Routine Outcome Monitoring Data Research Articles

Mental health of help-seeking outpatients pre and post COVID-19: A real-world data, multicentre study.

A great deal of research addresses the mental health implications of the COVID-19 pandemic for the general population. Little is known about the implications for mental health of help-seeking outpatients and for the effectiveness of mental health services. The present study investigated the mental health and treatment response of help-seeking outpatients before and during the COVID-19 pandemic. Routine outcome monitoring data from 3706 clients in the United States and Northern Europe was analysed using multilevel modelling with global subjective well-being as the dependent variable. As opposed to before the pandemic, during the pandemic, well-being scores were significantly higher at intake and improvement throughout treatment was significantly smaller in the US sample, while both were comparable in the EU sample. Although there is also evidence of less effective treatments since the pandemic, no conclusive picture emerges that portrays the impact of the pandemic on mental health as uniform. More research is needed to elucidate the impact of the pandemic on the help-seeking population.

A systematic review on the effect of routine outcome monitoring and feedback on client outcomes in alcohol and other drug treatment.

Routine outcome monitoring (ROM) involves regularly measuring clients' outcomes during treatment, which can then be fed back to clinicians and/or clients. In the mental health field, ROM and feedback have been shown to improve client outcomes; however, no systematic reviews have examined whether improvement is also seen in alcohol and other drug (AOD) treatment outcomes. This review examines whether feedback to clients and/or clinicians of ROM data in AOD treatment improves future client outcomes. This systematic review of papers identified in Medline, PsycInfo and Scopus examines the effect on client outcomes of feeding back ROM data to clinicians and/or clients in AOD treatment settings. Key client outcomes included substance use, treatment attendance and wellbeing measures. Ten studies were included-five randomised controlled trials and five pre-post within-subjects designs. Six studies were deemed good- or fair-quality. Of these six, three provided feedback to clinicians only, one to clients only, and two to both clients and clinicians. Only one of the six found feedback was associated with significant reductions in substance use and only among off-track clients. Four of the six found feedback improved other outcomes, including treatment retention, global functioning, therapeutic alliance and mood symptoms. There may be some positive effects for clients of providing feedback to clients and/or clinicians; however, the small number of randomised trials and the heterogeneity of methods, outcome measures and findings, mean that firm conclusions cannot be drawn about the efficacy of feedback until larger randomised studies are conducted.

A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders

ABSTRACT Objectives Applying machine-learning methodology to clinical data could present a promising avenue for predicting outcomes in patients receiving treatment for psychiatric disorders. However, preserving privacy when working with patient data remains a critical concern. Methods In showcasing how machine-learning can be used to build a clinically relevant prediction model on clinical data, we apply two commonly used machine-learning algorithms (Random Forest and least absolute shrinkage and selection operator) to routine outcome monitoring data collected from 593 patients with eating disorders to predict absence of reliable improvement 12 months after entering outpatient treatment. Results An RF model trained on data collected at baseline and after three months made 31.3% fewer errors in predicting lack of reliable improvement at 12 months, in comparison with chance. Adding data from a six-month follow-up resulted in only marginal improvements to accuracy. Conclusion We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.

The effects of a computerized clinical decision aid on clinical decision-making in psychosis care

ObjectiveClinicians in mental healthcare have few objective tools to identify and analyze their patient's care needs. Clinical decision aids are tools that support this process. This study examines whether 1) clinicians working with a clinical decision aid (TREAT) discuss more of their patient's care needs compared to usual treatment, and 2) agree on more evidence-based treatment decisions. MethodsClinicians participated in consultations (n = 166) with patients diagnosed with psychotic disorders from four Dutch mental healthcare institutions (research registration number 201700763). Primary outcomes were measured with the modified Clinical Decision-making in Routine Care questionnaire and combined with psychiatric, physical and social wellbeing related care needs. A multilevel analysis compared discussed care needs and evidence-based treatment decisions between treatment as usual (TAU) before, TAU after and the TREAT condition. ResultsFirst, a significant increase in discussed care needs for TREAT compared to both TAU conditions (β = 20.2, SE = 5.2, p = 0.00 and β = 15.8, SE = 5.4, p = 0.01) was found. Next, a significant increase in evidence-based treatments decisions for care needs was observed for TREAT compared to both TAU conditions (β = 16.7, SE = 4.8, p = 0.00 and β = 16.0, SE = 5.1, p = 0.01). ConclusionTREAT improved the discussion about physical health issues and social wellbeing related topics. It also increased evidence-based treatment decisions for care needs which are sometimes overlooked and difficult to treat. Our findings suggest that TREAT makes sense of routine outcome monitoring data and improves guideline-informed care.

Improvement in the quality of life of outpatients with severe mental illness in conjunction with intellectual disabilities and post-traumatic stress disorder.

Elucidating the influence of mild intellectual disability (MID; IQ 50-70)/borderline intellectual functioning (BIF; IQ 70-85) and (comorbid) post-traumatic stress disorder (PTSD) on the quality of life of patients with serious mental illness (SMI) could improve their mental health care. This study comprises a prospective longitudinal cohort study using routine outcome monitoring data. The cohort comprised 601 patients who had undertaken at least one Manchester Short Assessment of Quality of Life (MANSA). The scores for screeners to detect MID/BIF and PTSD were analysed, and a repeated measures analysis of variance and a multi-level linear regression was performed on the MANSA scores. The average quality of life for all patient groups increased significantly over time. A between-subject effect on quality of life was observed for PTSD, but not MID/BIF. PTSD but not MID/BIF is associated with a lesser quality of life over time.

Feasibility of a Mobile Health App for Routine Outcome Monitoring and Feedback in SMART Recovery Mutual Support Groups: Stage 1 Mixed Methods Pilot Study.

BackgroundMutual support groups are an important source of long-term help for people impacted by addictive behaviors. Routine outcome monitoring (ROM) and feedback are yet to be implemented in these settings. SMART Recovery mutual support groups focus on self-empowerment and use evidence-based techniques (eg, motivational and behavioral strategies). Trained facilitators lead all SMART Recovery groups, providing an opportunity to implement ROM.ObjectiveThe aim of this stage 1 pilot study is to explore the feasibility, acceptability, and preliminary outcomes of a novel, purpose-built mobile health ROM and feedback app (SMART Track) in mutual support groups coordinated by SMART Recovery Australia (SRAU) over 8 weeks.MethodsSMART Track was developed during phase 1 of this study using participatory design methods and an iterative development process. During phase 2, 72 SRAU group participants were recruited to a nonrandomized, prospective, single-arm trial of the SMART Track app. Four modes of data collection were used: ROM data directly entered by participants into the app; app data analytics captured by Amplitude Analytics (number of visits, number of unique users, visit duration, time of visit, and user retention); baseline, 2-, and 8-week follow-up assessments conducted through telephone; and qualitative telephone interviews with a convenience sample of study participants (20/72, 28%) and facilitators (n=8).ResultsOf the 72 study participants, 68 (94%) created a SMART Track account, 64 (88%) used SMART Track at least once, and 42 (58%) used the app for more than 5 weeks. During week 1, 83% (60/72) of participants entered ROM data for one or more outcomes, decreasing to 31% (22/72) by the end of 8 weeks. The two main screens designed to provide personal feedback data (Urges screen and Overall Progress screen) were the most frequently visited sections of the app. Qualitative feedback from participants and facilitators supported the acceptability of SMART Track and the need for improved integration into the SRAU groups. Participants reported significant reductions between the baseline and 8- week scores on the Severity of Dependence Scale (mean difference 1.93, SD 3.02; 95% CI 1.12-2.73) and the Kessler Psychological Distress Scale-10 (mean difference 3.96, SD 8.31; 95% CI 1.75-6.17), but no change on the Substance Use Recovery Evaluator (mean difference 0.11, SD 7.97; 95% CI –2.02 to 2.24) was reported.ConclusionsFindings support the feasibility, acceptability, and utility of SMART Track. Given that sustained engagement with mobile health apps is notoriously difficult to achieve, our findings are promising. SMART Track offers a potential solution for ROM and personal feedback, particularly for people with substance use disorders who attend mutual support groups.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12619000686101; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336International Registered Report Identifier (IRRID)RR2-10.2196/15113

Developing a mHealth Routine Outcome Monitoring and Feedback App ("SMART Track") to Support Self-Management of Addictive Behaviours.

Background: Routine outcome monitoring (ROM) has been implemented across a range of addiction treatment services, settings and organisations. Mutual support groups are a notable exception. Innovative solutions are needed. SMART Track is a purpose built smartphone app designed to capture ROM data and provide tailored feedback to adults attending Australian SMART Recovery groups for addictive behaviour(s).Objective: Details regarding the formative stage of app development is essential, but often neglected. Improved consideration of the end-user is vital for curtailing app attrition and enhancing engagement. This paper provides a pragmatic example of how principles embedded in published frameworks can be operationalised to address these priorities during the design and development of the SMART Track app.Methods: Three published frameworks for creating digital health technologies (“Person-Based Approach,” “BIT” Model and IDEAS framework) were integrated and applied across two stages of research to inform the development, design and content of SMART Track. These frameworks were chosen to ensure that SMART Track was informed by the needs and preferences of the end-user (“Person-Based”); best practise recommendations for mHealth development (“BIT” Model) and a collaborative, iterative development process between the multi-disciplinary research team, app developers and end-users (IDEAS framework).Results: Stage one of the research process generated in-depth knowledge to inform app development, including a comprehensive set of aims (clinical, research/organisation, and usage); clear articulation of the target behaviour (self-monitoring of recovery related behaviours and experiences); relevant theory (self-determination and social control); appropriate behavioural strategies (e.g., behaviour change taxonomy and process motivators) and key factors that may influence engagement (e.g., transparency, relevance and trust). These findings were synthesised into guiding principles that were applied during stage two in an iterative approach to app design, content and development.Conclusions: This paper contributes new knowledge on important person-centred and theoretical considerations that underpin a novel ROM and feedback app for people with addictive behaviour(s). Although person-centred design and best-practise recommendations were employed, further research is needed to determine whether this leads to improved usage outcomes.Clinical Trial Registration: Pilot Trial: http://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336.

Psychopathology in a treatment-seeking sample of homicidally bereaved individuals: Latent class analysis

BackgroundViolently bereaved individuals are at increased risk of developing severe and comorbid disorders. Comorbidity may increase psychiatric symptom severity and suicide risk and decrease psychosocial functioning compared with having one disorder. We aimed to identify subgroups of individuals with similar symptom patterns, describe prevalence rates and overall levels of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and generalized anxiety disorder (GAD) per class, and explore associations between class membership and personal and homicide related variables. MethodsWe investigated the comorbidity of symptoms of PGD, PTSD, MDD, and GAD in a sample of 923 treatment-seeking homicidally bereaved individuals by deploying latent class analysis. ResultsThree subgroups were identified: (i) a moderate distress, low depression class (12.4%), (ii) a high distress, moderate depression class (42.7%), and (iii) a high distress and high depression class (45.0%). Prevalence rates and total scores of the questionnaires followed the pattern of iii ≥ ii ≥ i (ps ≤ .001). Being female and having experienced prior life stress distinguished between all classes (ps ≤ .05). Limitations: The data-driven analytic approach and reliance on self-reported routine outcome monitoring data limit the generalizability and validity of the study. Strengths include the large sample size and the inclusion of four measures in a treatment-seeking, violently bereaved sample. ConclusionsClasses were most clearly distinguishable based on symptom severity, indicating high comorbidity following bereavement by homicide. This argues for an integrated treatment that targets different complaints simultaneously rather than successively.

An outpatient clinic for integrative mental health: Patient and treatment characteristics and health outcomes based on patient reported routine outcome monitoring data

IntroductionThere is an increasing interest in integrative (mental) health care and a growth in centers offering such services, but a paucity of research on patient characteristics, diagnosis, treatments offered, the effects of those treatments and patient satisfaction.ObjectivesTo examine the course of mental health outcomes in the context of the nature and quality of care of outpatients at a center for integrative psychiatry in the Netherlands, as well as relevant sociodemographic, clinical, and treatment-related moderators of this course.MethodsBaseline patient demographics, clinical and treatment characteristics of 537 patients with a completed care episode between 2012 and 2019 were assessed. Satisfaction and mental health treatment outcomes were examined using routine outcome monitoring and analyzed with multilevel intention-to-treat models.ResultsTwo thirds of patients were woman (median age 41 years), predominantly with a primary diagnosis of mood or anxiety disorder. Mean number of treatment sessions was 49 (SD=94) and total clinical time was 54 hours (SD=109). Mean treatment duration was 460 days (SD=407). Ninety percent of the sample filled out one or more assessment(s). Of the individuals with a baseline assessment, 50% completed a follow-up. Significant improvements in symptomatology, social functioning, interpersonal functioning, wellbeing, resilience and quality of life were found. Clinical and scientific interpretation, moderator analyses and patient satisfaction will be presented at the conference.ConclusionsAlthough no definite conclusions can be drawn due to the naturalistic design and missing data, especially at follow-up, patients seem to improve on all measured domains, including psychopathology, functioning and wellbeing.

The effectiveness of mindfulness-based cognitive therapy for major depressive disorder: evidence from routine outcome monitoring data.

Meta-analyses show efficacy of mindfulness-based cognitive therapy (MBCT) in terms of relapse prevention and depressive symptom reduction in patients with major depressive disorder (MDD). However, most studies have been conducted in controlled research settings. We aimed to investigate the effectiveness of MBCT in patients with MDD presenting in real-world clinical practice. Moreover, we assessed whether guideline recommendations for MBCT allocation in regard to recurrence and remission status of MDD hold in clinical practice. This study assessed a naturalistic cohort of patients with (recurrent) MDD, either current or in remission (n = 765), who received MBCT in a university hospital out-patient clinic in The Netherlands. Outcome measures were self-reported depressive symptoms, worry, mindfulness skills and self-compassion. Predictors were MDD recurrence and remission status, and clinical and sociodemographic variables. Outcome and predictor analyses were conducted with linear regression. MBCT adherence was high (94%). Patients with a lower level of education had a higher chance of non-adherence. Attending more sessions positively influenced improvement in depressive symptoms. Depressive symptoms significantly reduced from pre- to post-MBCT (Δ mean = 7.7, 95%CI = 7.0-8.5, Cohen's d = 0.75). Improvement of depressive symptoms was independent from MDD recurrence and remission status. Unemployed patients showed less favourable outcomes. Worry, mindfulness skills and self-compassion all significantly improved. These improvements were related to changes in depressive symptoms. Previous efficacy results in controlled research settings are maintained in clinical practice. Results illustrate that MBCT is effective in routine clinical practice for patients suffering from MDD, irrespective of MDD recurrence and remission status.

The clinical effectiveness of an algorithm-guided treatment program for depression in specialized mental healthcare: A comparison with efficacy trials

BackgroundDoubts exist on whether effects found in randomized controlled trials (RCTs) are directly generalizable to daily clinical practice. This study aimed (a) to investigate the effectiveness of treatment options within an algorithm-guided treatment (AGT) program for depression and compare their effectiveness with outcomes of efficacy trials and (b) to assess the relation between treatment continuity and outcomes. MethodsThis naturalistic study linked treatment data from January 2012 to November 2014 from a Dutch mental healthcare provider, to routine outcome monitoring (ROM) data (N = 351). Effectiveness of the treatment options (pharmacotherapy, psychotherapy and their combination) was compared to the efficacy reported in the meta-analyses. We included treatment continuity as binary variable “early terminators versus completers of the recommended number of treatment sessions”. ResultsRemission rates for psychotherapy (38% [95% CI: 32–45]), pharmacotherapy (31% [95% CI: 22–42]) and combination therapy (46% [95% CI: 19–75]) were respectively lower, comparable, and comparable to those reported in the meta-analyses. Similarly, response rates were respectively lower (24% [95% CI: 19–30]), lower (21% [95% CI: 13–31]), and comparable (46% [95% CI: 19–75]) to meta-analyses results. A similar share of early terminators and completers achieved remission and response. LimitationsA substantial proportion of patients had incomplete ROM data after data linkage. Limited set of patient characteristics to check for selection bias. ConclusionsDespite the more heterogeneous patient population in clinical practice, the effectiveness of an AGT program, emphasizing strict guideline adherence, approached that found in RCTs. A fixed number of treatment sessions may not suit all individual patients.

Feasibility of a Mobile Health App for Routine Outcome Monitoring and Feedback in Mutual Support Groups Coordinated by SMART Recovery Australia: Protocol for a Pilot Study

BackgroundDespite the importance and popularity of mutual support groups, there have been no systematic attempts to implement and evaluate routine outcome monitoring (ROM) in these settings. Unlike other mutual support groups for addiction, trained facilitators lead all Self-Management and Recovery Training (SMART Recovery) groups, thereby providing an opportunity to implement ROM as a routine component of SMART Recovery groups.ObjectiveThis study protocol aims to describe a stage 1 pilot study designed to explore the feasibility and acceptability of a novel, purpose-built mobile health (mHealth) ROM and feedback app (Smart Track) in SMART Recovery groups coordinated by SMART Recovery Australia (SRAU) The secondary objectives are to describe Smart Track usage patterns, explore psychometric properties of the ROM items (ie, internal reliability and convergent and divergent validity), and provide preliminary evidence for participant reported outcomes (such as alcohol and other drug use, self-reported recovery, and mental health).MethodsParticipants (n=100) from the SMART Recovery groups across New South Wales, Australia, will be recruited to a nonrandomized, prospective, single-arm trial of the Smart Track app. There are 4 modes of data collection: (1) ROM data collected from group participants via the Smart Track app, (2) data analytics summarizing user interactions with Smart Track, (3) quantitative interview and survey data of group participants (baseline, 2-week follow-up, and 2-month follow-up), and (4) qualitative interviews with group participants (n=20) and facilitators (n=10). Feasibility and acceptability (primary objectives) will be analyzed using descriptive statistics, a cost analysis, and a qualitative evaluation.ResultsAt the time of submission, 13 sites (25 groups per week) had agreed to be involved. Funding was awarded on August 14, 2017, and ethics approval was granted on April 26, 2018 (HREC/18/WGONG/34; 2018/099). Enrollment is due to commence in July 2019. Data collection is due to be finalized in October 2019.ConclusionsTo the best of our knowledge, this study is the first to use ROM and tailored feedback within a mutual support group setting for addictive behaviors. Our study design will provide an opportunity to identify the acceptability of a novel mHealth ROM and feedback app within this setting and provide detailed information on what factors promote or hinder ROM usage within this context. This project aims to offer a new tool, should Smart Track prove feasible and acceptable, that service providers, policy makers, and researchers could use in the future to understand the impact of SMART Recovery groups.Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12619000686101; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336.International Registered Report Identifier (IRRID)PRR1-10.2196/15113

Symptomatic and Functional Remission in Young Adults with a Psychotic Disorder in a Rehabilitation Focused Team

The aim of this study is to assess symptomatic remission (SR) and functional remission (FR) in a rehabilitation focused program for young adults with a psychotic disorder in the Netherlands, and to investigate which individual and mental health care factors are associated with SR and/or FR, by using Routine Outcome Monitoring data and data on met needs and unmet needs for care. Data of 287 young adults were collected. Almost 40% achieved or maintained SR, 34% FR, and 26% achieved or maintained both. In addition to sociodemographic factors, living independently, paid employment, higher levels of compliance with treatment, and better fulfillment of unmet needs for care in relation to psychological distress, company and daytime activities were associated with better outcomes on SR and/or FR. Our findings underscore that to successfully improve and sustain remission in young adults with a psychotic disorder, it is needed to conduct specific research into the relationship between SR and FR.

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data.

BackgroundObservational research based on routine outcome monitoring is prone to missing data, and outcomes can be biased due to selective inclusion at baseline or selective attrition at posttest. As patients with complete data may not be representative of all patients of a provider, missing data may bias results, especially when missingness is not random but systematic.MethodsThe present study establishes clinical and demographic patient variables relevant for representativeness of the outcome information. It applies strategies to estimate sample selection bias (weighting by inclusion propensity) and selective attrition bias (multiple imputation based on multilevel regression analysis) and estimates the extent of their impact on an index of provider performance. The association between estimated bias and response rate is also investigated.ResultsProvider‐based analyses showed that in current practice, the effect of selective inclusion was minimal, but attrition had a more substantial effect, biasing results in both directions: overstating and understating performance. For 22% of the providers, attrition bias was estimated to be in excess of 0.05 ES. Bias was associated with overall response rate (r = .50). When selective inclusion and attrition bring providers' response below 50%, it is more likely that selection bias increased beyond a critical level, and conclusions on the comparative performance of such providers may be misleading.ConclusionsEstimates of provider performance were biased by selection, especially by missing data at posttest. Results on the extent and direction of bias and minimal requirements for response rates to arrive at unbiased performance indicators are discussed.

The Assessment and Impact of Careless Responding in Routine Outcome Monitoring within Mental Health Care

Careless responding by mental health patients on self-report assessments is rarely investigated in routine care despite the potential for serious consequences such as faulty clinical decisions. We investigated validity indices most appropriate for detecting careless responding in routine outcome monitoring (ROM) in mental health-care. First, we reviewed indices proposed in previous research for their suitability in ROM. Next, we evaluated six selected indices using data of the Brief Symptom Inventory and the Mood and Anxiety Symptom Questionnaire from 3,483 outpatients. Simulations showed that for typical ROM scales the index, Mahalanobis distance, and inter-item standard deviation may be too strongly confounded with the latent trait value to compare careless responding across patients with different symptom severity. Application of two different classification methods to the validity indices did not converge in similar prevalence estimates of careless responding. Finally, results suggest that careless responding does not have a substantial biasing effect on scale-score statistics. We recommend the person-fit index to screen for random careless responding in large ROM data sets. However, additional research should further investigate methods for detecting repetitive responding in typical ROM data and assess whether there are specific circumstances in which simpler validity statistics or direct screening methods perform similarly as the index.

Trajectories of patients with severe mental illness in two-year contact with Flexible Assertive Community Treatment teams using Routine Outcome Monitoring data: An observational study.

ObjectiveUsing outcome data collected routinely over a continuous two-year treatment period, we wished to distinguish homogeneous subgroups of patients with a severe mental illness whose psychosocial problems followed a similar pattern over time. By identifying the effectiveness of health services for different patient groups, this approach allowed us to identify patients at risk of deterioration and those recovering from their symptoms.MethodsIn total we included 2,660 patients who were in two-year continuous contact with a Flexible Assertive Community Treatment team (FACT). We collected outcome data on psychosocial functioning, needs for care and quality of life. We performed a latent class growth analysis (LCGA).ResultsThe LCGA identified six homogenous patient subgroups using trajectories of HoNOS scores. On the basis of the patterns of patients’ psychosocial problems over time, we labelled these as follows: 1) stable at a low problem-severity level (N = 709; 27%); 2) stable at a low medium problem-severity level (N = 1,208; 45%); 3) stable at a high medium problem-severity level (N = 528; 20%); 4) stable at a high problem-severity level (N = 116; 4%); 5) amelioration of problems (N = 42; 2%); and 6) deterioration of problems (N = 57; 2%). Patients with stable and a high severity of psychosocial problems had more practical and somatic unmet needs than those in other subgroups, and also had the fewest decrease in the number of unmet needs.DiscussionAfter linking patient subgroups with clinical features such as the need for care, we found that, over two years, most patients remained relatively stable in terms of psychosocial functioning, but that their unmet needs decreased over time. However, in terms of needs for treatment during two years of contact with a FACT team, patients in the subgroup with a stable and high problem-severity level tended to derive little or no benefit.

Treatment course and its predictors in patients with somatoform disorders: A routine outcome monitoring study in secondary psychiatric care.

Somatoform disorders are common and often chronic. It would be helpful to distinguish those patients who are likely to have a positive treatment course from those who are likely to follow a negative course. Such studies of different somatoform disorders are scarce, especially in secondary psychiatric care. This study examined the 6-month treatment course of psychological, physical symptoms, and functioning, and its predictors in a naturalistic sample of secondary psychiatric care outpatients with somatoform disorders. The present study used routine outcome monitoring data of patients with somatoform disorders regarding their 6-month treatment course of psychological and physical symptoms as well as functioning. The following patient groups were included: total group of somatoform disorders (N=435), and undifferentiated somatoform disorder (N=242), pain disorder (N=102), body dysmorphic disorder (N=51), and hypochondriasis (N=40). Measures were Mini-International Neuropsychiatric Interview plus, Brief Symptom Inventory, Montgomery-Ǻsberg Depression Rating Scale, Brief Anxiety Scale, Short Form Health Survey 36, and Physical Symptom Checklist (PSC). The study population generally showed high co-morbidity, especially with anxiety and mood disorders. The PSC total score, body dysmorphic disorder, and hypochondriasis were significant predictors for the treatment course of symptoms (Brief Symptom Inventory), whereas the PSC total score was the only significant predictor for the course of functioning (Short Form Health Survey 36). Secondary psychiatric care outpatients with somatoform disorders showed high co-morbidity with anxiety and mood disorders, and an unfavourable 6-month course of both symptoms and functioning. Clinical implications are discussed, such as additional treatment of co-morbidity in somatoform disorders.

Provider-Associated Measurement Error in Routine Outcome Monitoring in Community Mental Health.

Despite the strengths of routine outcome monitoring (ROM) in community mental health settings, there are a number of barriers to effective implementation of ROM, including measurement error due to provider factors (e.g., training level) and non-target client factors (i.e., client characteristics which have no meaningful relationship to the outcome of interest). In this study, ROM data from 80 client-provider dyads were examined for sources of variance due to provider factors and non-target client factors. Results indicated that provider factors and non-target client factors accounted for between 9.6 and 54% of the variance in the ROM measures. Our findings supported past research that provider characteristics impact ROM, and added the novel finding that client gender, age, diagnosis, and cognition also impact ROM. Methods to increase accuracy and utility of ROM in community mental health are discussed.

Routine Outcome Monitoring in CAMHS: How Can We Enable Implementation in Practice?

Many CAMHS teams across the UK are now required to use Routine Outcome Monitoring (ROM). However, some clinicians hold negative attitudes towards ROM and various practical implementation issues have been identified. The aim of this study was to explore clinician experiences of using ROM in the context of an 'enforced' initial implementation initiative. Twenty clinicians were surveyed at the beginning (T1) and end (T2) of the six-month period in a large UK CAMHS network adopting CYP-IAPT practice. Changes in the use of and attitudes towards ROM were investigated, as well as barriers to implementation. Overall, a small but significant increase in clinician use of ROM was observed from T1 to T2, but attitudes towards ROM did not change significantly. On the whole, clinicians were more positive than negative about ROM during the implementation period, but key implementation challenges included clinician concerns about the value and (mis)use of ROM data, poor technological support and additional workload demands. CAMHS clinicians will not necessarily become more receptive to ROM simply as a by-product of being asked to use it more. Seeking clinician feedback at the early stages of implementation can help provide a focus for improvement efforts. Ideas for future research and important limitations of the study are discussed.

Risk adjustment of self-reported clinical outcomes in Dutch mental health care

Aim A meaningful comparison of clinical outcomes of mental health care providers requires risk adjustment of case mix variables that affect treatment outcome. The first goal of the present study was to describe the risk adjustment method we applied to routine outcome monitoring data in The Netherlands. Second, the consequences of risk adjustment for the ranking of providers were investigated.