To reduce therapist burnout and improve care, turn to longitudinal data

Mental health workers are burning out, and it's not because of treatment. Despite growing awareness of the issue, alarming statistics—such as APA's 2023 Practitioner Pulse Survey, which found that 36% of psychologists report experiencing burnout—remain high.

By now, health care leaders understand that mental health workers are stretched to their limits. But what is less understood is how the consequences of this crisis extend beyond clinician well-being, ultimately putting patient outcomes at risk.

The study, published in JAMA Network Open, found that only 28.3% of patients treated by burnout therapists achieved clinically meaningful improvements, compared with 36.8% of patients treated by non-burnout therapists.

This crisis isn’t just about therapists’ health. It’s about whether health systems allow clinicians to focus on what matters most: providing truly effective mental health treatment without sacrificing their time, sanity, and passion for their work. Solving this problem will require more than just wellness programs or resilience training—it will require comprehensive systems that can reduce the administrative burden, not increase it.

The problem is never the patient

Conventional wisdom about therapist burnout misses a key distinction: Emotional labor is far from the only factor at play.

The culprit is everything in patient care. When therapists talk about feeling overwhelmed, they often describe the fragmented tools they are forced to use: one system for scheduling, another for documentation, a third for insurance verification, and so on.

One study found that Medicaid physicians lost 18% of their revenue due to billing issues, including repeated claim denials and resubmissions. These costs, including financial and logistical costs, directly impact clinical practice time, provider job satisfaction, and, over time, clinician well-being.

Looking specifically at the literature in the field of mental health, the irony is even deeper. Unlike broken bones that either heal or don't heal, improvements in mental health are gradual and subjective. Compared with other medical fields, the mental health sector lags behind in developing performance measures and lacks the infrastructure to capture the data elements needed to justify quality-based reimbursement, making mental health one of the few specialties where it is difficult to document patient progress.

This documentation challenge creates a vicious cycle: therapists struggle to demonstrate progress, insurance companies deny claims, therapists resubmit more documentation, and administrative burdens mount. This isn't just an administrative headache; it's revenue left on the table, plus time and energy stolen from patient care.

Longitudinal data: the answer to your documentation needs

Enter longitudinal patient data. Unlike traditional mental health records, this approach provides objective evidence of progress without requiring the therapist to expend resources to produce it.

For example, wearable devices can continuously collect physiological data at different stages of mental health disorders, from initial risk factors to treatment progression to recovery. A large cohort study using longitudinal Fitbit data from nearly 9,000 participants in the All of Us program shows that the wearable device can detect depression and anxiety by combining daily activity patterns with clinical data from electronic health records.

When integrated into treatment delivery, this approach directly addresses documentation issues. The longitudinal data do not rely on patients' recollections during 50-minute sessions or therapists' subjective clinical notes, but instead capture objective patterns: sleep disruptions before depressive episodes, activity levels associated with mood improvements, and physiological stress markers that indicate treatment effects. These are clinical insights but also evidence – something that will stand up to the scrutiny of insurance companies.

NIH research highlights tangible benefits: early detection of worsening conditions, proactive intervention, increased patient engagement through real-time feedback, and more consistent data than traditional monitoring methods. For reimbursement purposes, these data transform vague records of progress into quantifiable treatment trajectories.

The problem is that despite the potential of wearable technology, mental health professionals currently lack the tools and knowledge to properly implement this technology in practice without further burdening their workload.

On average, implementing a structured EHR system can reduce face-to-face patient care time by 8.5% because administrative tasks distract from clinical work. If therapists have to manually extract data from a Fitbit, cross-reference it with Apple Health, aggregate it from a mood tracking app, and synthesize it all into clinical documentation, we've just replaced one administrative burden with another.

The promise of data-driven care cannot be realized if capturing the data accelerates the very burnout it is supposed to address. In short, the solution to documentation burnout is not to create more documentation work.

Artificial intelligence is responsible for programming, and therapists are responsible for treatment

To realize their full potential, longitudinal data require artificial intelligence—not as an optional augmentation or substitute for treatment judgment, but as a critical infrastructure for automated data collection.

Researchers estimate that AI technology could save $20-360 billion in health care spending over the next five years, primarily by automating routine tasks and reducing administrative waste. More specifically, research shows that AI and automation can improve operational efficiency by streamlining processes for prior authorization, quality measurement, and documentation.

In mental health, AI can orchestrate data pipelines to make longitudinal monitoring feasible: automated synthesis of patient data from wearables, mood trackers, and other sources; intelligent documentation that extracts clinically relevant patterns without manual data entry; automated generation of evidence-backed reimbursement progress reports; and streamlined claims processes that leverage objective data to reduce denials.

Longitudinal data provides the objective evidence therapists need to demonstrate treatment effectiveness, while AI orchestrates data collection that would otherwise become another burden—providing a tangible solution to the documentation paradox.

This result has nothing to do with flashy AI hype. It’s about using artificial intelligence as a supporting tool to realign healthcare with its fundamental goal: to allow healthcare professionals to focus on patient care by automating repetitive tasks. In this case, the repetitive task is to aggregate data that can solve the document crisis.

What's really at stake?

The solution to behavioral health staff burnout is not asking therapists to practice more self-care or become more resilient. It's recognizing how managing stress impacts therapists and patients, and what it actually takes to centralize and harness the power of longitudinal patient data.

But this answer only works if we build infrastructure to reduce workloads rather than worsen them. Automatically aggregate data from wearables, mood trackers, and patient apps. Intelligent synthesis renders clinically relevant patterns. Documentation systems generate evidence-based progress reports based on treatment conversations and objective data, rather than requiring manual input.

The technology exists. Wearable devices are capturing data. AI can orchestrate it. The real question is whether the mental health industry will implement these tools in a way that truly serves therapists and patients, or whether we will simply add longitudinal data monitoring to an already fragmented tool stack that therapists must navigate manually.

Therapist burnout is not inevitable. But solving this problem requires understanding that longitudinal data can only be a solution when we automate orchestration.

Photo: iodrakon, Getty Images

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