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Practice-Based Research: The Missing Link in Personalized Mental Health Care
- Cindy Hansen
- 11 minutes ago
- 4 min read
Across mental health and addiction treatment, variability in outcomes remains a persistent challenge—even when gold-standard interventions are delivered by highly trained and experienced professionals. Many individuals fail to achieve expected improvement, and dropout rates remain high. This reality underscores the limitations of standardized, one-size-fits-all approaches in addressing the complex and diverse needs of those seeking care.
A recent study by Darnell, Benfer, and Litz (2025) exemplifies this issue in the context of PTSD treatment. Their heuristic recommendations call for advancing personalization through practice-based research, emphasizing the integration of diverse methodologies and patient experiences into clinical practice. They advocate for localized learning systems, measurement-based care, and iterative feedback loops—strategies designed to tailor interventions to individual needs in real-world settings.
Why Practice-Based Research Matters
Traditional research models often rely on randomized controlled trials (RCTs), which provide valuable evidence but can fall short in capturing the complexity of real-world clinical environments. Practice-based research bridges this gap by generating insights from everyday clinical practice, allowing providers to learn what works for whom under actual conditions—not just in controlled settings.
Practice-based research, driven by digital innovation, is the solution for effective, personalized mental health care.
This approach prioritizes:
Local learning systems that adapt to specific populations and contexts.
Continuous feedback loops to inform treatment decisions dynamically.
Integration of client experience as a core component of care.
Feedback-Informed Treatment (FIT): A Proven Practice-Based Model
FIT operationalizes these principles through two ultra-brief, validated tools:
Outcome Rating Scale (ORS) – captures client-rated functioning.
Session Rating Scale (SRS) – measures alliance and session quality.
Completed every session, these measures generate granular, client-level trajectories, enabling clinicians to identify “not-on-track” clients early and adapt interventions before deterioration. FIT transforms therapy into a learning healthcare system, precisely the adaptive model envisioned in the 2025 heuristic framework.
Understanding the FIT Measures
Outcome Rating Scale (ORS)
The ORS consists of four items assessing major domains of functioning, closely aligned with Michael Lambert’s OQ-45 but in a much shorter format:
Individually – The client’s personal sense of distress or well-being.
Interpersonally – Quality of relationships with significant others.
Socially – Functioning in work, school, friendships, and other social arenas.
Overall – Any other factors impacting well-being.
For children and adolescents, the Child Outcome Rating Scale (CORS) retains these domains but uses age-appropriate language:
Me (personal well-being)
Family (relationships at home)
School (academic and peer environment)
Everything (general life satisfaction)
To aid comprehension, the CORS uses visual anchors like smiley and frowny faces. The ORS/CORS is completed at the beginning of each session, providing a snapshot of the client’s current functioning.
Session Rating Scale (SRS)
The SRS (and CSRS for younger clients) is administered at the end of the session and focuses on the therapeutic alliance—a critical predictor of treatment success. It includes four items rated on a sliding scale:
Relationship – How the client feels about their connection with the therapist.
Goals and Topics – Whether the session addressed what the client wanted to cover.
Approach or Method – How well the therapist’s approach fits the client’s preferences.
Overall – A general assessment of the session.
These ratings provide a quantitative measure of alliance quality. Using composite and sub-scale scores, and a red-yellow-green signalling system, clinicians can quickly identify if the alliance is strong or at risk. If scores indicate a rupture, the therapist can address concerns immediately—before they lead to disengagement or dropout.
Key Features of FIT
Measurement-Based Care (MBC): Real-time data informs treatment adjustments.
Iterative Feedback Loops: Clinicians respond to trends as they emerge.
Client-Centered Approach: Feedback is collaborative and actionable.
FIT’s design addresses common implementation barriers:
ORS/SRS take less than one minute to complete.
Feedback is instant, visual, and clinically relevant.
Clinicians report improved engagement and reduced dropout rates.
Evidence and Impact
Feedback-Informed Treatment (FIT) has consistently demonstrated strong effectiveness in a wide range of clinical environments. Research highlights that FIT is associated with notable reductions in dropout rates, leading to more clients remaining engaged in therapy for longer durations (Anker et al., 2009; Bohanske & Franczak, 2010; Duncan & Miller, 2008). In particular, FIT has proven beneficial for clients who are considered “not-on-track,” helping to improve their outcomes by allowing clinicians to identify and address issues early in the therapeutic process.
One of the core strengths of FIT lies in its ability to strengthen the therapeutic alliance between clinician and client. By systematically gathering and responding to client feedback, FIT fosters a collaborative environment where clients feel heard and valued, which in turn supports better therapeutic engagement and progress.
Recent meta-analyses further reinforce the empirical foundation of FIT, demonstrating its value across various contexts and populations (Pejtersen et al., 2020; Lambert et al., 2018). . These analyses show that integrating feedback mechanisms not only helps to improve clinical outcomes but also allows for more efficient allocation of therapeutic resources, ensuring that support is directed where it is most needed.
The routine use of the Outcome Rating Scale (ORS) and Session Rating Scale (SRS) in clinical practice has been shown to significantly enhance patient outcomes. By incorporating these tools, clinicians can monitor client progress in real time and adjust treatment as needed, resulting in higher satisfaction with the therapy process overall (Mikeal et al., 2016; Bovendeerd et al., 2019; Pejtersen et al., 2020; Lambert et al., 2018).
Conclusion
The heuristic recommendations by Darnell, Benfer, and Litz (2025) highlight the importance of personalization, as shown by practice-based research models like FIT. FIT integrates client feedback and offers a scalable, adaptable solution for mental health care. Practice-based research transforms mental health and addiction care by offering dynamic, personalized alternatives to static approaches. Models like FIT use real-world feedback to improve outcomes and satisfaction.
Advanced digital tools based on ORS and SRS, guided by FIT, enable session-by-session monitoring and real-time feedback, creating an adaptive system that supports personalized care and optimizes resources. The integration of predictive analytics and AI is set to enhance therapy outcomes further.
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