Return on Investment of Enhanced Behavioral Health Services

Matt Hawrilenko; Casey Smolka; Emily Ward; Geetu Ambwani; Millard Brown; Anita Mohandas; Martin Paulus; John Krystal; Adam Chekroud

doi:10.1001/jamanetworkopen.2024.57834

. 2025 Feb 5;8(2):e2457834. doi: 10.1001/jamanetworkopen.2024.57834

Return on Investment of Enhanced Behavioral Health Services

Matt Hawrilenko ^1,^✉, Casey Smolka ¹, Emily Ward ¹, Geetu Ambwani ¹, Millard Brown ¹, Anita Mohandas ¹, Martin Paulus ², John Krystal ³, Adam Chekroud ^1,³

¹Spring Health, New York, New York

²Laureate Institute for Brain Research, Tulsa, Oklahoma

³Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut

Accepted for Publication: November 23, 2024.

Published: February 5, 2025. doi:10.1001/jamanetworkopen.2024.57834

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2025 Hawrilenko M et al. JAMA Network Open.

^✉

Corresponding Author: Matt Hawrilenko, PhD, Spring Health, 60 Madison Ave, New York, NY 10010 (mhawrilenko@springhealth.com).

Author Contributions: Dr Hawrilenko had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hawrilenko, Smolka, Ambwani, Brown, Paulus, Krystal, Chekroud.

Acquisition, analysis, or interpretation of data: Hawrilenko, Smolka, Ward, Ambwani, Mohandas, Krystal, Chekroud.

Drafting of the manuscript: Hawrilenko, Paulus, Chekroud.

Critical review of the manuscript for important intellectual content: Hawrilenko, Smolka, Ward, Ambwani, Brown, Mohandas, Krystal, Chekroud.

Statistical analysis: Hawrilenko, Smolka, Ward.

Administrative, technical, or material support: Smolka, Ward, Ambwani, Chekroud.

Supervision: Ambwani, Brown, Mohandas, Paulus, Chekroud.

Conflict of Interest Disclosures: Dr Hawrilenko reported being employed by and holding equity in Spring Care Inc outside the submitted work. Mr Smolka reported being employed by and holding equity in Spring Care Inc outside the submitted work. Dr Ward reported being employed by and holding equity in Spring Care Inc outside the submitted work. Ms Ambwani reported being employed by and holding equity in Spring Care Inc outside the submitted work. Dr Brown reported being employed by and holding equity in Spring Care Inc outside the submitted work. Dr Paulus reported receiving grants from the National Institute of General Medical Sciences and the National Institute on Drug Abuse outside the submitted work. Dr Krystal reported holding stock in Spring Care Inc and holding stock or options (with or without additional compensation) in Freedom Biosciences, Biohaven Pharmaceuticals, Cartego Therapeutics, Damona Pharmaceuticals, EpiVario Inc, Neumora Therapeutics, Rest Therapeutics, Response Pharmaceuticals, Tempero Bio, Terran Biosciences, and Tetricus Inc outside the submitted work. In addition, Dr Krystal reported serving as a paid scientific advisor for AbbVie, Aptinyx Inc, Biogen, Bionomics, Boehringer Ingelheim Pharmaceuticals, Cerevel Pharmaceuticals, Epiodyne, Eisei Pharmaceuticals, Jazz Pharmaceuticals, Johnson & Johnson, Novartis Pharmaceuticals, Psychogenics Inc, and Takeda Pharmaceuticals. Finally, Dr Krystal reported conducting research with drugs supplied by Cerevel Pharmaceuticals and BioXcel Pharmaceuticals. Dr Chekroud reported being employed by and holding equity in Spring Care Inc outside the submitted work. In addition, Dr Chekroud reported being the lead inventor on 3 patent submissions relating to treatment for major depressive disorder (US Patent and Trademark Office number Y0087.70116US00 and provisional application numbers 62/491 660 and 62/629 041) outside the submitted work. Finally, Dr Chekroud reported holding equity in Carbon Health Technologies Inc, Wheel Health Inc, Parallel Technologies Inc, Healthie Inc, and UnitedHealthcare; receiving consulting fees from Fortress; and providing unpaid advisory services to health care technology startups outside the submitted work. No other disclosures were reported.

Data Sharing Statement: See Supplement 2.

^✉

Corresponding author.

PMCID: PMC11800021 PMID: 39908020

Key Points

Question

Does a clinically effective employer-sponsored mental health benefit result in net medical cost savings?

Findings

In this cohort study of 13 990 employees and dependents, medical claims costs were reduced by $190 for every $100 invested in the mental health benefit.

Meaning

These findings suggest that expanding access to behavioral health care may be a financially viable cost-reduction strategy for health care buyers.

This cohort study examines the association between an employer-sponsored behavioral health benefit and net medical cost savings.

Abstract

Importance

Employer-sponsored benefit programs aim to increase access to behavioral health care, which may help contain health care costs. However, research has either focused solely on clinical outcomes or demonstrated reductions in medical claims without accounting for the costs of behavioral health services, leaving the financial return on investment unknown.

Objective

To determine whether a clinically effective employer-sponsored behavioral health benefit is associated with net medical cost savings.

Design, Setting, and Participants

This retrospective cohort study included participants eligible for an employer-sponsored behavioral health benefit between November 1, 2019, and May 31, 2023. Eligibility criteria included having a behavioral health diagnosis and, in the program group, attending at least 1 behavioral health appointment. Program users were matched to nonusers on medical risk scores, behavioral health diagnoses, date of diagnosis, age, sex, and employer. Participants were followed up for 1 year before and after the benefit launch.

Exposure

A digital platform screened individuals for common behavioral health conditions and provided access to video and in-person psychotherapy, medication management, care navigation, and self-guided digital content.

Main Outcomes and Measures

Primary outcomes were per member per month (PMPM) medical spending, inclusive of all medical claims and program costs. A difference-in-differences analysis was used to compare changes in net medical spending between groups from the year before and up to 1 year after an index mental health diagnosis.

Results

This study included 13 990 participants: 4907 of 4949 (99.1%) eligible program group members were matched to 9083 control participants. Their mean (SD) age was 37 (13.2) years, and most participants (65.5%) were female. Costs decreased in the program group relative to the control group, with a net difference-in-differences of −$164 PMPM (95% CI, −$228 to −$100 PMPM), corresponding to savings of $1070 per participant in the first program year and a return on investment of 1.9 times the costs (ie, every $100 invested reduced medical claims costs by $190). Behavioral health costs in the program group increased relative to the control group but were more than offset by decreases in physical health care costs. Savings were larger for participants with higher medical risk.

Conclusions and Relevance

In this cohort study, every $100 invested in an employer-sponsored behavioral health program with fast access to psychotherapy and medication management was associated with a reduction in medical claims costs by $190. These findings suggest that expanding access to behavioral health care may be a financially viable cost-reduction strategy for health care buyers.

Introduction

The total health care costs of undertreated behavioral health disorders are more than $290 billion each year in the US alone.¹ Behavioral health conditions have been linked with up to 3-fold spending increases for individuals with otherwise the same physical disease burden.^2,3 The impact is largest for patients with high-cost physical health conditions.^4,5 More than half of patients with a high-cost condition also have a behavioral health disorder,⁶ and behavioral health disorders lead to increased medical spending for chronic pain,^7,8 cardiovascular disease,^9,10,11 and diabetes.^12,13 Expanding timely access to specialty behavioral health treatments may be a cost-effective solution to improve behavioral health and physical health.^1,2,4,14,15

Behavioral health and physical health are intertwined. Behavioral health difficulties influence physical health by negatively affecting health behaviors. Depression has been linked to lower adherence to hypertension medication,^16,17 lower attendance to cardiac rehabilitation,¹⁸ and decreased physical activity.^19,20 Untreated behavioral health conditions are linked to illness persistence, higher medical complication rates, preventable hospitalizations, and greater health care service use.⁴ In contrast, behavioral health interventions positively affect physical health, such as decreasing pain^21,22,23 and improving glycemic control for type 2 diabetes.¹²

Poor access to care is a substantial barrier to effective treatment, with only 20.8% of diagnosed individuals receiving care from a behavioral health specialist within a year.²⁴ Structural barriers—such as difficulty finding appropriate clinicians, limited appointment availability, and high costs—discourage patients from seeking care, often leading to emergency or hospital visits when conditions worsen.^25,26,27 Although many employers sponsor behavioral health programs to alleviate these barriers, high-quality evidence demonstrating their effectiveness in reducing medical costs is limited by methodologic issues.^28,29 Common issues include failing to include the cost of the intervention itself,^3,30 failing to validate or enhance risk adjustment using preprogram medical spending,³⁰ or using no risk adjustment at all.³¹ Thus, expanding access to behavioral health care is a promising avenue to improve health outcomes and lower costs, but the evidence base would benefit from more rigorous research designs.

This study used a rigorous matched cohort design, with a difference-in-differences analysis and medical risk scores derived from more than 150 health conditions to evaluate the association between an employer-sponsored behavioral health program and total medical spending. Secondary objectives explored changes in behavioral health care use after the program was launched, differences in spending across medical service categories (eg, emergency department), and whether program outcomes differed for participants with high-cost medical conditions.

Methods

This cohort study was considered exempt from approval and the need for informed consent by the Yale University Institutional Review Board, which deemed it nonhuman participant research. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Study Design

A retrospective, matched cohort design was used to compare 2 groups of participants. The program group included participants receiving behavioral health care (defined as psychotherapy, medication management, or both from a behavioral health specialist) delivered through an employer-sponsored behavioral health program. The control group included participants receiving treatment as usual delivered by the health plan, which could include behavioral health care, general care from a nonspecialist (eg, primary care physician), or a behavioral health diagnosis without follow-up treatment. The observation period spanned November 1, 2019, and May 31, 2023, with each employer contributing 2 consecutive years, spanning 1 year before launch and 1 year after launch.

Program

The study analyzed data from an employer-sponsored behavioral health benefit (Spring Health; Spring Care Inc), which has been demonstrated to improve clinical outcomes for depression and anxiety and financial outcomes related to workplace productivity and retention.^32,33 The program used evidence-based components including measurement-based care, unlimited care navigation sessions, and 6 to 12 free psychotherapy sessions with a clinician, up to 2 of which could be used for medication evaluation and treatment. Care beyond the prepaid session limit was available as an in-network health plan benefit.

Participants

Participants were US employees and dependents drawn from 7 employers from industries including manufacturing, media, health care, and technology. Participant flow is shown in eFigure 1 in Supplement 1. Participants were included in this study if they received a behavioral health diagnosis any time after program launch (index diagnosis), were insured by the health plan for 6 months before and 1 month after the index diagnosis, aged 5 years or older, and did not have a transplant or end-stage kidney disease. Participants receiving treatment from both the program and the health plan during the study year were excluded to prevent complications in causal attribution of potentially time-delayed effects. To mitigate survivorship bias between control group participants (possibly in ongoing care at program launch) and program group participants (by definition, initiating a new care episode), a washout period was implemented requiring all participants to have 6 consecutive months without treatment from a behavioral health specialist prior to their index diagnosis. If a control group participant did not receive treatment from a behavioral health specialist at or after their index diagnosis, they were required to have 6 months without any behavioral health diagnosis. Program group participants needed at least 1 psychotherapy or medication management session with the program; they could also receive a diagnosis via the health plan, but it was not required. Nearest neighbor matching was used to match program users to a control cohort of nonusers. The dataset included 429 781 individuals, of whom 40 323 met criteria to enter the matching pool.

Data Sources

Medical claims data were full medical and prescription claims incurred 12 months before program launch through 12 months after launch, paid through 15 months after launch. Prescription claims were analyzed separately from medical claims when available but were unavailable for 2 employers.

Allowed amounts, representing the maximum amount the health plan will pay for a service, were used to calculate medical spending. Two employers only provided paid amounts, which were converted to allowed amounts using the following formula: Paid = Allowed/0.8.³⁴ Allowed amounts were truncated at $50 000 per claim and $100 000 per month.

Behavioral health diagnoses were obtained via medical claims data and the program electronic medical record system, and they were defined as International Statistical Classification of Diseases, Tenth Revision (ICD-10) codes between F01 and F99 among the first 3 diagnosis codes. The last diagnosis in the index month was used as the index diagnosis.

Behavioral Health Care

Behavioral health care was defined using a list of 15 Current Procedural Terminology codes (eTable 1 in Supplement 1), which could occur in any service category. Behavioral health–related claims were defined as any medical claim with a behavioral health diagnosis, regardless of whether it was provided by a behavioral health specialist. Service categories were classified as behavioral health visits, nonbehavioral health office visits, emergency department visits, hospital outpatient visits, hospital inpatient visits, and other visits using place of service codes (eTable 2 in Supplement 1), which were not available for 1 employer.

High-Cost Medical Conditions

Medical claims codes were used to define 10 high-cost medical conditions frequently associated with mental health difficulties (eg, hypertension, chronic pain) (eTable 3 in Supplement 1). Medical conditions were chosen based on a literature review of physical health conditions previously associated with behavioral health disorders. Binary indicator variables were created to denote whether each condition was present at any time during the study period. Conditions with 200 or more participants in each match group were included in the analysis.

Medical risk scores were calculated using the Health and Human Services Hierarchical Condition Categories (HHS-HCC) risk adjustment model,³⁵ with factor weights from the 2019 platinum metal tier. Risk scores were calculated over the 12 months up to and including the patient’s index date.

Program Costs

All program costs for members insured on the health plan were included in the study, including costs for members who did not actually use the program. Program costs were added to monthly medical allowed amounts (eMethods in Supplement 1).

Statistical Analysis

Behavioral Health Care Use

Behavioral health care use rates were computed on both monthly and yearly bases using all 429 781 health plan members. The use rate was defined as the number of individuals with at least 1 behavioral health visit during the period divided by the total number of eligible members in the same period. Inverse probability weighting was used to weight each employer population equally. The difference in use between the year before and the year after program launch was tested using a generalized linear logistic regression model. Standard errors were calculated using a sandwich estimator to reflect the nesting of observations within participants and employers.

Cohort Matching

Nearest neighbor matching was used to match users to nonusers at up to a 1:2 ratio using the following criteria: employer (exact match), behavioral health diagnosis category (prior to matching, ICD-10 F codes were collapsed into 4 categories of mood, anxiety, substance use disorder, or other; exact match), log-transformed medical risk score, age, sex, and date of diagnosis. A caliper of standardized mean difference of 0.1 was applied to medical risk scores to prioritize high-quality matches. Weights were used to adjust for participants who only received 1 match.

Medical Spending Analysis

The study was designed from a program-year perspective and included data from the 12 months prior to each participant’s index date (defined as the date they first received a mental health diagnosis or first interacted with the program) and up to 12 months after their index date, ending at 1 year after launch. Program outcomes were calculated on a per member per month (PMPM) basis using a difference-in-differences model as follows: PMPM Spending = β₀ + β₁ × Program Group + β₂ × Phase +β₃ × Program Group × Phase + β_4…9 × Employer + ϵ, where Program Group represents a binary indicator variable representing membership in the program group, Phase represents whether the month of spend occurred before or after the index diagnosis, and Employer represents a series of 6 binary indicator variables representing the seven employers included in the study, with the seventh employer serving as the reference category. The difference-in-differences is captured by the Program Group × Phase interaction, representing the association between program participation and changes in PMPM spending after the index diagnosis. Employer-level clustering was modeled as a fixed effect.³⁶

A generalized linear model with survey weights was used to accommodate clustering within individuals and match strata. Residual bootstrapping was used to estimate 95% CIs.

Return on Investment

Return on investment (ROI) was calculated for the first year after program launch. ROI was defined as gross spending differences divided by total program costs: ROI Multiple = 1 + (Net Difference-in-Differences × Member Months Post Index Date/Total Program Costs). In this equation, a 1 is added to the multiple to reflect total program costs. An ROI multiple of 1.0 represents full cost offset (total costs are equal between groups), with values greater than 1.0 indicating net-positive ROI. Service categories were analyzed by fitting separate models to the spending accrued for each category, further stratified by whether spending was associated with a behavioral health diagnosis.

Moderation analysis was conducted by fitting separate models with Program Group × Phase interactions for each candidate moderator. To determine whether any conditions were especially responsive to behavioral health treatment, we controlled the Program × Risk Score × Phase interaction and re-estimated the high-cost condition moderators. Statistically significant interactions were interpreted as physical health conditions differentially affected by behavioral health care.

Two-tailed P < .05 was considered statistically significant. Data were analyzed using R, version 4.4 (R Project for Statistical Computing), and the R software package survey.³⁷

Results

Participant Characteristics

The sample comprised 13 990 participants: 4907 of 4949 (99.1%) program group members were matched to 9083 control participants. Their mean (SD) age was 37 (13.2) years, 65.5% of participants were female and 34.5% were male, and index diagnoses consisted primarily of anxiety (73.3%) and mood (21.0%) disorders. As presented in the Table, the program group and the control group were similar on all matching variables (eTable 4 in Supplement 1 presents sample statistics for the full eligible population). However, small but statistically significant differences were present for sex (program group, female participants: 2.1 percentage points; P = .04) and months post index date (control group: 0.16 months; P = .02). Assessment of the parallel trends assumption found that match groups had nearly identical medical spending trends prior to their index date, implying suitability of the data for difference-in-differences modeling (eFigure 2 in Supplement 1).

Table. Participant Characteristics^a.

Characteristic	Control group (n = 9083)	Program group (n = 4907)	Adjusted P value^b
Matching
Age, mean (SD), y	37.3 (13.9)	37.4 (11.8)	>.99
Sex assigned at birth
Male	3197 (35.2)	1626 (33.1)	.03
Female	5886 (64.8)	3281 (66.9)	.03
Mental health diagnosis
Mood disorder	1907 (21.0)	1031 (21.0)	>.99
Anxiety disorder	6662 (73.3)	3598 (73.3)
Substance use disorder	257 (2.8)	139 (2.8)
Other	257 (2.8)	139 (2.8)
HHS-HCC medical spending risk score, mean (SD)	1.32 (3.25)	1.32 (3.25)	>.99
Time after index date, mo	6.68 (3.42)	6.52 (3.47)	.02
Additional
High-cost condition
Asthma or COPD	759 (8.4)	439 (8.9)	.36
Diabetes	558 (6.1)	293 (6.0)	.82
Hypertension	1938 (21.3)	814 (16.6)	<.001
Pregnancy	413 (4.5)	253 (5.2)	.17
Chronic pain	2293 (25.2)	1075 (21.9)	<.001
Gastrointestinal conditions	1540 (17.0)	613 (12.5)	<.001
Any	4962 (54.6)	2368 (48.3)	<.001
Any specialty behavioral health treatment	3031 (33.4)	4907 (100)	<.001
Specialty behavioral health session count, among those with ≥1 visit, mean (SD)	7.2 (8.3)	4.7 (4.8)	<.001

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Abbreviations: COPD, chronic obstructive pulmonary disease; HHS-HCC, Health and Human Services Hierarchical Condition Categories.

^{^a}

Unless specified otherwise, values are presented as No. (%) of participants.

^{^b}

P values were calculated using the Benjamini-Hochberg adjustment with the false discovery rate set to 5%. Weighting was applied to account for individuals who did not receive a 2:1 match.

Behavioral Health Care Use

The behavioral health care use rate increased from 3.8% per month before launch to 5.5% in the 2 months after program launch and remained steady at that higher rate over the rest of the program year, despite little change in the proportion of individuals accessing care via the health plan alone (Figure 1A). Annual use was 8.3% in the year prior to launch and increased to 12.2% in the year after launch, corresponding to an absolute annual increase of 3.9 percentage points (95% CI, 2.8-5.1 percentage points) and a relative increase of 47.0% (Figure 1B). Among the 33.4% of individuals who used behavioral health care in the matched control group, the mean session count was 2.5 sessions higher compared to the program group (P < .001).

Figure 1. — A, The vertical dashed line represents the program launch; the horizontal dashed line represents the mean prelaunch use. B, Error bars denote 95% CIs.

Medical Spending

Total Medical Spending

Difference-in-differences modeling results are shown in Figure 2. The program and control groups had similar monthly spending in the year prior to a behavioral health diagnosis (program group: β = $12 [95% CI, −$27 to $50]). In the year following a diagnosis, control group spending increased by $361 PMPM (77.8%) but program group spending only increased by $197 (net difference-in-differences, −$164 PMPM [95% CI, −$228 to −$100 PMPM]), corresponding to $1070 in savings per participant during the first program year. The difference-in-differences represented a 29.6% gross decrease excluding program costs (95% CI, 25.0%-35.0%) (eTable 5 in Supplement 1), a 13.5% net decrease factoring in program costs (95% CI, 8.0%-19.0%) (eTable 6 in Supplement 1), and an ROI of 1.9 times the cost of the program (95% CI, 1.6-2.2) (ie, medical claims costs were reduced by $190 for every $100 invested in the mental health benefit).

In an exploratory analysis of monthly difference-in-differences (eResults, eFigure 3, and eTable 7 in Supplement 1), a larger than average group difference was observed in the index month, followed by smaller monthly group differences that increased beginning in month 5 after the index date. Savings for the first full participant year were estimated to be −$1796 (95% CI, −$2734 to −$859).

Pharmacy Spending and Service Categories

Changes in pharmacy spending were not significantly different between the program and control groups (β = −$2 [95% CI, −$32 to $27]). Mental and physical health services spending was significantly different between groups (Figure 3 and eTable 8 in Supplement 1). The program group had a larger increase in total behavioral health spending relative to the control group (β = $40 [95% CI, $20-$60]) but a decrease in total physical health spending (β = −$206 [95% CI, −$265 to −$147]). The pattern of services spending within the mental and physical health categories also differed. In the program group, behavioral health–related spending was significantly higher for behavioral health specialists (β = $163 [95% CI, $157-$169]) but lower for office visits (β = −$21 [95% CI, −$23 to −$20]), emergency department visits (β = −$13 [95% CI, −$15 to −$10]), and hospital visits (outpatient: β = −$34 [95% CI, −$43 to −$26]; and inpatient: β = −$28 [95% CI, −$37 to −$19]). Differences in physical health spending were associated primarily with hospital visits (outpatient: β = −$73 [95% CI, −$103 to −$43]; and inpatient β = −$71 [95% CI, −$101 to −$41]), with smaller but significant differences in emergency department spending (β = −$12 [95% CI, −$21 to −$4]) and office visits (β = −$13 [95% CI, −$21 to −$4]).

Figure 3. — BH costs and physical health costs are presented overall (A and C) and by service category (B and D). In panel C, BH specialist care could occur in any setting; other categories represent care occurring in those settings from a non-BH specialist. PMPM indicates per member per month.

Moderation Analysis

Savings were moderated by HHS-HCC medical risk score, improving by −$281 (95% CI, −$336 to −$226) (Figure 4A) for each 1-SD increase in the log risk score. Savings were also significantly higher for men than women (difference-in-differences, −$139 [95% CI, −$256 to −$22]) and higher for older participants (difference-in-differences, −$6 per year older [95% CI, −$10 to −$2]). Savings of −$372 (95% CI, −$465 to −$278) for members with any high-cost condition were observed, with significant moderation effects for all conditions besides pregnancy (Figure 4B). Figure 4C illustrates the moderation effects for each high-cost condition plotted at the average HHS-HCC risk score for participants with that condition. Statistically significant differences from the risk score trend line represent excess savings. After adjusting for the Program × Risk Score × Phase interaction, significant excess savings were found for chronic pain (−$219 [95% CI, −$350 to −$88]), hypertension (−$247 [95% CI, −$393 to −$101]), and gastrointestinal conditions (−$219 [95% CI, −$378 to −$60]) (eTables 9-11 in Supplement 1).

Sensitivity Analysis

A sensitivity analysis examined the possibility for an association between the timing of the COVID-19 pandemic and study results. Most participants began program engagement between June 2021 and July 2022 (eFigure 4 in Supplement 1). Program outcomes appeared unrelated to key pandemic events (eFigure 5 in Supplement 1), suggesting that the pandemic did not influence the study results. An analysis examining the design choice to exclude the 11.6% of participants who crossed over between the program and control groups found that the crossover effect would have to be $866 PMPM in favor of the control group—directionally opposite the main effect of the study by a factor of 5 times—to change the primary conclusion of the study (eFigure 6 in Supplement 1).

Discussion

In this study, implementation of an employer-sponsored behavioral health benefit was associated with a 47.0% relative increase in behavioral health care use, a 29.6% gross decrease in total costs, and a 13.5% net decrease in total costs. Consistent with prior research,^1,2 medical costs in the control group increased by 77.8% ($361 PMPM) in the year following a behavioral health diagnosis. Costs in the program group also increased, but by $164 PMPM less than the control group, in line with actuarial expectations of the effect of behavioral health care reported previously.¹ Together, this resulted in an ROI of 1.9 times the costs in the year following program implementation.

Behavioral health care use increased by 47.0% relative to the year before launch. This increase was likely attributable, in part, to the program attracting new users to behavioral health care, because use via health plan clinicians did not decrease after launch. The increase in use contrasts with prior research on a different employer-sponsored program, which showed no increase in behavioral health care utilization.³¹ The increase in use observed here is likely due to reduced financial barriers to care because sessions were subsidized by the employer and to a modern web-based platform that made it easier to choose clinicians, schedule appointments within days, and attend treatment sessions. Higher financial and operational barriers to care among control group participants could have delayed their care until their behavioral health difficulties progressed and required more intensive treatment. Indeed, among the 33.4% of individuals in the control group who used specialty care, the average number of sessions was 2.5 more than program group participants. Lower session use in the program group may also reflect a more consistent use of measurement-based care by program clinicians, in which symptom measures administered throughout therapy enable clinicians to more quickly course-correct patients for a successful outcome and more quickly identify when patients meet their treatment goals.³⁸

Behavioral health spending in the program group in this study shifted toward more effective services earlier in the care continuum. Behavioral health spending was higher overall in the program group than in the control group (in particular for behavioral health specialists) but was lower for office visits, emergency department visits, and hospital settings. Prior research has shown that office visits, emergency department visits, and hospital visits are characterized by infrequent or brief treatment episodes rather than comprehensive courses of care and, consequently, are less clinically effective than behavioral health care.^39,40,41 Earlier use of the program, shown previously to have large clinical effect sizes,³² may have prevented behavioral health conditions from escalating to the point that they required more time and cost-intensive settings such as the emergency department or inpatient stays. Consistent with this interpretation, an exploratory analysis revealed above-average spending reductions in the index month, when medical activity was highest, followed by smaller but increasing reductions after 4 months, enough time to complete a standard course of care. These savings likely result from (1) diverting care to behavioral health services earlier in the continuum and (2) improved mental health reducing the need for other health care services.

The program group’s increased behavioral health care costs were more than offset by decreased physical health care costs. Behavioral health care may reduce physical health care costs in both indirect and direct ways. For some people, behavioral health care may indirectly improve physical health by addressing barriers to engaging in preventive or disease management practices, such as exercise or medication adherence. Research shows that unaddressed behavioral health disorders are associated with higher rates of preventable hospitalizations in patients with diabetes,^42,43 asthma, and chronic obstructive pulmonary disease,⁴³ supporting the notion that behavioral health can impair medical disorder care adherence practices.

For other people, behavioral health care may directly improve physical health. In this study, 3 physical health conditions (chronic pain, hypertension, and gastrointestinal conditions) had substantial savings that could not be explained by their level of medical risk alone. These conditions are similar in that they are biologically linked to stress, anxiety, and depression, which amplify the body’s pain response,^44,45 increase blood pressure,^46,47 and exacerbate ulcers and irritable bowel syndrome.^48,49 These and other conditions that can be affected directly by a behavioral health condition may deliver outsized cost savings when a high-quality behavioral health program is readily available.^{21,22,23,50,51} Overall, these findings support the enhancement of access to behavioral health care among individuals with high-cost conditions as a strategy not only for cost containment but also for improving both mental and physical health treatment outcomes.

Limitations

The study has several limitations. The nonrandomized design may have introduced selection bias, because control group patients may have initiated care for different reasons than program participants, possibly inflating program savings estimates. We mitigated this by using medical risk scores at the time of behavioral health diagnosis to account for new medical information at care initiation. Although a validated algorithm for identifying chronic conditions would enhance the generalizability of our results, we selected diagnostic codes most relevant to mental health conditions because common algorithms focus on severe populations and explicitly exclude psychological factors (eg, chronic pain).⁵² Relatedly, the small but statistically significant prevalence differences for several high-cost conditions could indicate residual bias from the matching process or may reflect an association between program use and subsequent condition prevalence during the study year. However, the close match on overall risk scores and the use of difference-in-differences modeling should mitigate any bias.

Conclusions

In this cohort study, a large-scale, employer-sponsored behavioral health program with fast access to psychotherapy and medication management was associated with a substantial relative increase in behavioral health care use and notable net decreases in total health care cost. These findings suggest that providing subsidized access to high-quality, measurement-based behavioral health care may be an effective cost containment strategy, particularly in populations with high-cost medical conditions.

Supplement 1.

eMethods

eResults

eTable 1. Behavioral Health Care Current Procedural Terminology (CPT) Codes

eTable 2. Place of Service Code Groupings

eTable 3. Code Groupings for High-Cost Conditions and Exclusionary Conditions

eTable 4. Sample Statistics: Full Eligible Population

eTable 5. Gross Difference-in-Differences Modeling

eTable 6. Net Difference-in-Differences Modeling Results

eTable 7. Monthly Difference-in-Differences

eTable 8. Changes in Medical Service Costs in the Year Following a Mental Health Diagnosis

eTable 9. Effect Modification by Demographics

eTable 10. Effect Modification by High-Cost Condition

eTable 11. Behavioral Health Responsiveness: Risk-Adjusted Moderation by High-Cost Condition

eFigure 1. Participant Flow Diagram

eFigure 2. Assessment of Parallel Trends in the Year Prior to the Index Date

eFigure 3. Net Monthly Spending Pre and Post Index Date

eFigure 4. Index Date by Month for Program Users

eFigure 5. Gross Difference-in-Differences by Employer and Program Launch Date

eFigure 6. Crossover Effect Size Needed to Alter Study Conclusion

jamanetwopen-e2457834-s001.pdf^{(1.1MB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2457834-s002.pdf^{(14.9KB, pdf)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods

eResults

eTable 1. Behavioral Health Care Current Procedural Terminology (CPT) Codes

eTable 2. Place of Service Code Groupings

eTable 3. Code Groupings for High-Cost Conditions and Exclusionary Conditions

eTable 4. Sample Statistics: Full Eligible Population

eTable 5. Gross Difference-in-Differences Modeling

eTable 6. Net Difference-in-Differences Modeling Results

eTable 7. Monthly Difference-in-Differences

eTable 8. Changes in Medical Service Costs in the Year Following a Mental Health Diagnosis

eTable 9. Effect Modification by Demographics

eTable 10. Effect Modification by High-Cost Condition

eTable 11. Behavioral Health Responsiveness: Risk-Adjusted Moderation by High-Cost Condition

eFigure 1. Participant Flow Diagram

eFigure 2. Assessment of Parallel Trends in the Year Prior to the Index Date

eFigure 3. Net Monthly Spending Pre and Post Index Date

eFigure 4. Index Date by Month for Program Users

eFigure 5. Gross Difference-in-Differences by Employer and Program Launch Date

eFigure 6. Crossover Effect Size Needed to Alter Study Conclusion

jamanetwopen-e2457834-s001.pdf^{(1.1MB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2457834-s002.pdf^{(14.9KB, pdf)}

[zoi241617r1] 1.Melek SP, Norris DT, Paulua J. Economic impact of integrated medical-behavioral healthcare. Milliman American Psychiatric Association Report. Milliman Inc. January 2018. Accessed August 30, 2023. https://www.psychiatry.org/File%20Library/Psychiatrists/Practice/Professional-Topics/Integrated-Care/Milliman-Report-Economic-Impact-Integrated-Implications-Psychiatry.pdf

[zoi241617r2] 2.Davenport S, Gray T, Melek S. How do individuals with behavioral health conditions contribute to physical and total healthcare spending? Milliman Inc. August 13, 2020. Accessed April 15, 2024. https://www.milliman.com/-/media/milliman/pdfs/articles/milliman-high-cost-patient-study-2020.ashx

[zoi241617r3] 3.Bellon J, Quinlan C, Taylor B, Nemecek D, Borden E, Needs P. Association of outpatient behavioral health treatment with medical and pharmacy costs in the first 27 months following a new behavioral health diagnosis in the US. JAMA Netw Open. 2022;5(12):e2244644. doi: 10.1001/jamanetworkopen.2022.44644 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r4] 4.Manderscheid R, Kathol R. Fostering sustainable, integrated medical and behavioral health services in medical settings. Ann Intern Med. 2014;160(1):61-65. doi: 10.7326/M13-1693 [DOI] [PubMed] [Google Scholar]

[zoi241617r5] 5.Sporinova B, Manns B, Tonelli M, et al. Association of mental health disorders with health care utilization and costs among adults with chronic disease. JAMA Netw Open. 2019;2(8):e199910. doi: 10.1001/jamanetworkopen.2019.9910 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r6] 6.Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37-43. doi: 10.1016/S0140-6736(12)60240-2 [DOI] [PubMed] [Google Scholar]

[zoi241617r7] 7.Arnow BA, Blasey CM, Lee J, et al. Relationships among depression, chronic pain, chronic disabling pain, and medical costs. Psychiatr Serv. 2009;60(3):344-350. doi: 10.1176/ps.2009.60.3.344 [DOI] [PubMed] [Google Scholar]

[zoi241617r8] 8.Baumeister H, Knecht A, Hutter N. Direct and indirect costs in persons with chronic back pain and comorbid mental disorders: a systematic review. J Psychosom Res. 2012;73(2):79-85. doi: 10.1016/j.jpsychores.2012.05.008 [DOI] [PubMed] [Google Scholar]

[zoi241617r9] 9.De Hert M, Detraux J, Vancampfort D. The intriguing relationship between coronary heart disease and mental disorders. Dialogues Clin Neurosci. 2018;20(1):31-40. doi: 10.31887/DCNS.2018.20.1/mdehert [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r10] 10.Cohen BE, Edmondson D, Kronish IM. State of the art review: depression, stress, anxiety, and cardiovascular disease. Am J Hypertens. 2015;28(11):1295-1302. doi: 10.1093/ajh/hpv047 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r11] 11.Charlson FJ, Moran AE, Freedman G, et al. The contribution of major depression to the global burden of ischemic heart disease: a comparative risk assessment. BMC Med. 2013;11:250. doi: 10.1186/1741-7015-11-250 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r12] 12.Musselman DL, Betan E, Larsen H, Phillips LS. Relationship of depression to diabetes types 1 and 2: epidemiology, biology, and treatment. Biol Psychiatry. 2003;54(3):317-329. doi: 10.1016/S0006-3223(03)00569-9 [DOI] [PubMed] [Google Scholar]

[zoi241617r13] 13.Avila C, Holloway AC, Hahn MK, et al. An overview of links between obesity and mental health. Curr Obes Rep. 2015;4(3):303-310. doi: 10.1007/s13679-015-0164-9 [DOI] [PubMed] [Google Scholar]

[zoi241617r14] 14.Sobocki P, Ekman M, Agren H, et al. Resource use and costs associated with patients treated for depression in primary care. Eur J Health Econ. 2007;8(1):67-76. doi: 10.1007/s10198-006-0008-3 [DOI] [PubMed] [Google Scholar]

[zoi241617r15] 15.Prince M, Patel V, Saxena S, et al. No health without mental health. Lancet. 2007;370(9590):859-877. doi: 10.1016/S0140-6736(07)61238-0 [DOI] [PubMed] [Google Scholar]

[zoi241617r16] 16.Bautista LE, Vera-Cala LM, Colombo C, Smith P. Symptoms of depression and anxiety and adherence to antihypertensive medication. Am J Hypertens. 2012;25(4):505-511. doi: 10.1038/ajh.2011.256 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r17] 17.Wu JR, Lennie TA, Dekker RL, Biddle MJ, Moser DK. Medication adherence, depressive symptoms, and cardiac event-free survival in patients with heart failure. J Card Fail. 2013;19(5):317-324. doi: 10.1016/j.cardfail.2013.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r18] 18.Kronish IM, Rieckmann N, Halm EA, et al. Persistent depression affects adherence to secondary prevention behaviors after acute coronary syndromes. J Gen Intern Med. 2006;21(11):1178-1183. doi: 10.1111/j.1525-1497.2006.00586.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r19] 19.Roshanaei-Moghaddam B, Katon WJ, Russo J. The longitudinal effects of depression on physical activity. Gen Hosp Psychiatry. 2009;31(4):306-315. doi: 10.1016/j.genhosppsych.2009.04.002 [DOI] [PubMed] [Google Scholar]

[zoi241617r20] 20.Kandola A, Ashdown-Franks G, Hendrikse J, Sabiston CM, Stubbs B. Physical activity and depression: towards understanding the antidepressant mechanisms of physical activity. Neurosci Biobehav Rev. 2019;107:525-539. doi: 10.1016/j.neubiorev.2019.09.040 [DOI] [PubMed] [Google Scholar]

[zoi241617r21] 21.Gandy M, Pang STY, Scott AJ, et al. Internet-delivered cognitive and behavioural based interventions for adults with chronic pain: a systematic review and meta-analysis of randomized controlled trials. Pain. 2022;163(10):e1041-e1053. doi: 10.1097/j.pain.0000000000002606 [DOI] [PubMed] [Google Scholar]

[zoi241617r22] 22.Niknejad B, Bolier R, Henderson CR Jr, et al. Association between psychological interventions and chronic pain outcomes in older adults: a systematic review and meta-analysis. JAMA Intern Med. 2018;178(6):830-839. doi: 10.1001/jamainternmed.2018.0756 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r23] 23.Morley S, Eccleston C, Williams A. Systematic review and meta-analysis of randomized controlled trials of cognitive behaviour therapy and behaviour therapy for chronic pain in adults, excluding headache. Pain. 1999;80(1-2):1-13. doi: 10.1016/S0304-3959(98)00255-3 [DOI] [PubMed] [Google Scholar]

[zoi241617r24] 24.Borges G, Aguilar-Gaxiola S, Andrade L, et al. Twelve-month mental health service use in six countries of the Americas: a regional report from the World Mental Health Surveys. Epidemiol Psychiatr Sci. 2019;29:e53. doi: 10.1017/S2045796019000477 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r25] 25.Andrade LH, Alonso J, Mneimneh Z, et al. Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychol Med. 2014;44(6):1303-1317. doi: 10.1017/S0033291713001943 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r26] 26.Priester MA, Browne T, Iachini A, Clone S, DeHart D, Seay KD. Treatment access barriers and disparities among individuals with co-occurring mental health and substance use disorders: an integrative literature review. J Subst Abuse Treat. 2016;61:47-59. doi: 10.1016/j.jsat.2015.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r27] 27.Alegría M, Bijl RV, Lin E, Walters EE, Kessler RC. Income differences in persons seeking outpatient treatment for mental disorders: a comparison of the United States with Ontario and the Netherlands. Arch Gen Psychiatry. 2000;57(4):383-391. doi: 10.1001/archpsyc.57.4.383 [DOI] [PubMed] [Google Scholar]

[zoi241617r28] 28.de Oliveira C, Cho E, Kavelaars R, Jamieson M, Bao B, Rehm J. Economic analyses of mental health and substance use interventions in the workplace: a systematic literature review and narrative synthesis. Lancet Psychiatry. 2020;7(10):893-910. doi: 10.1016/S2215-0366(20)30145-0 [DOI] [PubMed] [Google Scholar]

[zoi241617r29] 29.Lerner D, Rodday AM, Cohen JT, Rogers WH. A systematic review of the evidence concerning the economic impact of employee-focused health promotion and wellness programs. J Occup Environ Med. 2013;55(2):209-222. doi: 10.1097/JOM.0b013e3182728d3c [DOI] [PubMed] [Google Scholar]

[zoi241617r30] 30.Penev T, Zhao S, Lee JL, Chen CE, Metcalfe L, Ozminkowski RJ. The impact of a workforce mental health program on employer medical plan spend: an application of cost efficiency measurement for mental health care. Popul Health Manag. 2023;26(1):60-71. doi: 10.1089/pop.2022.0240 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi241617r31] 31.Maeng D, Cornell AE, Nasra GS. Employer-sponsored behavioral health program impacts on care utilization and cost. Am J Manag Care. 2021;27(8):334-339. doi: 10.37765/ajmc.2021.88724 [DOI] [PubMed] [Google Scholar]

[zoi241617r32] 32.Bondar J, Babich Morrow C, Gueorguieva R, et al. Clinical and financial outcomes associated with a workplace mental health program before and during the COVID-19 pandemic. JAMA Netw Open. 2022;5(6):e2216349. doi: 10.1001/jamanetworkopen.2022.16349 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Return on Investment of Enhanced Behavioral Health Services

Matt Hawrilenko, PhD

Casey Smolka, MAAA

Emily Ward, PhD

Geetu Ambwani, MSc

Millard Brown, MD

Anita Mohandas, MSc, MHP

Martin Paulus, PhD

John Krystal, MD

Adam Chekroud, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design

Program

Participants

Data Sources

Behavioral Health Care

High-Cost Medical Conditions

Program Costs

Statistical Analysis

Behavioral Health Care Use

Cohort Matching

Medical Spending Analysis

Return on Investment

Results

Participant Characteristics

Table. Participant Characteristicsa.

Behavioral Health Care Use

Figure 1. Behavioral Health Care Use Rates in the Year Before and After Program Launch.

Medical Spending

Total Medical Spending

Figure 2. Changes in Medical Spending After a Behavioral Health Diagnosis.

Pharmacy Spending and Service Categories

Figure 3. Medical Spending in the Year Following a Behavioral Health (BH) Diagnosis.

Moderation Analysis

Figure 4. Medical Spending Differences by Medical Risk Score and High-Cost Condition.

Sensitivity Analysis

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table. Participant Characteristics^a.