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Published in final edited form as: Acad Pediatr. 2025 Apr 22;25(6):102839. doi: 10.1016/j.acap.2025.102839 Search in PMC Search in PubMed View in NLM Catalog Add to search Evaluating Clinical Decision Supports to Improve Adolescent Depression Screening and Management in Pediatric Primary Care Nathaniel J Glasser Nathaniel J Glasser , MD, MPP 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; Find articles by Nathaniel J Glasser 1 , Camron Shirkhodaie Camron Shirkhodaie , MD 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; Find articles by Camron Shirkhodaie 1 , Zachary C Newman Zachary C Newman , BS 2 University of Chicago Pritzker School of Medicine, Chicago, Illinois; Find articles by Zachary C Newman 2 , Joanne Wang Joanne Wang , BA 3 Washington University in St. Louis School of Medicine, St. Louis, Missouri; Find articles by Joanne Wang 3 , Mengqi Zhu Mengqi Zhu , MS 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; Find articles by Mengqi Zhu 1 , James W Mitchell James W Mitchell , MD, FAAP 4 University of Chicago Medicine, Department of Pediatrics, Chicago, Illinois; Find articles by James W Mitchell 4 , Erin Staab Erin Staab , MPH 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; Find articles by Erin Staab 1 , Stephanie Lichtor Stephanie Lichtor , MD 5 Boston Children’s Hospital, Boston, Massachusetts; 6 Harvard Medical School, Boston, Massachusettes; Find articles by Stephanie Lichtor 5, 6 , Neda Laiteerapong Neda Laiteerapong , MD, MS 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; 7 University of Chicago Medicine, Department of Psychiatry, Chicago, Illinois Find articles by Neda Laiteerapong 1, 7 Author information Article notes Copyright and License information 1 University of Chicago Medicine, Department of Medicine, Chicago, Illinois; 2 University of Chicago Pritzker School of Medicine, Chicago, Illinois; 3 Washington University in St. Louis School of Medicine, St. Louis, Missouri; 4 University of Chicago Medicine, Department of Pediatrics, Chicago, Illinois; 5 Boston Children’s Hospital, Boston, Massachusetts; 6 Harvard Medical School, Boston, Massachusettes; 7 University of Chicago Medicine, Department of Psychiatry, Chicago, Illinois ✉ Corresponding Author: Nathaniel J Glasser, MD, MPP, 5841 South Maryland Avenue, MC 2007, Chicago, IL 60637; [email protected] Issue date 2025 Aug. PMC Copyright notice PMCID: PMC12317833 NIHMSID: NIHMS2076703 PMID: 40274223 The publisher's version of this article is available at Acad Pediatr Abstract Objectives: To examine how depression screening rates changed after implementation of electronic health record (EHR) clinical decision support tools and medical assistant (MA)-led depression screening at an outpatient pediatric practice. Methods: We assessed changes in depression screening rates at an urban academic pediatric clinic between September 2016-December 2020 using interrupted time series analysis. During this time, we implemented (1) EHR clinical decision support tools for depression screening and management (November 2017) and (2) training of MAs to screen for depression (July 2019). Results: Over the study period, 3,963 patients received care in the pediatric clinic. Their mean age was 14.9 years (SD, 2.6) and about half were female (n=2,011, 51%). The majority were Black/African American (n=2,852, 72%) and had private insurance (n=2,860, 72%). Depression screening rates increased from 3% to >80%. Pre-intervention, depression screening rates were not increasing (0.9% per month, 95% CI: −0.3 to 2.1%; p=0.15). After implementing EHR clinical decision support tools, there was a 15.6% (95% CI: 2.5–28.6%, p=0.02) increase in the screening rate. Also, MA-led screening was associated with a 24.6% (95% CI: 9.9–39.2%, p=0.002) screening rate increase. Conclusions: This study demonstrates that EHR clinical decision support tools and MA-led screening are likely to increase adolescent depression screening and management in pediatric clinics. Keywords: Electronic Health Record, Quality Improvement, Adolescent Health, Depression, Pediatric Primary Care Introduction Depression currently affects approximately 15–18% of adolescents annually, though many cases of adolescent depression go unidentified. 1 , 2 While adolescent depression is “eminently treatable,” 3 its prevalence is rising, 1 , 2 especially after the COVID-19 pandemic. 4 Adolescence is a crucial time period for social, emotional, and cognitive development that can be substantially compromised by depression. 5 ) Untreated depression in adolescents may lead to impairment in psychosocial functioning, low educational attainment, and suicide, which is a leading cause of death in U.S. children age 12–17. 6 , 7 Risk factors for depression include low birthweight, family history of depression, interpersonal and family stressors, exposure to early life stressors, low educational and socioeconomic standing, traumatic brain injury, chronic illness, and substance use. 5 The first step in managing adolescent depression is diagnosing the condition. 8 The American Academy of Pediatrics (AAP) and United States Preventive Services Task Force (USPSTF) currently recommend that primary care clinicians screen all patients aged 12 years and older for depression, as well as establish referral and collaborative arrangements with mental health resources to provide care for those who screen positive. 9 – 11 However, current efforts to screen and manage adolescents for depression often fail to meet these recommendations, 12 with estimates of recent screening rates ranging as low as 0.2% of clinic visits. 13 , 14 Of those adolescents who are screened and ultimately diagnosed with depression, only about 40% receive some form of mental health services. 15 , 16 Given substandard screening and care for adolescent depression, prior studies have evaluated strategies for increasing depression screening in pediatric clinics. 12 , 17 – 23 Efforts that have leveraged electronic health records (EHR) to improve screening and management have shown particular promise. Yet evaluations of such EHR-based programs have typically only assessed programs’ associations with either depression screening or depression referral and/or management, but rarely both. 19 – 21 Additionally, only one published evaluation of an EHR-based depression screening effort used a quasi-experimental method to assess intervention causality. 16 Further, many of these studies do not report the sociodemographic characteristics of their studies’ sample populations, 19 and those that do report sociodemographic characteristics primarily included majorities of White patients in their populations. 16 We identified only one study that enrolled a sample that was not majority Non-Hispanic White. 21 Recently published guidance on “Principles for Primary Care Screening in the Context of Population Health” updated prior recommendations for pediatric primary care-based screening initiatives. 24 Previously, screening efforts were targeted based on (a) the health problem’s perceived importance; (b) the availability of an adequate screening tool; and (c) the existence of clinically and cost-effective treatments. The authors of the updated guidance suggest that screening programs should also aim to improve population health through screening by considering the epidemiology of – and availability of efficacious treatments for – target conditions across diverse populations. Given the high prevalence or mental health conditions and relative scarcity of appropriate treatment options in areas of higher deprivation, dedicated efforts to assess the effect of evidence-based screening and management strategies for mental health issues such as depression in these high deprivation areas are essential. 25 , 26 The objective of this study was to evaluate the effect of a continuous quality improvement project aimed at improving pediatric depression screening and management using a quasi-experimental design within a low income, low resourced area. We hypothesized that EHR clinical decision support tools and medical assistant (MA)-led screenings would increase adolescent depression screening rates within the pediatric clinic. We also evaluated associations of screening completion with sociodemographic characteristics to evaluate the distribution of screening across our sample population. Methods Setting This quality improvement study occurred at an academic general pediatric clinic located in the South Side of Chicago and used a quasi-experimental interrupted time series design. Chicago has marked disparities in income, education, and access to primary and mental health care. For example, the ratio of psychiatrists to residents is 1/24 th on the Far South side (3:100,0000) compared to North and Central Chicago (72:100,000). 27 , 28 Furthermore, while the rate of child psychiatrists increased in Illinois from 2007 to 2016, child psychiatrists were less likely to work in neighborhoods with lower levels of education and income. 29 The clinic’s catchment area also serves multiple primary and mental health care professional shortage areas. 27 The study was determined to be quality improvement research by the University of Chicago Medical Center and did not need to be reviewed by the institutional review board. The study followed the STROBE reporting guidelines During the study period (2016–2020), the clinic was staffed by about 7 attending general pediatricians (MDs) and 1 advance practice nurse (APN), which totaled 8 full-time equivalent primary clinicians. Residents do not work in the clinic. In addition, 3 licensed practical nurses, 6 medical assistants, and 1 general medical social worker worked at the clinic. Depression screening – Pre-intervention Prior to our intervention, clinicians (MDs and APNs) assessed patients for depression if there was clinical suspicion. Clinicians used various tools to assess mood symptoms, ranging from clinical interview questions to brief screening tools, like the Patient Health Questionnaire (PHQ)-2 or PHQ-9A (Adolescent version). 16 In 2016, we implemented an electronic form in the EHR (Epic, Verona, WI) that they could fill out to document depression screening. Depression screening – Intervention In November 2017, clinical informatics tools in the EHR were turned on (Intervention 1) to encourage depression screening and management in adolescents age 12 years or older, consistent with AAP/USPSTF guidance. 10 These tools included a “health maintenance topic,” also known as a “care gap,” for annual depression screening in patients without a history of depression who did not have an EHR-documented screening in the prior 12 months ( Figure S1 ). In addition, a Depression Screening “Best Practice Advisory” (BPA) became visible to clinicians for any patient who was due for the health maintenance topic ( Figure S2 ). This BPA included a link to the PHQ-2/9A. In the PHQ-2, patients are asked to rate how frequently in the past two weeks they experienced (1) “Little interest or pleasure in doing things” and (2) “Feeling down, depressed, or hopeless.” 30 Each item is scored on a scale from 0 (not at all) to 3 (nearly every day) for a possible total score ranging from 0–6. If patients score 3 or higher on the PHQ-2 (positive screen), they were then reflexively prompted to answer 7 additional questions from the PHQ-9A, adopted from Johnson et al to assess symptoms. 14 Responses are summed, with a maximum possible score of 27. Numeric scores on the PHQ-9A are used to categorize the severity of depression symptoms as either minimal (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), or severe (20–27). In analyses, those with mild and moderate, moderately severe, and severe symptoms were grouped together for reasons of statistical power. 31 For patients who screened positive for depression (i.e., PHQ-9A score of greater than or equal to 10), a Positive Screen BPA informs clinicians of the positive screen and how to interpret the PHQ score ( Figure S3 ). This BPA also includes a link to a “smart order set” to assist with clinical management ( Figure S4 ). The smart order set includes recommended referral and medication orders. A one-page clinical decision support tool was also made available. The current version of this tool is available in Figure 1 and on our website: https://voices.uchicago.edu/behavioralhealthintegrationprogram/provider-resources/cds/ . Figure 1. Clinical Decision Support Tool. Open in a new tab Clinical Decision Support Tool was available to clinicians electronically. From November 2017 to June 2019, clinicians were primarily responsible for completing depression screening. In July 2019, the clinic hired additional medical assistants (MAs), allowing for the responsibility for screening to be transferred from clinicians to MAs (Intervention 2). MAs gave patients a paper version of the PHQ-2/9A and would ask patients to complete the full PHQ-9A, if the PHQ-2 score was 3 or greater. The primary care clinician was responsible for entering the score into the EHR. In November 2019, the MAs assumed responsibility for entering results into the EHR. All patients aged 12 years and older were given the opportunity to speak to their clinician alone, without the presence of a parent or caregiver, frequently at towards the end of their visit, to allow them to disclose any information they may wish to have kept confidential. Outcomes The primary outcome was the completed depression screening rate at eligible clinic visits. A completed depression screening was indicated by either a completed PHQ-2 or a clinician documenting in the health maintenance topic that depression screening had been done. Secondary outcomes included the rate of positive PHQ-2 screens, PHQ-9A scores, depression symptom severity, and positive response to the 9 th question of the PHQ-9A (which assesses suicidality and frequently requires immediate action if positive). Furthermore, a chart review was conducted to understand management of depression screens with PHQ-9A scores of ≥ 5 (chosen to maximize sensitivity). Chart review was performed by two independent reviewers (JW and NL), who resolved discrepancies through discussion. Most information was contained in structured fields (e.g., past medical history, internal referrals to mental health treatment, medication prescriptions, or medication dose increases); however other information (e.g., patient education and external referrals required review of clinician assessments and plans) was in the free text portion of clinician notes. Data were collected using REDCap (Research Electronic Data Capture; see Figure S5 for REDCap Chart Review Data Extraction Tool). “Any treatment” was defined as evidence on chart review of patient education; therapy, psychiatry, or an intensive outpatient program referral; medication initiation; or medication dose increase. Patients with positive scores were considered to receive “any management” if they received “any treatment” and/or had their symptoms of depression re-measured with a PHQ-9A. “Treatment” and “management” were assessed in aggregate to facilitate assessment of changes in trends before and after interventions. Data analysis Data were obtained from the Center for Research Informatics’ Clinical Research Data Warehouse and included all patients who visited the pediatrics clinic at least one time during the study period. Descriptive statistics were used to summarize patient sociodemographic characteristics, monthly screening rates, screening results, and management of patients with positive screens. Interrupted time series analyses were used to compare screening rates over time, before and after interventions. Logistic regression was performed to evaluate associations of patient-level sociodemographic characteristics including age, sex, race, insurance type (private or governmental) with receipt of depression screening and treatment across the entire study period. An additional sensitivity analysis also included number of visits as a predictor variable in the logistic regression. Two-sided p-values less than .05 were considered significant. Statistical analyses were performed using R (version 4.1.0). Results Overall, there were 3,963 unique patients who received care in the pediatric clinic and were due for a depression screening from September 2016 to December 2020. Table 1 shows the sociodemographic characteristics of patients, stratified by whether or not they received depression screening during the study period. The mean age of participants was 14.9 years (standard deviation, SD, 2.6); about half were female (n=2,011, 51%). The majority were Black/African American (n=2,852, 72%) and had private insurance (n=2,860, 72%). The mean number of visits per patient during the study period was 2.0 (SD, 1.3). Table 1. Baseline sociodemographic characteristics of study participants Overall, N = 3,963 1 Not screened, N = 967 1 Screened, N = 2,996 1 p-value 2 Age, years 14.93 (2.58) 15.52 (3.29) 14.74 (2.27) <0.001 Sex 0.30 Female 2,011 (51%) 477 (49%) 1,534 (51%) Male 1,952 (49%) 490 (51%) 1,462 (49%) Race 0.20 Black/African American 2,852 (72%) 713 (74%) 2,139 (71%) Other/unknown 337 (8.5%) 83 (8.6%) 254 (8.5%) White 774 (20%) 171 (18%) 603 (20%) Insurance type 0.09 Government 1,093 (28%) 287 (30%) 806 (27%) Private 2,860 (72%) 676 (70%) 2,184 (73%) Missing 10 4 6 Number of visits 1.99 (1.34) 1.63 (1.29) 2.11 (1.34) <0.001 Open in a new tab 1 Mean (SD); n (%) 2 Wilcoxon rank sum test; Pearson’s Chi-squared test Depression Screening Rates In total, patients were eligible for screening in 7,884 clinic visits during the study period. Among these 7,884 clinic visits, depression screenings were completed in about half (53%, n=4,155) of visits. Figure 2 presents the changes in depression screening rates over time and in relation to implementation of the two interventions. At baseline, the depression screening rate was about 3% and increased non-significantly by 0.9 percentage points per month (95% CI: −0.3 to 2.1%; p=0.15). After implementing the EHR clinical decision support tools in November 2017, the rate of depression screening increased initially by 15.6% (95% CI: 2.5 to 28.6%, p=0.02) and then increased non-significantly by 0.4% per month (95% CI: −1.1 to 1.8; p=0.61). About 26 months later, when MAs began screening for depression, the screening rate increased by 24.6% (95% CI: 9.9 to 39.2; p<0.01) initially and reached a highest screening rate of 84% in August 2019 but then declined by 3.0% per month afterwards (95% CI: −6.0 to −0.1; p=0.04). Eight months later, at the start of the COVID-19 pandemic, the screening rate decreased by 15.7% (95% CI: −33.6 to 2.3; p=0.09). Screening rates then resumed rising by 5.4% monthly (95% CI: 2.0–8.9%; p<0.01) compared to the pre-COVID trend and went back to 81% screening rate by the end of our study period. Figure 2. Pediatric Clinic Visit Depression Screening, September 2016 to December 2020. Open in a new tab Changes in depression screening over time, in relation to Interventions 1 & 2. Table 2 presents the results of multivariate models assessing associations of screening completion with patient sociodemographic characteristics. Results of these multivariate models revealed no significant differences in depression screening rates by sex or race (comparing Black/African American vs. non-Black/African American race/ethnicity). Older age was associated with a lower rate of screening ( aOR = 0.89, 95% confidence interval, CI, [0.86, 0.91]) while having private insurance (compared to public or no insurance) was associated with a higher screening rate ( aOR = 1.20, 95% CI [1.01, 1.42]). However, when number of visits was added to the model, patients’ insurance type was no longer significantly associated (aOR = 1.18, 95% CI [0.99, 1.39]) with screening rates while higher number of visits was associated with greater screening completion ( aOR = 1.47, 95% CI [1.36, 1.59]). Table 2. Coefficients of estimates of screening likelihood comparing sociodemographic characteristics in multivariable regression analysis Model 1 1 Model 2 2 aOR (95% CI) aOR (95% CI) Age 0.89 (0.86, 0.91) *** 0.87 (0.85, 0.90) *** Male sex 0.92 (0.79, 1.06) 0.95 (0.91, 1.10) Other/Unknown vs. Black race 0.94 (0.72, 1.23) 0.93 (0.71, 1.23) White vs. Black race 1.06 (0.87, 1.29) 1.04 (0.85, 1.28) Private vs. Government insurance 1.20 (1.01, 1.42) * 1.18 (0.99, 1.39) Number of Visits 1.47 (1.36, 1.59) *** Open in a new tab Significance codes: * 0.05, ** 0.01, ***0.001 1 Multivariate model did not include the number of visits. 2 Multivariate model did include the number of visits PHQ Scores Figure 3 depicts the results of PHQ screening. Among visits with a documented PHQ-2 score, 10% (n=425) visits had PHQ-2 scores of 3 or greater and the majority of these visits (92%, n=391/425) had a PHQ-9A completed. Among completed PHQ-9As, 27% (n=107/391) had scores consistent with mild depression (5–9), 39% (n=153/391) had moderate (10–14), and 34% (n=131/391) had moderately severe or severe (15+) symptoms. Suicidal ideation was reported by 17% (n=67/393) of adolescents: (“several days”: n=37; “more than half the days”: n=19; “nearly every day”: n=11). Figure 3. Pediatric Clinic Visit Depression Screening Results, September 2016 to December 2020. Open in a new tab Results of pediatric depression screens during the study period as indicated in the chart by the clinician. Depression Management Figure S6 & Table S1 depict management of depression in patients, stratified by symptom severity ( Figure S6 ) and in relation to intervention implementation ( Table S1 ). Among adolescents who screened positive for depression and had a documented PHQ-9A (n=391), 94% (n=367) received some form of treatment defined by patient education, antidepressant medication initiation or dose increase, or a behavioral health referral. An additional 13 patients had re-measurement of PHQ scores at a subsequent visit, for a total of 97% (n=380) receiving either treatment or a re-assessment of symptoms. Referrals to therapy, psychiatry, or an intensive outpatient program were placed in just over half (n=217, 55%) of patients. Patients with moderately severe or severe depressions symptoms (PHQ-9A 15+) were more likely (aOR = 2.8, 95% CI [1.8–4.6]) to be referred for therapy services (psychiatry, therapy, or an intensive outpatient program) than individuals with mild or moderate symptoms. After implementation of the EHR clinical decision support tools, rates of depression management for adolescents were higher. Prior to the intervention, only 3 patients had a PHQ-9A of 10 or greater, and 1 of those patients (33%) was referred for treatment. After implementation of the EHR clinical decision support tools and MA-led depression screening, more adolescents received management of depression symptoms (post-EHR clinical decision support tools implementation: 58/59 (98%) and MA-led screening, 320/329 (97%)). Treatment rates were near 100% for both interventions (post-EHR- clinical decision support tools, 58/59 (98%) and MA-led screening, 307/329 (93%) and follow-up scores increased from 0/3 pre-intervention to 21/59 (36%) after EHR-based tools and 169/329 (51%) MA-led screening. Discussion Nationwide, screening rates and treatment for adolescent depression in primary care are well below the targets recommended by the AAP and USPSTF. 14 Findings from this quality improvement study reveal significant increases in adolescent depression screening rates in a pediatric primary care clinic after implementation of two key interventions: (1) EHR clinical decision support tools and (2) MA-led screening. While not a randomized clinical trial, the quasi-experimental interrupted time series study design allows us to attribute differences in pre- and post-intervention screening rates to the interventions themselves. 32 Screening rates also increased in a linear fashion with time, indicating that these interventions were able to maintain effectiveness over the four-year study period. Our use of EHR clinical decision support tools and MA-led depression screening were associated with significant improvements in the depression screening rate from about 3% to over 80%. These strategies have been explored previously in adult primary care by our team and other pediatric practices with demonstrated similar rates of success. 16 , 18 , 19 , 33 , 34 However, previous studies of depression screening in pediatric populations have been mainly descriptive, leaving debate as to whether these strategies led to the improvement in screening rates. 17 , 35 By using this quasi-experimental design, we found that implementing EHR clinical decision support tools is likely to lead to a sustained increase in screening rates among pediatric patients. Furthermore, our analysis suggests that MA-led depression screening is likely to at least transiently increase screening rates, with possible decline over time. Prior studies corroborate this pattern of decline, which is frequently attributed to the waning of the initial excitement that immediately follows intervention implementation. 36 However, we are unable to fully evaluate this trend due to the likely impact of the COVID lockdown. Screening rates declined significantly in March 2020, likely due to disruptions to workflows caused by the onset of the COVID-19 pandemic. MAs did not perform triage for telehealth visits, which rapidly increased in prevalence with respect to office visits during the earlier months of the pandemic. Screening rates subsequently leveled out and returned to pre-pandemic levels. While the pandemic was highly disruptive to clinical care, these findings reinforce the importance of interventions that are robust to challenges to workflow. One opportunity to mitigate disruptions that can impact the screening rate could be to increase the frequency of clinic visits, since in our study, more clinic visits were associated with higher screening rates. Because there is strong evidence of disparities in depression treatment, it was important to explore how our interventions affected minoritized populations. Importantly, our analyses did not reveal differences in screening rates by race or sex, suggesting that these system-level interventions may not lead to disparities. One caveat is that insurance was associated with screening rates except for in models that adjust for number of visits. Prior evidence has suggested that being insured is associated with higher outpatient health service utilization even more than it is with inpatient care. 37 Thus expanded primary care-based screening efforts may disproportionality help patients with insurance, primarily by virtue of such patients being more likely utilize primary care services. We did also find lower rates of screening with higher age. Prior reports have offered conflicting data about associations of screening with adolescent age and revealed differences in screening rates by both sex and race/ethnicity. 25 , 38 Such differences by sex and race/ethnicity were importantly not observed in our analysis. Qualitative inquiry may be indicated to uncover facilitators and barriers. Because the major goal of depression screening is to increase depression detection and subsequent treatment, it was important to explore how patients who screened positive were managed. In our study, nearly all patients who had a PHQ-9A score of 10 or greater received mental health care. Rates of referrals increased over time and patients with more severe symptoms were more likely to be referred to mental health providers. Based on updated depression management guidance at the time of the intervention, our clinical decision support tools recommended the practice of measurement-based care, i.e., the use of PHQ-9A scores to triage patients, such that patients with moderate (or more severe) depression were recommended mental health referrals. 39 Importantly, symptom severity is not correlated with symptom duration (i.e. more severe symptoms do not reflect a longer duration of symptoms) as early intervention is essential to clinical improvement. We suspect the EHR-embedded clinical decision support led to this observed improvement in management rates for positive depression screens. However, we did not directly examine providers’ use of these tools using interrupted time-series analysis (due to limited sample size of pre-intervention positive screens), limiting our ability to assess the intervention impact on these domains. Limitations Our study had several limitations. Data were obtained from an EHR and, as such, depend on the quality and consistency of EHR data entry. Furthermore, the single-center, single-site nature of this study may limit some of its generalizability to other sites and centers, particularly those using other EHR systems. Last, without follow up PHQ scores, it is difficult to assess the impact of the intervention on depression symptom severity. Nonetheless, findings from this interrupted time series analysis provide compelling evidence that adolescent depression screening rates will likely increase with the implementation of EHR clinical decision support tools and MA-led screening. We additionally found evidence that appropriate referrals for therapy and psychiatry services for those who both screened positive for depression and showed evidence of severe disease likely occurred at higher rates after program implementation. Sustained increases in screening and referral rates after EHR tool implementation suggest a possible virtuous cycle in which clinicians become increasingly familiar and adept with EHR tools over time and thus increasingly able to utilize their benefits. A small backslide in increased rates after training of MAs shows less persistence of this intervention but was confounded by the contemporaneous onset of the COVID-19 pandemic and may be overcome through periodic refreshers. 40 Further efforts to improve screening and care of adolescents for depression may similarly leverage EHR capabilities and medical assistants. Supplementary Material 1 NIHMS2076703-supplement-1.docx (950KB, docx) What’s New. Using interrupted time series analysis, this study provides evidence that electronic health record-based clinical decision support tools and medical assistant-led screenings can be used to improve adolescent depression screening and care in a racially diverse, outpatient pediatrics practice in a mental healthcare professional shortage area. Acknowledgements: Funding/Support: The authors report no funding from outside organizations for the completion of this work. The project used REDCap ( NIH CTSA UL1TR002389 ). Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Conflict of Interest Disclosure: The authors report no financial relationships or conflicts of interest regarding the content herein. References 1. Administration SAaMHS. Key substance use and mental health indicators in the United States: Results from the 2023 National Survey on Drug Use and Health (HHS Publication No. PEP24-07-021, NSDUH Series H-59). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration.2024. [ Google Scholar ] 2. Daly M Prevalence of depression among adolescents in the US from 2009 to 2019: analysis of trends by sex, race/ethnicity, and income. 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