Impact of an Intensive Outpatient Clinic Model on Health Care Utilization and Cost

ABSTRACT

Objective: To evaluate the impact of an intensive outpatient clinic (IOC) model on health care utilization and costs among medically and behaviorally complex adults enrolled in a Medicaid managed care organization.

Study Design: A retrospective cohort study using interrupted time series (ITS) and Bayesian structural time series (BSTS) analysis was conducted to examine changes in health care use and costs before and after IOC enrollment.

Methods: Observational claims data from 2015 to 2023 were analyzed for 150 adults who enrolled in an IOC between 2017 and 2023. Outcomes included total, medical, and pharmacy costs; hospital admissions; and emergency department (ED) visits, tracked 24 months before and after enrollment.

Results: Mean annual health care spending decreased by 43% 2 years after IOC enrollment. ITS analysis showed significant monthly reductions in total spending (–$142; 95% CI, –$242 to –$42; P < .01) and medical costs (–$129; 95% CI, –$229 to –$29; P = .01). Hospital admissions and ED visits declined by 36 (95% CI, –58 to –13; P < .01) and 25 (95% CI, –35 to –16; P < .01) per 1000 patients per month, respectively. BSTS estimates showed a 6% decrease in total spending, a 10% decrease in medical costs, and a 28% decrease in ED visits.

Conclusions: The IOC was associated with significant reductions in health care costs and utilization among medically and behaviorally complex adults. Scalable interventions such as an IOC may help improve care coordination and reduce costs for high-risk Medicaid populations.

Am J Manag Care. 2026;32(7):In Press

Takeaway Points

Implementing an intensive outpatient clinic model for medically and behaviorally complex Medicaid patients led to substantial improvements in health care utilization and costs.

• Total health care spending dropped by 17% in the first year and 43% by the second year post enrollment, with medical costs showing similar reductions.
• Hospital admissions and emergency department visits fell significantly, reflecting reduced acute care reliance.
• Pharmacy costs rose slightly, possibly due to better medication adherence and improved chronic disease management.

The US spends more on health care than any other country, with spending rising steadily over time.¹ In 2015, total health care spending in the US reached $3.2 trillion ($10,586 per person), and by 2023, it had grown to $4.9 trillion ($14,570 per person), accounting for 17.6% of the US gross domestic product.^2,3 Although rising health care spending may reflect expanded services or improved care, evidence suggests that 25% to 50% of medical spending in the US may be attributable to waste, overuse, and excessive administrative costs.^4,5

Escalating health care costs increase patient financial burden. As health care costs grow faster than individual incomes, patients experience not only an absolute increase in their medical expenses but also an increase in the percentage of their income spent on medical care in the form of higher out-of-pocket expenses and premiums, which can lead to disparities in access, care delays, and inappropriate utilization.⁴

To control health care costs and improve care, state and federal agencies have implemented various care and delivery reforms. For instance, CMS has promoted hospital price transparency and launched the CMS Innovation Center to test innovative payment models.^6,7 In 2014, Maryland introduced an All-Payer Model, which required all public and private payers to pay the same established rate for any hospital service^8,9; building on the success of the All-Payer Model, the Maryland Total Cost of Care Model showed a $689 million reduction in total Medicare spending between 2019 and 2021.¹⁰ Massachusetts enacted cost-containment legislation in 2012 and has adopted global payment models.¹¹ Similarly, the patient-centered medical home (PCMH) has been adopted by many primary care clinics as a new delivery model to achieve the Triple Aim of improving the health of populations, reducing the cost of care, and improving the patient experience.¹²

Despite these innovations, it remains unclear which models are most effective and beneficial for patients. To that end, an academic medical center developed a new care model called the intensive outpatient clinic (IOC) to serve medically and behaviorally complex, high-cost patients enrolled in Medicaid. This study evaluates the impact of the IOC model on health care utilization and costs.

METHODS

Intensive Outpatient Clinic

In January 2017, an academic health system established the IOC to serve medically and behaviorally complex patients enrolled in its Medicaid managed care organization. The IOC consisted of a multidisciplinary team (ie, primary care providers, care coordinators, psychiatrists, behavioral health therapists, ancillary clinic staff, and dedicated home health nurses) offering comprehensive patient risk assessment. Potential patients were identified through claims data, chart reviews, or referrals from clinicians. The team conducted outreach and provided extended visits (60-90 minutes) to assess medical, behavioral, and social needs, including housing instability, food insecurity, and social isolation. Physical limitations, cognitive impairment, social support, substance use, and medication adherence were regularly evaluated to address barriers to care. Transportation assistance was provided for patients requiring it, and the IOC maintained a food pantry and clothing closet for those in need. Additional details on clinic operations have been previously published.¹³

Patient Selection

Members of a Medicaid population in Utah aged 18 to 63 years were eligible for IOC enrollment if they demonstrated both medical and behavioral complexity alongside high health care cost and utilization. Using claims data from the University of Utah Health Plans, patients were identified based on the following criteria for the 12 months prior to enrollment: (1) at least 2 emergency department (ED) visits, (2) at least 2 hospital admissions, (3) at least 2 comorbid conditions or any mental disorder diagnosis, and (4) total medical cost (medical plus pharmacy) of $30,000 or more. Costs were calculated from a payer perspective using reimbursed amounts. ED visits and hospital admissions were identified by CMS Place of Service Codes (eg, code 11), revenue codes (eg, 0450), Current Procedural Terminology (CPT) codes (eg, 99281), and admission dates. Comorbidities and mental disorders were identified using International Statistical Classification of Diseases, Tenth Revision (ICD-10) codes. Relevant comorbidities included diabetes, heart failure, liver disease, chronic obstructive pulmonary disease, asthma, cancer, substance use disorders (ICD-10: F10-F19), and chronic pain. Mental disorders (ICD-10: F01-F99, excluding F10-F19) included depression, schizophrenia, mood disorders, anxiety, behavioral and emotional disorders, personality disorders, and other psychiatric conditions. The first 4 diagnosis codes were used to identify comorbid conditions. Once patients agreed to participate, IOC staff conducted evaluations and documented enrollment, disenrollment, and follow-up visits in the IOC roster. This study was approved by the University of Utah Institutional Review Board (IRB_00119331) and adhered to ethical research standards.

Study Design and Data

This retrospective cohort study utilized the IOC registry and linked claims data from the University of Utah Health Plans. Patients were matched to their corresponding medical, pharmacy, and eligibility files using medical record numbers and health plan identifier. The eligibility file provided demographic information such as birth year, enrollment start/end date, and sex. The medical file contained visit dates, reimbursed amounts, place of service, revenue code, CPT codes, provider specialty, and admission dates. The pharmacy file included prescription dates, reimbursed amounts, and national drug codes. Continuous Medicaid enrollment was assessed using the eligibility file. The study period spanned from January 1, 2015, to December 31, 2023.

Outcomes

To compare the health care costs and utilization before and after IOC enrollment, we analyzed data from 24 months pre- and up to 24 months post enrollment. Monthly total costs were calculated as the sum of medical and pharmacy reimbursed costs. Hospitalizations and ED visit rates (per 1000 patients) were derived from medical claims. Inpatient admissions were identified using place of service (21), bill type, and revenue codes (100-219), but they were excluded if no overnight stay occurred. ED visits were identified using place of service (23), revenue codes (450-459, 981), and CPT codes (99281-99285). Costs were inflation adjusted to 2023 US$ using the personal health care expenditures component of the National Health Expenditure Accounts.^14,15 The analysis included patients with at least 12 months of continuous enrollment pre-IOC and 6 months of continuous enrollment post IOC.

Covariates

The main predictor variable was a dummy variable representing the implementation of an IOC (pre-IOC = 0; post IOC = 1). Regression adjusted for mean age, percentage male, and mean Elixhauser Comorbidity Index (ECI), calculated from ICD-10 diagnosis codes 1 year prior to IOC enrollment. Age was based on birth year and enrollment date. Race/ethnicity was excluded from the analysis because 69% of patients were non-Hispanic White.

Time Periods

Pre- and postintervention periods were defined based on enrollment date for each patient. The preintervention period included 24 months prior to enrollment, and the postintervention period covered up to 24 months after enrollment. In total, 48 months of data were analyzed to ensure sufficient power to detect small changes in outcomes before and after the IOC intervention.

Statistical Approach

Descriptive statistics summarized patient characteristics at IOC enrollment using mean and SD for continuous variables and frequency and percentages for categorical variables. To evaluate changes in health care costs and utilization before and after IOC implementation, we conducted an interrupted time series (ITS) analysis with Newey-West SEs to account for autocorrelation and potential heteroskedasticity. This approach is frequently used to examine statistical changes in an outcome before and after implementation of an intervention.¹⁶ Autocorrelation was tested up to the 5 lags, and autocorrelation was present at lag 1 in the pharmacy cost, inpatient admissions, and ED visits. These autocorrelations were accounted for in the first-order autoregressive models. Changes in the number of enrolled patients over time were incorporated into the ITS. The Cumby-Huizinga test for autocorrelation was completed on all pre- and postdata comparisons.¹⁷ To estimate the impact of the IOC, Bayesian structural time series (BSTS) analysis was used.¹⁸ Because the IOC model targeted patients with both physical and behavioral health conditions, we conducted stratified ITS analyses for mental and physical health conditions, as described in the Patient Selection subsection earlier. This analysis excluded prescription costs and visits unrelated to these conditions. A sensitivity analysis using only pre–COVID-19 pandemic data (before March 1, 2020) was conducted. A P value less than or equal to .05 was interpreted as statistically significant. Analyses were performed using Stata 18 (StataCorp LLC) and R 4.3.0 (R Foundation for Statistical Computing).

RESULTS

Of the 259 individuals enrolled in the IOC between January 2017 and January 2023, 109 were excluded because they were either less than 6 months post enrollment, dually eligible for Medicare and Medicaid, or younger than 18 years, resulting in 150 included individuals (eAppendix Figure 1 [eAppendix available at ajmc.com]).

The mean (SD) age at IOC enrollment was 45 (11) years; 66% were female, and 69% were non-Hispanic White. The mean (SD) ECI score at IOC enrollment was 8.1 (3.2) (Table 1).

Before enrollment, the mean (SD) total health care cost per patient was $6113 ($1043) per month—$5197 ($1013) in medical costs and $916 ($198) in pharmacy costs. Annual per-patient spending was a mean (SD) of $73,354 ($59,291). On average, patients experienced 1.03 hospital admissions and 1.16 ED visits per month 12 months before IOC enrollment. In the first year post enrollment, mean total cost decreased to $60,905, a 17% reduction from the prior year (P = .11). In the second year, total spending fell further to $41,909—a 43% reduction compared with 12 months preenrollment (P < .001). Although medical spending declined by $12,771 in the first year post enrollment, pharmacy spending showed a modest increase (from $10,991 to $11,313; P = .91) (Table 2).

Trends analysis using ITS showed a significant decrease in total monthly spending post IOC enrollment (–$142; 95% CI, –$242 to –$42; P < .01). The BSTS analysis estimated mean monthly total spending post intervention at $5370 with the IOC intervention vs $5760 without it, representing a 6% reduction (Figure 1).

Medical costs followed a similar pattern. ITS showed a monthly decrease of $129 (95% CI, –$229 to –$29; P = .01), and BSTS estimated a 10% reduction: $4390 with IOC vs $4860 without (eAppendix Figure 2). In contrast, the trend of pharmacy costs post IOC showed a slight increase. Although a decreasing trend was observed in the second year post IOC (coefficient = –$13), it was not statistically significant (95% CI, –$29 to $4; P = .13). BSTS analysis estimated monthly pharmacy costs at $981 with IOC and $896 without, a 10% increase, possibly reflecting improved medication adherence (eAppendix Figure 3).

Utilization metrics also declined. Hospital admissions fell by 36 per 1000 patients monthly (95% CI, –57.7 to –13.2; P < .01), and ED visits dropped by 25 per 1000 patients (95% CI, –34.7 to –15.6; P < .01). BSTS analysis estimated monthly hospital admissions at 850 per 1000 with the IOC intervention and 920 per 1000 without (6% decrease). ED visits were projected at 720 per 1000 with IOC and 1000 per 1000 without, indicating a 28% decrease (Figure 2 and Figure 3).

The Cumby-Huizinga test revealed insignificant autocorrelations in total cost, medical cost, hospital admissions, and ED visits. However, pharmacy costs had significant autocorrelation in the first lag (P < .01), prompting adjustment in the pharmacy cost ITS regression. Furthermore, sensitivity analysis conducted using only prepandemic data (before March 1, 2020) yielded consistent results that mirrored the main findings.

Stratified ITS analyses showed a $63 per month decline in mental health–related costs (P = .09) (eAppendix Figure 4) and a $67 per month decline in physical health–related costs (P = .03) (eAppendix Figure 5) after IOC enrollment, with the former not statistically significant due to an upward trend late in follow-up.

DISCUSSION

This study assessed the impact of an IOC model on health care utilization and cost for medically and behaviorally complex Medicaid patients. Our findings showed that the IOC model was associated with a 43% reduction in total health care spending over 2 years (equivalent to $31,445 per patient) and decreases in hospital admissions and ED visits. Although these findings suggest meaningful improvements in utilization patterns, the results should be interpreted as associative rather than causative given the absence of a concurrent comparison group.

Although definitions of medically and behaviorally complex patients vary,¹⁹ it is increasingly recognized that these individuals drive disproportionate health care spending and require well-coordinated multidisciplinary care. Multiple care delivery and payment models have been piloted in the US to address this population. One example is CareMore, which integrates behavioral health care, patient engagement, and multidisciplinary teams within its managed care plans across 6 states. Evaluation of the model demonstrated a 20% reduction in hospital admissions and a 23% decrease in bed days among its high-risk patients.²⁰ In a randomized trial in Tennessee, CareMore’s intervention reduced total spending by 37% and significantly lowered inpatient admissions compared with usual care.²¹ Additionally, in CareMore’s pilot Brain Health program for 46 members with dementia or Alzheimer disease, ED visits for behavioral symptoms dropped from 14% to 0% and ED visits for falls decreased from 40% to 2% after 6 months.²⁰ Our findings align closely with these results; however, unlike the dementia-focused program, we observed ED visit reductions in a broader population of medically and behaviorally complex adults. This contrast may reflect the IOC model’s stronger emphasis on behavioral health integration and social determinant interventions tailored for a high-need Medicaid population compared with the older adult Medicare population in CareMore’s evaluation.

A systematic review by Delaney et al of IOC-like programs for high-need, high-cost patients found consistent reductions in health care use and costs across multiple studies.²² Several included studies were single-arm studies. For instance, Frankel and colleagues reported health improvements in 44 of 52 patients,²³ and Lynch et al observed a decrease in hospitalizations and ED visits 6 months post enrollment.²⁴ Schuttner et al also observed a significant decrease in the odds of all-cause ED visits 12 months post enrollment in an IOC-like program.²⁵ Studies by Mosquera et al²⁶ and Horn et al,²⁷ which were examined in the systematic review and included control groups, reported similar associations between intervention participation and reductions in health care utilization and costs.

Several mechanisms may explain the associations observed in our study. The IOC model integrates medical, behavioral, and social care, factors known to influence health outcomes. Social determinants such as housing, food insecurity, and transportation are addressed through the clinic’s support services, which include transportation assistance, a food pantry, and a clothing closet. Behavioral health support is robust, including on-site psychiatric consultation and therapy. This holistic model may help reduce acute exacerbations of chronic illnesses and prevent avoidable ED visits and hospitalizations. The slight increase in pharmacy costs may reflect improved medication adherence. When patients receive regular follow-up and care coordination, they may be more likely to consistently take prescribed medications, which can, in turn, reduce future medical costs.

A previous quasi-experimental study found that social needs navigation for high utilizers may have a modest positive effect on utilization.²⁸ However, a systematic review of 28 studies investigating the effectiveness of social needs screening and interventions revealed mixed results regarding their impact on health care utilization and cost.²⁹ Patient counseling and engagement, as well as helping patients track health-related goals, may also play a role in improved treatment adherence.²¹ Moreover, a multidisciplinary team working together to provide comprehensive patient assessment may help eliminate redundancies and inefficiencies in the system, which in turn could be associated with lower health care costs and utilization.

Studies have evaluated different care settings (such as the home, community, ED, and primary care clinics) where these innovative models are implemented. Chang et al found that interventions based in the ED and primary care settings may be linked to reduced health care spending, although specific outcomes varied depending on program design.³⁰ Similarly, a review of PCMH models reported improvements in patient experience and clinical outcomes, but there was no evidence of overall cost savings due to heterogeneity across studies.³¹ Compared with many PCMH interventions, our IOC model places greater emphasis on integrated on-site behavioral health care and social supports in the form of transportation, food, and housing, which may have contributed to the observed reductions in health care costs and utilization.

Rising health care costs have driven efforts to improve care quality while reducing spending.³² Value-based care models such as the IOC, which targets high-risk patients, could help achieve this goal while not compromising on patient well-being. States such as Massachusetts and Maryland have implemented reforms to curb spending. Massachusetts’ 2012 cost control law introduced global payments and an oversight commission, helping reduce annual health care spending growth to 3.4% and saving consumers $7.2 billion by 2018.^11,33,34 Maryland’s Total Cost of Care Model, launched in 2019, cut hospital spending by 6.6% and reduced total health care costs by $781 million over 3 years.^10,35

Limitations

This study has several limitations. First, the IOC model focused exclusively on Medicaid patients aged 18 to 63 years. Hence, findings may not be generalizable to pediatric, older, or privately insured populations. Second, the study sample was predominantly non-Hispanic White, limiting the ability to assess the intervention’s effectiveness in more racially or ethnically diverse populations. Third, this was a single-arm study without a control group. Because patients in the IOC program represented the most expensive and clinically complex patients, a matched control group could not be identified from the payer claims data. The absence of a comparator group limits the ability to draw causal inferences, and some of the observed improvements may be partly due to regression to the mean.²⁴ Nonetheless, the consistency of our findings with those from other studies provides supportive external validity. As additional context, statewide data show that Utah’s Medicaid program covers approximately 10% of the state’s population. Prior reports suggest that roughly 70% of adult Medicaid enrollees in Utah are female, 14% have 3 or more chronic conditions, and 8% are adults aged 19 to 64 years.^36,37 Although recent race/ethnicity enrollment data are not publicly available, census estimates show that approximately 75% of Utah’s population is non-Hispanic White.³⁸ These figures emphasize the heterogenous nature of Utah’s broader Medicaid population and that our high-cost, high-need IOC cohort likely differs in meaningful ways from the average Medicaid enrollee in the state. Fourth, comorbidities were identified using diagnosis codes from claims data rather than prescription records, which may underestimate patient comorbidities. Finally, only patients with continuous enrollment were included in the analysis, which may introduce some selection bias.

CONCLUSIONS

Our study suggests that the IOC model may be associated with lower overall costs and health care utilization for high-cost, high-need patients in a Medicaid managed care organization. These findings should be confirmed through future studies using matched control groups or randomized controlled trials.

Author Affiliations: Department of Physical Therapy and Athletic Training, College of Health (JKi), Department of Population Health Sciences, School of Medicine (JKe, AF, EMO), Department of Pharmacotherapy, College of Pharmacy (KCB-U) University of Utah, Salt Lake City, UT; University of Utah Medical Group Population Health (SH, PW), Salt Lake City, UT; Spokane Regional Health District Data Center (BLS-T), Spokane, WA; Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, US Department of Veterans Affairs Health System (AF), Salt Lake City, UT.

Source of Funding: None.

Author Disclosures: Mr Ben-Umeh is employed as a research assistant at the University of Utah College of Pharmacy. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JKi, KCB-U, JKe, BLS-T, AF, EMO, PW); acquisition of data (JKi, EMO); analysis and interpretation of data (JKi, KCB-U, JKe, PW); drafting of the manuscript (JKi, KCB-U, SH, BLS-T, PW); critical revision of the manuscript for important intellectual content (JKi, KCB-U, SH, JKe, BLS-T, AF, EMO, PW); statistical analysis (JKi); provision of patients or study materials (AF, PW); administrative, technical, or logistic support (KCB-U, SH, BLS-T, EMO); and supervision (EMO, PW).

Address Correspondence to: Jaewhan Kim, PhD, Department of Physical Therapy and Athletic Training, University of Utah, 520 Wakara Way, Salt Lake City, UT 84108. Email: jaewhan.kim@utah.edu.

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