Improving measurement feedback systems for measurement-based care

Mental Health Information Reporting Assistant (MHIRA)-an open-source software facilitating evidence-based assessment for clinical services

Evidence-based assessment (EBA) in mental health is a critical aspect of improving patient outcomes and addressing the gaps in mental health care. EBA involves the use of psychometric instruments to gather data that can inform clinical decision-making, inform policymakers, and serve as a basis for research and quality management. Despite its potential, EBA is often hindered by barriers such as workload and cost, leading to its underutilization. Regarding low- and middle-income countries (LMIC), the implementation of EBA is recognized as a key strategy to address and close the prevalent mental health treatment gap.To simplify the application of EBA including in LMIC, an international team of researchers and practitioners from Tanzania, Kosovo, Chile, and Switzerland developed the Mental Health Information Reporting Assistant (MHIRA). MHIRA is an open-source electronic health record that streamlines EBA by digitising psychometric instruments and organising patient data in a user-friendly manner. It provides immediate and convenient reports to inform clinical decision-making.The current article provides a comprehensive overview of the features and technical details of MHIRA, as well as insights from four implementation scenarios. The experience gained during the implementations as well as the user-feedback suggests that MHIRA has the potential to be successfully implemented in a variety of clinical contexts and simplify the use of EBA. However, further research is necessary to establish its potential to sustainably transform healthcare services and impact patient outcomes.In conclusion, MHIRA represents an important step in promoting the widespread adoption of EBA in mental health. It offers a promising solution to the barriers that have limited the use of EBA in the past and holds the potential to improve patient outcomes and support the ongoing efforts to address gaps in mental health care.

Common procedures of remote measurement-based care in an integrated behavioural health context: protocol for a scoping review

INTRODUCTION

Integrated behavioural health, a model of care that embeds mental health services in primary care, can potentially increase access to mental healthcare. With the increase in health information technologies, remote measurement-based care (RMBC) presents an opportunity to improve support of integrated care. This scoping review will comprehensively examine what common procedures are followed when RMBC for mental health is tested in integrated care settings.

METHODS AND ANALYSIS

Based on an established six-step framework for conducting scoping reviews, we will search PubMed, Embase, PsycINFO, Cochrane, EBSCOhost and Web of Science with search terms related to 'integrated care' and 'RMBC'. Articles published from 2015 onwards, in English, including an intervention that meets our definition of RMBC, and are conducted in collaboration with primary care or in a primary care setting will be included. After data extraction, we will categorise key findings along the following dimensions: (1) common delivery practices of RMBC; (2) common technologies and instruments used and (3) most common barriers and facilitators when implementing RMBC in an integrated care model.

ETHICS AND DISSEMINATION

Ethics approval is not required for this scoping review. For maximum impact, we will disseminate the findings to the scientific community (via publication in a peer-reviewed journal and at national conferences) and to the broader healthcare community. We will share findings with the broader healthcare community through our research centre's existing stakeholder communication structures and through guidance from our multidisciplinary research team. These key stakeholder relationships will continue to guide our subsequent RMBC research following the review.

To Utility and Beyond! Specifying and Advancing the Utility of Measurement-Based Care for Youth

Mental health organizations that serve youth are under pressure to adopt measurement-based care (MBC), defined as the continuous collection of client-report data used to support clinical decision-making as part of standard care. However, few frameworks exist to help leadership ascertain how to select an MBC approach for a clinical setting. This paper seeks to define how an MBC approach can display clinical utility to provide such a framework. Broadly, we define clinical utility as evidence that an MBC approach assists stakeholders in fulfilling clinical goals related to care quality (i.e., improve client-clinician alliance and clinical outcomes) at the client (i.e., youth and caregiver), clinician, supervisor, and administrator levels. More specifically, our definition of clinical utility is divided into two categories relevant to the usability and usefulness of an MBC approach for a specific setting: (a) implementability (i.e., evidence indicating ease of use in a clinical setting) and (b) usefulness in aiding clinical activities (i.e., evidence indicating the potential to improve communication and make clinical activities related to care quality easier or more effective). These categories provide valuable information about how easy an MBC approach is to use and the potential benefits that the MBC data will confer. To detail how we arrived at this definition, we review prior definitions of clinical utility, discuss how previous definitions inform our definition of clinical utility for MBC, and provide examples of how the concept of clinical utility can be applied to MBC. We finish with a discussion of future research directions.

Patients’ and Psychologists’ Preferences for Feedback Reports on Expected Mental Health Treatment Outcomes: A Discrete-Choice Experiment

In recent years, there has been an increasing focus on routine outcome monitoring (ROM) to provide feedback on patient progress during mental health treatment, with some systems also predicting the expected treatment outcome. The aim of this study was to elicit patients’ and psychologists’ preferences regarding how ROM system-generated feedback reports should display predicted treatment outcomes. In a discrete-choice experiment, participants were asked 12–13 times to choose between two ways of displaying an expected treatment outcome. The choices varied in four different attributes: representation, outcome, predictors, and advice. A conditional logistic regression was used to estimate participants’ preferences. A total of 104 participants (68 patients and 36 psychologists) completed the questionnaire. Participants preferred feedback reports on expected treatment outcome that included: (a) both text and images, (b) a continuous outcome or an outcome that is expressed in terms of a probability, (c) specific predictors, and (d) specific advice. For both patients and psychologists, specific predictors appeared to be most important, specific advice was second most important, a continuous outcome or a probability was third most important, and feedback that includes both text and images was fourth in importance. The ranking in importance of both the attributes and the attribute levels was identical for patients and psychologists. This suggests that, as long as the report is understandable to the patient, psychologists and patients can use the same ROM feedback report, eliminating the need for ROM administrators to develop different versions.

Clinician perceptions of nomothetic and individualized patient-reported outcome measures in measurement-based care

Objective: Measurement-based care (MBC), which encompasses routine outcome monitoring (ROM) and measurement feedback systems (MFSs), is an evidence-based practice (EBP) supporting treatment personalization and clinical responsiveness. Despite MBC's effectiveness, clinicians report reservations regarding its utility, which may be a function of overreliance on nomothetic (i.e., standardized) measures. Although research suggests that individualized (i.e., idiographic) patient-reported outcome measures (I-PROMs) may have the potential to overcome these obstacles, little is known regarding clinicians' perceptions of different measurement approaches to MBC. Methods: This study examined clinicians' perceptions of the clinical utility, relevance to treatment planning, and practicality of nomothetic, individualized, and combined clinical feedback provided by a simulated MFS. Three hundred and twenty-nine clinicians were randomized to one of three conditions that presented a clinical vignette comprising: (a) nomothetic, (b) individualized, or (c) combined clinical feedback. Results: Participants' perceptions of the clinical feedback were not affected by the measurement approach. However, cognitive behavioral participants reported more positive perceptions of all aspects associated with the clinical feedback. Conclusion: These results were consistent with previous findings, suggesting that clinicians' theoretical orientation may have a significant impact on their perceptions of MBC, and should be considered when designing and implementing these systems.

Understanding routine outcome monitoring and clinical feedback in context: Introduction to the special section

The practice of routine outcome monitoring and providing clinical feedback has been widely studied within psychotherapy. Nevertheless, there are many outstanding questions regarding this practice. Is it an evidence-based adjunct to ongoing psychotherapies, or an ineffective complication of treatment? If it is effective, through what mechanism(s) does it act? Is it effective with all patient populations, treatment types, and service delivery mechanisms, or does its impact vary across context? What choices in the implementation process affect the utility of patient-reported data feedback on psychotherapy outcomes? The studies in this special section explore these questions using a wide variety of methods and significantly expand the reach of studies on feedback. Together, these studies represent a snapshot of a maturing field of study: Initial discoveries are developed into more robust theories and applied in a wider range of contexts, while the limits of that theory are tested. They also signal directions for future clinical and research work that may improve patient care in psychosocial interventions into the future.