcomes, ranging from small (2.6% for OQ-45 change) to moderate (6.5% for number of sessions). The cluster analysis revealed four distinct clusters of therapists with specific profiles. They had performance differences in certain areas but not in others. This exploratory study supports the notion that therapists exhibit diverse profiles regarding treatment outcomes. These findings are significant for future investigations of therapist effects that aim to identify the characteristics of effective therapists and in the context of personalizing treatment for clients.

Routine outcome monitoring (ROM) has become an increasingly utilized tool in therapeutic practice that has the potential to improve therapy outcomes. This study aimed to synthesize the findings of existing qualitative studies investigating how clinicians use ROM in their work with clients. A systematic search of qualitative studies on clinicians’ experience with the use of ROM in mental health services was conducted via PsycInfo, PsycArticles, Medline, Web of Science, and Scopus databases. Qualitative meta-analysis was used to synthesize the finding of the primary studies. Forty-seven studies met the inclusion criteria. The analysis resulted in 21 meta-categories organized into six clusters, namely (1) obtaining clinically relevant information, (2) adapting treatment, (3) facilitating communication, (4) enhancing the therapeutic relationship, (5) facilitating change in clients, and (6) personalized usage of ROM. The meta-analysis revealed that clinicians utilized ROM in diverse ways, including both informational and communicational functions. From the clinicians’ perspective, ROM was an element that, on the one hand, introduced additional structure and standardization in treatment and, on the other hand, allowed for greater flexibility and tailoring of treatment.