To Reduce Therapist Burnout and Improve Care, Turn to Longitudinal Data

The mental health workforce is burning out, and it’s not from doing therapy. And despite increasing awareness of the issue, alarming data — such as the APA’s 2023 Practitioner Pulse Survey, which found that 36% of psychologists report feeling burnt out — remains stubbornly high. 

By now, healthcare leaders understand that mental health workers are at their limits. But what’s less universally understood is how the consequences of this crisis extend beyond clinician wellbeing, ultimately putting patient outcomes in jeopardy. 

Research published in JAMA Network Open found that patients treated by burned-out therapists achieved clinically meaningful improvement only 28.3% of the time, compared to 36.8% with non-burned-out therapists.

The crisis isn’t just about therapist wellbeing. It’s about whether health systems allow clinicians to focus on what matters most: delivering mental health treatment that actually works, without sacrificing their time, sanity, and passion for their work. Addressing this requires more than wellness programs or resilience training — it demands integrated systems that lighten administrative burdens rather than compound them.

The problem has never been patients

The conventional wisdom around therapist burnout misses a critical distinction: emotional labor is far from the only factor at play.

The culprit is everything around patient care. When therapists talk about feeling overwhelmed, they’re often describing the fragmented tooling they’re forced to navigate: one system for scheduling, another for documentation, a third for insurance verification, and on it goes. 

One study found that Medicaid-participating physicians lose 18% of their revenue to billing problems, including repeated claims denials and resubmissions. These are costs, both financial and logistical, that directly cut into clinical practice time, provider job satisfaction, and, over time, clinicians’ well-being.

Looking at documentation within mental health specifically, the irony runs even deeper. Unlike a broken bone that either heals or doesn’t, mental health improvements are incremental and subjective. Compared to other medical fields, the mental health sector lags behind in developing performance measures, with inadequate infrastructure to capture the data elements necessary to justify quality-based reimbursement — making mental health one of the few specialties where documenting patient progress is genuinely difficult.  

This documentation challenge creates a vicious cycle: therapists struggle to prove progress, insurers deny claims, therapists resubmit with more documentation, and the administrative burden compounds. It’s not just an administrative headache; it’s revenue left on the table, plus time and energy stolen from patient care.

Longitudinal data: the answer to the demands of documentation

Enter longitudinal patient data. Unlike traditional mental health documentation, this method provides objective evidence of progress without requiring therapists to expend resources to manufacture it.

Wearables, for example, enable continuous collection of physiological data across different stages of mental health disorders, from initial risk factors through treatment progress to recovery. A large cohort study using longitudinal Fitbit data from nearly 9,000 participants in the “All of Us” program demonstrated that wearables can detect depressive and anxiety disorders by combining daily activity patterns with clinical data from electronic health records. 

When integrated into therapy delivery, this approach directly addresses the documentation problem. Instead of relying on a patient’s recall during a 50-minute session or a therapist’s subjective clinical notes, longitudinal data captures objective patterns: sleep disruption before a depressive episode, activity levels correlating with mood improvements, and physiological stress markers indicating treatment efficacy. These are clinical insights that double as evidence — the kind that withstands insurer scrutiny.

NIH research underscores the tangible benefits: early detection of deteriorating conditions, proactive interventions, increased patient engagement through real-time feedback, and more consistent data than traditional monitoring methods. For reimbursement purposes, this data transforms vague progress notes into quantifiable treatment trajectories.

The catch is that despite the potential of wearable technology, mental healthcare professionals currently lack the tools and knowledge to properly implement it in practice without further burdening their workloads. 

On average, implementing structured EHR systems can reduce face-to-face patient care time by 8.5%, as administrative tasks divert focus from clinical work. If therapists have to manually pull data from Fitbit, cross-reference it with Apple Health, aggregate mood tracking apps, and synthesize it all into clinical documentation, we’ve just replaced one administrative burden with another. 

The promise of data-driven care can’t be realized if capturing that data accelerates the burnout it’s meant to address. Put simply, the solution to documentation burnout can’t create more documentation work. 

AI handles the orchestration, therapists handle the healing

To reach its full potential, longitudinal data requires AI — not as an optional enhancement or a replacement for therapeutic judgement, but as essential infrastructure that automates data collection. 

Researchers estimate that AI technologies could potentially save $200-$360 billion in healthcare spending over the next five years, primarily by automating routine tasks and reducing administrative waste. More specifically, studies have shown that AI and automation can improve operational efficiency by streamlining processes for prior authorization, quality measurement, and, of course, documentation.

Within the mental health arena, AI can orchestrate the data pipeline that makes longitudinal monitoring practical: 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 progress reports for reimbursement; and streamlined claims processes that leverage objective data to reduce denials.

Longitudinal data provides the objective evidence therapists need to prove treatment efficacy, while AI handles the orchestration that would otherwise make data collection another burden — offering a tangible solution to the documentation paradox.

This outcome isn’t about flashy AI hype. It’s about using AI as a supportive tool to realign healthcare with its foundational goals: allowing healthcare professionals to focus on patient care by automating repetitive tasks. In this case, the repetitive task is aggregating the very data that could solve the documentation crisis.

What’s really at stake

The solution to behavioral health staff burnout isn’t asking therapists to do more self-care or be more resilient. It’s recognizing how administrative pressures impact therapists and patients alike, and what it realistically takes to centralize and utilize the power of longitudinal patient data.

But that answer only works if we build the infrastructure to reduce the workload, not worsen it. Automated aggregation of data from wearables, mood trackers, and patient apps. Intelligent synthesis that surfaces clinically relevant patterns. Documentation systems that generate evidence-backed progress reports from therapeutic conversations and objective data, rather than demanding manual entry.

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

Therapist burnout isn’t inevitable. But solving it requires understanding that longitudinal data only works as a solution if we automate the orchestration.

Photo: iodrakon, Getty Images

Raffay Rana is the co-founder and CTO at Oasys. He leads AI and product development, with expertise in machine learning and data infrastructure. Raffay is focused on building scalable, secure, and intelligent systems that turn fragmented data into actionable clinical insight.

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