The Hidden Cost of Early Dropout in Behavioral Health Care
For behavioral health organizations, growth is often measured by how many new clients enter care.
How many inquiries came in? How many intakes were booked? How quickly can a new client be matched with a provider?
But there is another metric that can have just as much influence on both clinical outcomes and financial performance.
How many clients stay in care long enough to benefit from it?
Early dropout has long been a challenge in behavioral health. A large meta-analysis encompassing 669 studies and more than 83,000 clients found that approximately one in five clients prematurely discontinue psychotherapy.1Â The exact rate varies considerably depending on the population, setting, treatment and even how dropout is defined, but the broader, consistent finding is that a meaningful portion of clients who begin psychotherapy leave before treatment is complete.
And disengagement can happen very early. In one study of insured adults seeking psychotherapy for depression in community practice, 22% never attended an initial appointment and only 42% attended three or more sessions.2 Another study found that 34% of patients who completed an initial psychotherapy visit did not return for a second within 45 days.3
What does this mean for behavioral health organizations, both from a clinical and financial perspective?
Early dropout is a clinical problem first
There are plenty of good reasons for therapy to end. A client may achieve their treatment goals. Their symptoms may improve to the point that regular treatment is no longer necessary. They may collaboratively decide with their provider to reduce the frequency of care or transition to another service.
Early dropout is different because it occurs when a client disengages before receiving the intended course of treatment or achieving the goals they entered care to address.
Given that starting therapy isn’t the same as receiving an effective course of care, that distinction matters. Although there is no universal number of sessions that every client needs, a systematic review of psychotherapy delivered in routine clinical settings found that the number of sessions associated with optimal outcomes ranged from approximately 4 to 26 sessions across different settings and populations.4
When someone attends only one or two sessions before disappearing from care, the organization may have successfully provided access, but that does not necessarily mean the client received enough care to meaningfully benefit. That makes retention an important part of the outcomes conversation.
The goal isn’t to keep clients in therapy indefinitely. It is to help more people remain engaged for the right amount of care, demonstrate meaningful improvement, and successfully complete treatment when it is clinically appropriate.
Early Dropout also has a financial impact
For behavioral health organizations, early dropout also has a relatively straightforward economic consequence. Whether care is reimbursed through Medicaid, commercial insurance, or paid out of pocket, each prematurely ended episode of care represents sessions that otherwise may have occurred as part of a clinically appropriate course of treatment.
Consider two clients who begin weekly therapy.
One attends an intake and one follow-up appointment before prematurely disengaging. The other remains engaged for eight sessions, demonstrates meaningful improvement and is successfully discharged.
Both count as new clients. But their clinical and financial trajectories are very different.
Consider a reimbursement rate of $120 per session:
- A client who disengages after 2 sessions generates $240 in session revenue.
- A client who completes 8 sessions generates $960.
- The difference is $720 for a single episode of care.
For an organization serving 10,000 clients annually, helping just 5% of clients who would otherwise disengage after two sessions remain engaged through an eight-session course of care would result in 3,000 additional completed sessions, representing $360,000 in additional annual session revenue.
The actual economics will vary significantly by organization, payor mix, reimbursement, treatment model, and clinically appropriate treatment duration. But the underlying relationship is simple. When clients prematurely leave care, organizations lose future sessions. At the same time, clients lose the opportunity to receive the intended course of treatment.
Acquisition only tells half the growth story
Organizations invest significantly in attracting new clients, building referral relationships, improving intake processes and keeping clinician caseloads full. But acquisition becomes less valuable when a significant portion of those clients disappear after the first few sessions.
The objective isn’t to maximize the lifetime value of every client by keeping people in treatment longer than necessary. In behavioral health, successful treatment should ultimately result in people receiving the care they need and completing treatment when clinically appropriate.
A healthier economic model is one in which organizations reduce premature dropout at the front end, deliver an appropriate course of treatment in the middle, and support successful discharge when clients have achieved their goals.
Early dropout distorts your outcomes data
Early dropout also has implications for organizations increasingly being asked to demonstrate outcomes to health plans, employers, funders and other stakeholders.
Imagine that 100 clients begin treatment, but only 40 remain engaged long enough to complete a follow-up outcome assessment. If an organization reports outcomes based exclusively on those 40 clients, an important question remains: what happened to the other 60?
This is the denominator problem.
Clients who remain engaged enough to provide follow-up data may differ meaningfully from those who disappear after one or two sessions. As a result, strong outcomes among treatment completers do not necessarily tell the full story of the population that entered care.
This is becoming increasingly important in value-based care discussions, where payors are beginning to look beyond improvement rates and ask how much of the population is actually represented in the outcomes being reported. An impressive improvement rate carries less weight if it reflects only a small fraction of the clients who entered care.
For organizations competing on quality, a stronger outcomes story shows both how many clients improve and how comprehensively outcomes are being measured across the population served. Strong retention and high measurement coverage make that data more representative, more credible, and ultimately more valuable in conversations with payors.
Beyond Outcomes: The ROI of MBC
Learn how to reduce costly challenges like no-shows, cancellations and early dropout with Measurement-Based Care in our upcoming webinar, taking place on October 1st, 1-2 PM ET.
Register NowThe role of Measurement-Based Care in reducing early dropout
There is no single reason clients disengage from behavioral health care. Cost, insurance changes, scheduling, therapeutic fit, expectations, transportation, symptom improvement, dissatisfaction, and countless life circumstances can influence whether someone returns for another session. Some factors are outside an organization’s control, while others provide an opportunity for intervention.
One of the most important is identifying when treatment isn’t going as expected before the client disappears.
That’s where Measurement-Based Care can play a role.
Rather than waiting until a client misses multiple appointments or stops responding to outreach, Measurement-Based Care gives clinicians a structured way to understand how someone is progressing throughout treatment.
Research into routine outcome monitoring and progress feedback provides evidence for this approach. A 2021 meta-analysis of 58 studies representing 21,699 patients found that providing clinicians with feedback on patient progress had a small but significant positive effect on symptom reduction and reduced dropout by approximately 20%.5
Earlier research has also demonstrated particular value when clinicians can identify clients who are not progressing as expected. A meta-analysis of 24 studies found that routine outcome monitoring-assisted psychotherapy generally outperformed treatment as usual, with feedback practices reducing deterioration and nearly doubling rates of clinically significant or reliable change among clients predicted to have poor outcomes.6
We’ve seen the impact of this approach firsthand. After implementing Greenspace, Epic Behavioral Health saw no-show rates among Adult Mental Health clients using MBC fall from 14.2% to 7.7%, a 46% reduction in missed appointments. Epic BH leadership attributes the improvement in part to clinicians using outcome data to discuss progress and challenges throughout care, helping clients become more engaged in their treatment and better understand their own improvement over time.
At scale, that improvement has a meaningful financial impact. A 6.5 percentage-point reduction in no-shows represents an estimated $195,000 in recovered annual revenue for an organization delivering 25,000 sessions per year, assuming an average reimbursement of $120 per session.
That’s why measurement alone isn’t the goal. The value comes from using the data to inform care. If a client’s symptoms aren’t improving, their therapeutic alliance is weakening or their assessment responses suggest that treatment isn’t meeting their needs, clinicians have an opportunity to start a conversation. They can revisit goals, explore barriers, adjust the treatment plan or determine whether another provider or level of care would be a better fit.
In other words, the data creates an opportunity to act while the client is still in the room.
A better retention metric
For behavioral health leaders, the opportunity is to move beyond simply measuring average treatment length. A high average number of sessions isn’t inherently good. A low number isn’t inherently bad.
Instead, organizations can begin asking more meaningful questions:
- What percentage of clients disengage after one, two or three sessions?
- What percentage complete their planned or clinically appropriate course of care?
- What are the most common discharge reasons?
- How does early dropout vary by location, program, payor, referral source or population?
- Are clients who consistently participate in outcome measurement more likely to remain engaged?
- What percentage of clients have sufficient follow-up data to determine whether treatment is working?
- Among clients who remain engaged, how many demonstrate meaningful clinical improvement before discharge?
Connecting these measures creates a much more complete view of organizational performance. Instead of simply knowing how many people entered care, organizations can understand how many engaged, how many received an appropriate course of treatment, how many improved and how many successfully completed care.
The ROI of helping clients stay engaged
For years, early dropout has largely been framed as a clinical challenge. For behavioral health organizations, it should increasingly be viewed as a financial and operational metric as well.
Reducing premature dropout can mean more clients receiving enough treatment to benefit, more complete and representative outcomes data, fuller clinician caseloads and more revenue generated from the clients an organization has already worked to bring into care.
It can also strengthen the organization’s position with payors. As value-based care conversations increasingly look beyond outcomes among treatment completers to ask how many clients remain engaged and how much of the population is represented in reported outcomes, reducing early dropout can help organizations demonstrate both stronger engagement and more credible evidence of impact.
The opportunity isn’t to maximize the number of sessions every client receives. It’s to reduce premature disengagement and help more clients receive an appropriate course of care.
When organizations can identify clients who are at risk of disengaging, intervene while they are still in care, measure whether treatment is working and support successful discharge at the right time, the benefits extend beyond individual outcomes. They can strengthen financial performance, improve the quality of outcomes data and create a more compelling story for payors increasingly focused on measurable value.
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Want to better understand the impact of early dropout across your organization? Book a call with Greenspace to learn how Measurement-Based Care can help strengthen client engagement, improve outcomes and uncover opportunities to drive greater clinical and financial ROI.
We’ll be sharing more about the financial impact of early dropout in our upcoming webinar, Beyond Outcomes: The ROI of Measurement-Based Care taking place on October 1st, with leaders from Calvert County Behavioral Health, Boulder Care, and Two Chairs. Register Now →
References
1. Swift, J. K., & Greenberg, R. P. (2012). Premature discontinuation in adult psychotherapy: A meta-analysis. Journal of Consulting and Clinical Psychology, 80(4), 547–559. https://doi.org/10.1037/a0028226
2. Simon, G. E., Imel, Z. E., Ludman, E. J., & Steinfeld, B. J. (2010). Predictors of early dropout from psychotherapy for depression in community practice. Psychiatric Services, 61(7), 684–689. https://doi.org/10.1176/ps.2010.61.7.684
3. Simon, G. E., Imel, Z. E., Ludman, E. J., & Steinfeld, B. J. (2012). Is dropout after a first psychotherapy visit always a bad outcome? Psychiatric Services, 63(7), 705–707. https://doi.org/10.1176/appi.ps.201100309
4. Robinson, L., Delgadillo, J., & Kellett, S. (2020). The dose-response effect in routinely delivered psychological therapies: A systematic review. Psychotherapy Research, 30(1), 79–96. https://doi.org/10.1080/10503307.2019.1566676
5. de Jong, K., Conijn, J. M., Gallagher, R. A. V., Reshetnikova, A. S., Heij, M., & Lutz, M. C. (2021). Using progress feedback to improve outcomes and reduce drop-out, treatment duration, and deterioration: A multilevel meta-analysis. Clinical Psychology Review, 85, 102002. https://doi.org/10.1016/j.cpr.2021.102002
6. Lambert, M. J., Whipple, J. L., & Kleinstäuber, M. (2018). Collecting and delivering progress feedback: A meta-analysis of routine outcome monitoring. Psychotherapy, 55(4), 520–537. https://doi.org/10.1037/pst0000167
