Provider-Driven Development of a Measurement Feedback System to Enhance Measurement-Based Care in VA Mental Health

Landes, Sara J.; Carlson, Eve B.; Ruzek, Josef I.; Wang, Dan; Hugo, Emily; DeGaetano, Noah; Chambers, Justin G.; Lindley, Steven E.

doi:10.1016/j.cbpra.2014.06.004

Cited by 32 publications

(23 citation statements)

References 26 publications

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“…Because these barriers are reported by a variety of service providers (i.e., our sample consisted of care coordinators; the sample from Gleacher et al 2015 consisted of trained clinicians while those from Connors et al 2015 andLyon et al 2016 consisted of school mental health providers), prioritizing these common barriers seems especially important. Specifically, provider concerns about the clinical utility and scientific merit of the system reported in previous research (Bickman 2008;Garland et al 2003;Hatfield and Ogles 2007;Landes et al 2014) are again reflected in our findings. For example, concerns with the measure's reliability and validity led to doubts over its clinical meaningfulness.…”

Section: Emergent Life Eventsupporting

confidence: 64%

“…Additional barriers frequently cited in the literature across these related systems include organizational factors, such as insufficient resources (Bickman 2008;Gleacher et al 2015;Jensen-Doss et al 2009), costs associated with purchasing and using the system (Bickman 2008;, and insufficient training on the system (Gleacher et al 2015;. Similarly, provider-level barriers, such as incompatibility between the innovation and clinician values (Bickman 2008;, fears about the impact on therapeutic alliance , and provider concerns about the clinical utility and scientific merit (Bickman 2008;Garland et al 2003;Hatfield and Ogles 2007;Landes et al 2014) have been identified.…”

mentioning

confidence: 97%

“…For example, practical facilitators are often presented as solutions to barriers of practicality, such as limiting clinicians' time expenditure by using brief measures and incorporating information technologies for administering and scoring measures (Lambert 2010;Landes et al 2014). Additional facilitators include having the leadership within an organization or agency champion help facilitate training support (Gleacher et al 2015;Lambert 2010) and using measures perceived by providers as clinically relevant (Gleacher et al 2015).…”

mentioning

confidence: 98%

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Facilitators and Barriers of Implementing a Measurement Feedback System in Public Youth Mental Health

Kotte

Hill

Mah

et al. 2016

Adm Policy Ment Health

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This study examines implementation facilitators and barriers of a statewide roll-out of a measurement feedback system (MFS) in a youth public mental health system. 76 % of all state care coordinators (N = 47) completed interviews, which were coded via content analysis until saturation. Facilitators (e.g., recognition of the MFS's clinical utility) and barriers (e.g., MFS's reliability and validity) emerged paralleling the Exploration, Adoption/Preparation, Implementation, and Sustainment framework outlined by Aarons et al. (Adm Policy Mental Health Mental Health Serv Res, 38:4-23, 2011). Sustainment efforts may leverage innovation fit, individual adopter, and system related facilitators.

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Section: Emergent Life Eventsupporting

confidence: 64%

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confidence: 97%

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confidence: 98%

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Facilitators and Barriers of Implementing a Measurement Feedback System in Public Youth Mental Health

Kotte

Hill

Mah

et al. 2016

Adm Policy Ment Health

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“…As indicated above, we defined the scope of the example application to focus on behavioral health MFS. The behavioral health literature has seen considerable MFS advancement in recent years, including a growing number of MFS publications [ 47 ], special issues [ 48 , 49 ], theories outlining intended use [ 38 ], and methods of MFS development [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Health Information Technologies—Academic and Commercial Evaluation (HIT-ACE) methodology: description and application to clinical feedback systems

et al. 2015

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BackgroundHealth information technologies (HIT) have become nearly ubiquitous in the contemporary healthcare landscape, but information about HIT development, functionality, and implementation readiness is frequently siloed. Theory-driven methods of compiling, evaluating, and integrating information from the academic and commercial sectors are necessary to guide stakeholder decision-making surrounding HIT adoption and to develop pragmatic HIT research agendas. This article presents the Health Information Technologies—Academic and Commercial Evaluation (HIT-ACE) methodology, a structured, theory-driven method for compiling and evaluating information from multiple sectors. As an example demonstration of the methodology, we apply HIT-ACE to mental and behavioral health measurement feedback systems (MFS). MFS are a specific class of HIT that support the implementation of routine outcome monitoring, an evidence-based practice.ResultsHIT-ACE is guided by theories and frameworks related to user-centered design and implementation science. The methodology involves four phases: (1) coding academic and commercial materials, (2) developer/purveyor interviews, (3) linking putative implementation mechanisms to hit capabilities, and (4) experimental testing of capabilities and mechanisms. In the current demonstration, phase 1 included a systematic process to identify MFS in mental and behavioral health using academic literature and commercial websites. Using user-centered design, implementation science, and feedback frameworks, the HIT-ACE coding system was developed, piloted, and used to review each identified system for the presence of 38 capabilities and 18 additional characteristics via a consensus coding process. Bibliometic data were also collected to examine the representation of the systems in the scientific literature. As an example, results are presented for the application of HIT-ACE phase 1 to MFS wherein 49 separate MFS were identified, reflecting a diverse array of characteristics and capabilities.ConclusionsPreliminary findings demonstrate the utility of HIT-ACE to represent the scope and diversity of a given class of HIT beyond what can be identified in the academic literature. Phase 2 data collection is expected to confirm and expand the information presented and phases 3 and 4 will provide more nuanced information about the impact of specific HIT capabilities. In all, HIT-ACE is expected to support adoption decisions and additional HIT development and implementation research.

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“…For instance, researchers have noted various organizational barriers, such as opposition by policy-holders regarding state-wide outcome measurement, a lack of prioritization of adoption of MFSs by funding agencies, and the prohibitive costs of such an endeavor (Bickman 2008b; Landes et al 2015). Additionally, when attempting to incorporate MFSs into their practices, providers have voiced concerns regarding difficulties in interpreting and applying client-level data stemming from routine feedback (Callaly et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Interpreting Progress Feedback to Guide Clinical Decision-Making in Children’s Mental Health Services

Tsai

Moskowitz

Brown

et al. 2015

Adm Policy Ment Health

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Measurement feedback systems (MFSs) can help improve clinical outcomes by enhancing clinical decision-making. Unfortunately, limited information exists to guide the use and interpretation of data from MFSs. This study examined the amount of data that would provide a reasonable and reliable prediction of a client's rate of symptomatology in order to help inform clinical decisionmaking processes. Results showed that use of more data predicted greater levels of accuracy. However, there were diminishing returns on the ability for additional data to improve predictive accuracy. Findings inform efforts to develop guidelines on the interpretation of data from MFSs.

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Provider-Driven Development of a Measurement Feedback System to Enhance Measurement-Based Care in VA Mental Health

Cited by 32 publications

References 26 publications

Facilitators and Barriers of Implementing a Measurement Feedback System in Public Youth Mental Health

Facilitators and Barriers of Implementing a Measurement Feedback System in Public Youth Mental Health

Health Information Technologies—Academic and Commercial Evaluation (HIT-ACE) methodology: description and application to clinical feedback systems

Interpreting Progress Feedback to Guide Clinical Decision-Making in Children’s Mental Health Services

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