Designing Health Information Technologies for Uptake: Development and Implementation of Measurement Feedback Systems in Mental Health Service Delivery

Lyon, Aaron R.; Lewis, Cara C.

doi:10.1007/s10488-015-0704-3

Cited by 38 publications

(24 citation statements)

References 44 publications

(68 reference statements)

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“…MHITS is a web-based MFS that supports MBC by facilitating administration of standardized measurement instruments for the purposes of screening and progress monitoring, providing graphical results and providing cues to clinicians based on severity or inadequate progress, among other functions. At the time of the current study, MHITS had been adapted to support MBC among clinicians working in school-based health centers (Lyon, Wasse, et al, 2016), yielding a School-Based version of MHITS (SB-MHITS).…”

Section: Methodsmentioning

confidence: 99%

“…Measurement feedback systems (MFS) – digital technologies with the ability to capture service recipient data from regular assessment of treatment progress (e.g., functional outcomes, symptom changes) or processes (e.g., therapeutic alliance, services delivered) and then deliver that information to clinicians (Bickman, 2008)– represent a leading implementation strategy to support the adoption and sustainment of MBC(Lyon & Lewis, 2016). A recent review of MFS technologiesrevealed that most systems support MBC by tracking outcomes with a library of instruments, delivering feedback to providers, and displaying outcomes graphically (Lyon, Lewis et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

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A Digital Feedback System to Support Implementation of Measurement-Based Care by School-Based Mental Health Clinicians

Lyon

Pullmann

Whitaker

et al. 2017

Journal of Clinical Child & Adolescent Psychology

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Objective. Evidence supports the utility of measurement-based care (MBC) to improve youth mental health outcomes, but clinicians rarely engage in MBC practices. Digital measurement feedback systems (MFS) may reflect a feasible strategy to support MBC adoption and sustainment. This pilot study was initiated to evaluate the impact of a MFS and brief consultation supports to facilitate MBC uptake and sustainment among mental health clinicians in the education sector, the most common mental health service delivery setting for youth. Method. Following an initial training in MBC, 14 clinicians were randomized to either: (1) a digital MFS and brief consultation supports, or (2) control. Baseline ratings of MBC attitudes, skill, and use were collected. In addition, daily assessment ratings tracked two core MBC practices (i.e., assessment tool administration, provision of feedback) over a six-month follow-up period. Results. Clinicians in the MFS condition demonstrated rapid increases in both MBC practices while the control group did not significantly change. For clinicians in the MFS group, consultation effects were significant for feedback and approached significance for administration. Over the follow-up period, average decreases in the current study were moderate with only one of the two outcome variables (administration) decreasing significantly. Inspection of individual clinician trajectories revealed substantial within-group trend variation. Conclusions. MFS may represent an effective MBC implementation strategy beyond initial training, although individual clinician response is variable. Identifying feasible and impactful implementation strategies is critical given the ability of MBC to support precision healthcare.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Digital Feedback System to Support Implementation of Measurement-Based Care by School-Based Mental Health Clinicians

Lyon

Pullmann

Whitaker

et al. 2017

Journal of Clinical Child & Adolescent Psychology

Self Cite

View full text Add to dashboard Cite

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“…It is encouraging that increasing attention is being given to measurement feedback systems in mental health, but these systems are innovations themselves that will require resources and unique implementation supports. Lyon and Lewis (2015) note that little empirical work has examined the strategies through which these systems are developed and implemented in behavioral health service settings. Moreover, we are not aware of any large system that has successfully implemented a measurement feedback system at the scale that would be required in Philadelphia.…”

Section: Ongoing Challenges Opportunities and Lessons Learnedmentioning

confidence: 99%

Applying the Policy Ecology Framework to Philadelphia’s Behavioral Health Transformation Efforts

Powell

Beidas

Rubin³

et al. 2016

Adm Policy Ment Health

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Raghavan et al. (2008) proposed that effective implementation of evidence-based practices requires implementation strategies deployed at multiple levels of the “policy ecology,” including the organizational, regulatory or purchaser agency, political, and social levels. However, much of implementation research and practice targets providers without accounting for contextual factors that may influence provider behavior. This paper examines Philadelphia’s efforts to work toward an evidence-based and recovery-oriented behavioral health system, and uses the policy ecology framework to illustrate how multifaceted, multilevel implementation strategies can facilitate the widespread implementation of evidence-based practices. Ongoing challenges and implications for research and practice are discussed.

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“…The use of MBC in mental healthcare substantially lags its use for physical health conditions, often due to practical barriers. MBC for physical health problems typically relies on biometric values reported in the electronic health record systems (EHRs), whereas MBC for depression often relies on patient-reported outcomes, which are not routinely administered or entered into EHRs (Lyon & Lewis, 2016). Most MBC assessment tools do not measure treatment goals that are not directly reflected as symptoms of depression or other medical conditions, and most MBC tools only provide snapshots of symptoms at the time of clinical appointments and little or no information about patient outcomes between appointments.…”

Section: Measurement-based Carementioning

confidence: 99%

“…Many administrative and technological barriers exist for integrating MBC and EHRs (Lyon & Lewis, 2016), including a limited presence of behavioral health elements in EHRs, limited interoperability and access to behavioral health data across different settings, and variability in EHRs and system requirements (Karakus, Ghose, Goldman, Moran, & Hogan, 2017). Implementation strategies will be necessary to better integrate patient-reported outcomes into EHRs, including studying key barriers and facilitators of integration, education on the benefits of MBC for different stakeholders, and obtaining stakeholder input across multiple domains (e.g., clinical, administrative, data management, software design, business) and throughout stages of development and implementation (Gleacher et al, 2016; Lyon, Wasse, et al, 2016).…”

Section: Measurement-based Carementioning

confidence: 99%

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities

2017

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Clinical decision-making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision-making. In this paper, we provide ‘snapshots’ of research and current directions on ways that digital technologies can support clinical decision-making in depression treatment. Practical facets of clinical decision-making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision-making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision-making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision-making for depression by (a) improving tools to incorporate depression symptom data into existing electronic health record systems, (b) enhancing measurement of treatment fidelity and treatment processes, (c) harnessing smartphone and biosensor data to inform clinical decision-making, (d) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (e) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision-making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.

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Designing Health Information Technologies for Uptake: Development and Implementation of Measurement Feedback Systems in Mental Health Service Delivery

Cited by 38 publications

References 44 publications

A Digital Feedback System to Support Implementation of Measurement-Based Care by School-Based Mental Health Clinicians

A Digital Feedback System to Support Implementation of Measurement-Based Care by School-Based Mental Health Clinicians

Applying the Policy Ecology Framework to Philadelphia’s Behavioral Health Transformation Efforts

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities

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