Patients’ experiences with routine outcome monitoring and clinical feedback systems: A systematic review and synthesis of qualitative empirical literature

Solstad, Stig Magne; Castonguay, Louis G.; Moltu, Christian

doi:10.1080/10503307.2017.1326645

Cited by 65 publications

(67 citation statements)

References 54 publications

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“…Factors that can undermine engagement with ROM and feedback include the "time burden" associated with completing, scoring, interpreting or discussing outcome assessments (28,30). Scepticism regarding the perceived relevance of the outcome(s) assessed and/or feedback generated has also been noted (31,32). Within addiction services, client engagement, retention and follow-up are well-documented challenges (33), therefore the introduction of ROM instruments must be brief and the turn-around of feedback rapid (13).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…In fact, there is growing evidence that many clinicians do not use feedback systems even when they are implemented at the clinic (Ionita & Fitzpatrick, 2014;Jensen-Doss et al, 2018). Challenges to implementing feedback systems probably depend on a mix of factors ranging from organizational to attitudinal issues (Boswell, 2020;Hovland & Moltu, 2019;Solstad, Castonguay, & Moltu, 2019). However, a contributing factor might be that these systems must detect information that would not have been apparent to the therapist in order to add value for them.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

Looking into the Future : How to Use Advanced Statistical Methods for Predicting Psychotherapy Outcomes in Routine Care

Uckelstam

2021

Linköping Studies in Behavioural Science

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Background: Psychotherapy research has shifted from mainly focusing on the average effects of different treatments to concentrating more on questions related to the individual patient. This aligns with the goals of precision medicine and patient-focused research and might include studying predictors to forecast important patient behaviors, such as premature termination or expected change rates.When research attention shifts, it often gives rise to the implementation of new statistical methods that, in turn, can illuminate new challenges that must be addressed. For example, the fields of classical statistics and data science have merged in interesting ways in recent times, which has led to an expansion of the methodological toolbox available for psychotherapy research. However, some strengths and limitations of these new approaches should be recognized as they come from slightly different research traditions. For instance, the broader goals of scientific exploration, such as description, prediction, and explanation, might be addressed and given different importance under these approaches.Studying these matters in the context of routine care leads to specific considerations that must be reflected upon, such as how to define relevant outcomes and draw sound conclusions from observational data collected in a naturalistic setting.Aim: The aim of the thesis was to concretize these matters by contrasting how traditional methods for predicting certain psychotherapy outcomes have been studied in the past, and how more advanced statistical methods might be used to enhance knowledge of how to predict these outcomes today.Studies/Results: Three studies were performed: Paper I focused on how Multi Level Modeling (MLM) can be used to study aspects of the relationship between working alliance and treatment outcome that has been overlooked by most earlier studies. The classical way of studying this question has been by using simple correlation analysis to explore the association between patient-rated alliance and treatment outcome. However, with the help of MLM and ratings from both parties of the therapeutic dyad, it is possible to analyze how these ratings relate to each other at different levels in the data (i.e., from patient to patient within the same therapist, denoted within therapist herein, and from therapist to therapist, denoted between therapists herein). MLM also makes it possible to explore the degree of correlation between working alliance and treatment outcome when studied between compared to within therapists, which might have importance for how to direct further studies of this association.

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“…Our findings also have implications for practice and research. In recent years, there has been increased interest in the use of systematic feedback in treatment (e.g., Overington, Fitzpatrick, Drapeau, & Hunsely, ; Solstad, Castonguay, & Moltu, ). There are a number of potential clinical benefits of using feedback systems, such as improved treatment outcomes (Reese, Norsworthy, & Rowlands, ), reduced numbers of clients who worsen during treatment (Lambert & Shimokawa, ), and increased ability for clients to express difficult or shameful issues (Youn, Kraus, & Castonguay, ).…”

Section: Discussionmentioning

confidence: 99%

Client descriptions of outcomes compared with quantitative data: A mixed‐methods investigation of a quantitative outcome measure

Leibert

Powell

Fonseca

2019

Couns and Psychother Res

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Aim In this mixed‐methods study, we examined client descriptions of counselling outcomes and how they corresponded with a quantitative measure of change (i.e., Outcome Questionnaire‐45; OQ‐45). Method Using purposeful sampling at a university counselling training centre, we compared 17 clients who had reliably improved, deteriorated or did not change to qualitative outcomes based on post‐counselling interviews using the Change Interview protocol. Results Qualitative outcomes included two Central change categories, the first of which included interpersonal, cognitive and behavioural, affective, and attitude changes. The second Central category of outcomes comprised four areas of change that varied by degree of change expressed (i.e., Problem Resolution, Confidence, Happiness and Changes Affirmed by Others). Congruence was not present between OQ‐45 and Central 1 changes but was present for three of the four Central 2 changes (Problem Resolution, Happiness and Changes Affirmed by Others), all correlating above .60. Conclusion OQ‐45 change scores showed congruence with three qualitative outcomes: degree of expressed improvement in Happiness, Problem resolution and changes noticed by significant others. However, the lack of congruence for Central 1 changes (i.e., Interpersonal, Cognitive and Behavioural, Affective, and Attitude Change) suggests a diverse range of outcomes important to clients that go undetected using the OQ‐45. We strongly encourage further mixed design research to better understand how best to appraise clinical improvement.

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Patients’ experiences with routine outcome monitoring and clinical feedback systems: A systematic review and synthesis of qualitative empirical literature

Cited by 65 publications

References 54 publications

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

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

Looking into the Future : How to Use Advanced Statistical Methods for Predicting Psychotherapy Outcomes in Routine Care

Client descriptions of outcomes compared with quantitative data: A mixed‐methods investigation of a quantitative outcome measure

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