The use of outcome measures is an essential component to achieving superior treatment outcomes in psychiatry. Using validated rating scales to assess symptoms, functioning, side effects, and quality of life can lead to better outcomes for both symptomatic and relapsed patients, and can improve treatment satisfaction for the patient and the practitioner.
The benefits of what is measurement based care for mental health are numerous and varied, but the primary benefit is that it provides empirical evidence of clinical outcomes. This is important because a growing number of insurance carriers are requiring objective documentation of improvement in the clinical assessment and management of behavioral healthcare. This is especially true in Medicare and Medicaid, where there is increased scrutiny for objectively monitoring behavioral healthcare.
Historically, measurement based care has been viewed as a difficult process to implement into practice due to many barriers. For example, patients often fear confidentiality breaches if they provide feedback about their mental health, practitioners may feel that measures are no better than clinical judgment, and organizational issues can make it hard to train clinicians in the use of such tools.
However, implementation science–a research field that studies methods for implementing and sustaining effective health behaviors–has provided strategies for overcoming these barriers. These include: introducing measurement feedback systems, leveraging local champions, forming learning collaboratives, improving expert consultation with clinical staff, and generating incentives.
Digital technologies can further expand measurement based care by introducing passively gathered data and expanding real-world experience sampling. This allows clinicians to collect more representative and expansive real-world data that can be used to track patient progress over time and improve patient engagement.
These systems also offer a range of analytics that allow leaders to benchmark performance across the organization and generate reports that support quality metrics for payers, grant funders, and policymakers. They also enable clinicians to optimize care pathways, monitor progress, and pivot treatments when necessary.
In addition, these systems can also help clinicians develop more individualized treatment plans. Rather than having to prescribe the same medications or psychotherapy sessions to every patient, these systems can automatically match patients to an appropriate treatment plan and track their progress over time.
This can significantly reduce treatment inertia, prompting clinicians to make changes to their treatment when patients are not responding as expected or showing relapse. This can lead to improved clinical outcomes, and it can reduce costs.
The use of symptom rating scales to measure outcomes has been shown to be an effective strategy for treating patients with depression. Several randomized controlled trials have demonstrated that routine symptom monitoring and algorithm-based treatment adjustments can result in more consistent and faster treatment outcomes for these patients. This is especially true in the case of relapses and depressive recurrences. For these reasons, the National Committee for Quality Assurance (NCQA) has recommended that health plans include monitoring of depression symptom severity and response to treatment as clinical performance measures in their health plan databases. In addition, several payers are implementing value-based payment programs that incentivize providers to utilize measurement based care as part of their treatment.