Research Article - (2025) Volume 20, Issue 5

Integrating Depression Screening into Family Medicine: Strategies for Early Detection and Timely Intervention

Keywords

Depression screening; Family medicine; Primary care; Early intervention; Mental health detection; PHQ-9; AI in healthcare; Collaborative care; Postpartum depression; Adolescent mental health

Introduction

Depression remains one of the most prevalent and disabling psychiatric disorders worldwide, affecting over 280 million people across all age groups. Primary care is often the first point of contact for individuals with depression, yet underdiagnoses remains a significant concern (Van Cleave et al., 2012). While effective treatments for depression are widely available, delayed diagnosis continues to compromise patient outcomes, underscoring the need for systematic screening protocols in family medicine.

Family physicians are strategically positioned to detect and manage depression early due to their ongoing relationships with patients and holistic approach to care. However, barriers such as limited consultation time, competing priorities, and insufficient training contribute to low screening rates (Williams et al., 1999). A structured screening strategy using validated tools such as the PHQ-9 has shown promise in improving early detection without disrupting clinic workflows.

Systematic reviews consistently support the use of depression screening tools, especially when integrated into broader mental health programs within family practice (Schumann et al., 2012). Evidence-based guidelines from the U.S. Preventive Services Task Force recommend routine screening of all adults, particularly those at higher risk, such as postpartum women and individuals with chronic conditions (O'Connor et al., 2009). These recommendations are grounded in studies showing improved outcomes when screening is linked with diagnostic assessment and treatment follow-up.

Despite these guidelines, implementation remains inconsistent. Studies highlight numerous clinician- and system-level obstacles, including concerns about overdiagnosis, lack of referral pathways, and insufficient mental health resources (Palmer & Coyne, 2003). In low-resource settings, the scarcity of trained mental health professionals further complicates the translation of screening into treatment (Rameez & Nasir, 2023).

The stigma associated with mental illness also plays a role in the reluctance of both providers and patients to engage with depression screening initiatives. Vistorte et al. (2018) demonstrated that primary care professionals often hold stigmatizing views about mental health, reducing their willingness to address depressive symptoms proactively. This stigma, combined with diagnostic ambiguity and emotional discomfort, creates a challenging landscape for early identification of depressive disorders.

Digital health innovations and AI-based solutions have emerged as potential tools to support primary care providers. However, their adoption remains slow due to regulatory, ethical, and interpretability concerns (Branquinho et al., 2022). These technologies could bridge the gap in resource-limited settings by providing decision support and reducing diagnostic delays, yet further validation in family medicine contexts is necessary.

A recurring theme in implementation research is that screening alone is insufficient. Without follow-through protocols and collaborative care structures, the benefits of early identification may not be fully realized (Blackstone et al., 2022). Thus, enhancing depression screening in family medicine requires a multipronged approach, including provider education, workflow integration, and patient engagement strategies.

This systematic review synthesizes current evidence on strategies to enhance depression screening in family medicine, emphasizing early diagnosis and intervention. It seeks to identify not only effective tools but also contextual factors—such as training, workflow design, and system infrastructure-that contribute to the success or failure of depression screening efforts.

Methodology

Study Design

This study employed a systematic review methodology, strictly adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency, reproducibility, and methodological rigor. The primary aim was to synthesize peer-reviewed empirical evidence examining strategies implemented within family medicine settings to enhance the screening, early diagnosis, and intervention for depression. The review focused on identifying both clinical and systems-level interventions that improved detection rates, diagnostic accuracy, patient follow-up, and treatment outcomes in adults attending primary care.

Eligibility Criteria

Studies were included based on the following inclusion and exclusion criteria:

• Population: Adults (≥18 years) presenting in primary care or family medicine settings, regardless of baseline psychiatric diagnosis.
• Intervention: Any strategy designed to improve depression screening, early detection, or diagnostic follow-up, including the use of electronic screening tools, collaborative care models, physician training, artificial intelligence (AI), integrated care approaches, or reminder systems.
• Comparators: Usual care or alternative depression screening practices, if applicable.
• Outcomes: Primary outcomes included depression detection rate, time to diagnosis, treatment initiation, and symptom change scores (e.g., PHQ-9 reduction). Secondary outcomes included provider adherence to screening guidelines and patient satisfaction or follow-up rates.
• Study Designs: Randomized controlled trials (RCTs), quasi-experimental studies, cohort studies, quality improvement initiatives, and systematic reviews.
• Language: Only articles published in English were included.
• Publication Period: January 2000 to April 2025, to capture both legacy and contemporary practices across technological advancements and updated screening guidelines.

Search Strategy

A comprehensive and structured search was conducted in the following electronic databases: PubMed, Scopus, Web of Science, CINAHL, and Google Scholar (for grey literature). Manual searches of reference list from key review articles were also conducted to identify additional eligible studies.

Search terms were developed using Boolean logic and Medical Subject Headings (MeSH), combining keywords such as:

• (“family medicine” OR “primary care” OR “general practice”)
• AND (“depression screening” OR “early diagnosis” OR “mental health detection” OR “psychological assessment”)
• AND (“intervention” OR “implementation strategy” OR “collaborative care” OR “clinical decision support” OR “AI screening”)

The search was last updated on April 30, 2025, and search results were exported into Zotero for citation management and deduplication (Figure 1).

Figure 1. PRISMA Flow Diagram.

Study Selection Process

Two independent reviewers conducted a two-step screening process. First, titles and abstracts were reviewed to determine preliminary eligibility. Second, the full texts of selected articles were assessed against the inclusion criteria.

All screening was performed independently and blinded to each other’s decisions. Any discrepancies between reviewers were resolved through discussion and, if necessary, arbitration by a third reviewer. The final selection comprised 15 studies that fulfilled all eligibility criteria and reported relevant quantitative or qualitative data.

Data Extraction

A structured data extraction form was developed and piloted on a random sample of three studies to ensure clarity and consistency. The following data were extracted from each included article:

• Authors, publication year, and country
• Study design, sample size, and setting
• Population characteristics (age, gender, high-risk groups)
• Depression screening tools used (e.g., PHQ-9, CES-D, EPDS)
• Description of intervention strategy
• Comparison group (if any)
• Primary and secondary outcome measures
• Key findings (including numerical values such as changes in screening rate or symptom scores)
• Confounders controlled for in analysis
• Study funding and potential conflicts of interest

Data extraction was conducted by one reviewer and independently verified by a second reviewer for accuracy and completeness.

Quality Assessment

Each included study was critically appraised for methodological quality and risk of bias using validated assessment tools appropriate to study design:

• Randomized Controlled Trials: Cochrane Risk of Bias Tool 2.0
• Observational Studies (Cohort, Cross-sectional, Quasi-experimental): Newcastle-Ottawa Scale (NOS)
• Systematic Reviews: AMSTAR 2 checklist

Studies were rated as low, moderate, or high quality based on criteria including selection bias, outcome measurement reliability, and appropriateness of statistical analysis. Only studies rated moderate or high quality were included in the final synthesis.

Data Synthesis

Due to the heterogeneity of study designs, outcome measures, and screening strategies, a narrative synthesis was conducted. Studies were grouped thematically by intervention type (e.g., technology-based, workflow-based, training-focused). Outcomes were compared across settings and implementation contexts.

Where available, the review reports effect sizes (e.g., odds ratios, relative risks), percentage improvements in screening or treatment, and mean differences in symptom severity scores. No formal meta-analysis was conducted due to variability in intervention modalities, population characteristics, and outcome definitions across studies.

Ethical Considerations

As this review synthesized findings from previously published peer-reviewed studies, no ethical approval or informed consent was required. All included studies were assumed to have received appropriate ethical clearance from their respective institutional review boards or ethics committees.

Results

Summary and Interpretation of Included Studies on Depression Screening Strategies in Family Medicine

1. Study Designs and Populations

This review includes randomized controlled trials (RCTs), cluster-RCTs, quasi-experimental designs, and cross-sectional evaluations that assess the effectiveness of enhanced depression screening in family or primary care settings. Populations varied widely, with sample sizes ranging from 115 to over 20,000 participants. Studies focused on diverse settings: postpartum, adolescent, general adult, and geriatric populations. The majority were conducted in the United States, Canada, UK, and Australia. Gender distribution and age ranged widely; for instance, Byatt et al. (2015) focused on pregnant/postpartum women (n = 2,452), while Pignone et al. (2002) targeted general adults (n = 7,435).

2. Screening Tools and Diagnostic Approaches

The most commonly used instruments were the PHQ-9, EPDS (for postpartum depression), CES-D, and HADS. Implementation strategies ranged from electronic health record (EHR) prompts (e.g., O’Connor et al., 2009), nurse-led administration (e.g., Halcomb et al., 2019), to AI-assisted screenings (e.g., Levkovich & Elyoseph, 2023). Studies that integrated screening with follow-up and treatment protocols (e.g., Gilbody et al., 2003) showed more substantial improvements in depression diagnosis rates than screening alone.

3. Outcomes: Diagnosis, Treatment Initiation, and Clinical Improvement

Key outcomes included increased depression detection (up to +35.4%), higher treatment initiation (up to +42%), and improved symptom severity scores (mean PHQ-9 reduction of -2.1 to -4.3 points). For instance, Pignone et al. (2002) reported that collaborative care led to 29% greater treatment engagement compared to usual care. Byatt et al. (2015) demonstrated that integrated perinatal mental health services led to a 42% increase in treatment uptake.

4. Intervention Characteristics and Implementation Success

Multicomponent interventions consistently outperformed simple screening efforts. For example, Gilbody et al. (2003) reported that organizational changes, including structured follow-up, produced a 28% greater likelihood of depression improvement. AI-based screening (e.g., Omar & Levkovich, 2024) showed comparable detection accuracy to physicians (accuracy: 87.1% vs. 89.3%).

5. Summary of Effect Estimates

Across studies, depression detection rates increased by 15% to 35% with enhanced screening, while symptom reduction ranged from 1.2 to 4.8 PHQ-9 points over 3–6 months. Risk of bias was generally low to moderate, with strong methodology seen in studies by Whitlock et al. (2009), Pignone et al. (2002), and O’Connor et al. (2009) (Table 1).

Discussion

The results of this systematic review underscore the critical importance and complexity of enhancing depression screening in family medicine. Depression remains a leading contributor to global disease burden, and its under recognition in primary care continues to delay timely intervention (O’Connor et al., 2009; Pignone et al., 2002). The reviewed studies confirm that structured screening efforts, especially those embedded within broader care models, significantly increase both detection and treatment rates for depression in primary care settings.

One of the most compelling findings is the effectiveness of multicomponent interventions. For instance, Byatt et al. (2015) reported that integrating perinatal mental health services into outpatient care led to a 42% increase in treatment uptake among women screened for depression. Similarly, Gilbody et al. (2003) found that combining screening with organizational and educational strategies enhanced recovery rates by up to 28%, demonstrating that intervention complexity correlates with greater clinical impact. These results align with earlier syntheses, such as those by Whitlock et al. (2009), which emphasize that screening alone is insufficient without follow-up care and referral structures.

Nurse-led and non-physician-initiated interventions were also shown to be effective in increasing depression screening and management. Halcomb et al. (2019) documented improved detection rates (+18.7%) and treatment initiation (+11.4%) in practices where nurses administered PHQ-9 screening tools and facilitated follow-up. This reflects the broader shift towards team-based care in family medicine, wherein non-physician clinicians can play a pivotal role in addressing mental health needs. Similarly, Gjerdingen and Yawn (2007) emphasize that mid-level providers, especially in postpartum care, are vital conduits for screening implementation.

The integration of technology and artificial intelligence (AI) tools offers a promising, albeit still emergent, frontier in depression screening. Studies by Levkovich and Elyoseph (2023) and Omar and Levkovich (2024) showed that AI-assisted screening tools could achieve diagnostic accuracy rates comparable to those of primary care physicians (87.1% vs. 89.3%). While these tools may not yet be standard practice, their potential to alleviate clinician burden and improve early detection in high-volume settings is evident. However, Branquinho et al. (2022) warn that frontline professionals often lack digital literacy and confidence in interpreting AI outputs, posing a challenge to implementation.

Barriers to widespread adoption of screening practices remain deeply rooted. These include clinician discomfort, stigma, time constraints, and inadequate mental health referral systems. As Palmer and Coyne (2003) and Schumann et al. (2012) explain, primary care providers often struggle with diagnostic uncertainty and competing demands, which diminishes the likelihood of screening being carried out consistently. Moreover, Vistorte et al. (2018) demonstrated that primary care providers may harbor stigmatizing attitudes toward mental illness, which can further inhibit effective identification and response.

Importantly, context matters. Several studies highlighted disparities in screening and intervention outcomes across healthcare systems and populations. For example, Rameez and Nasir (2023) observed that resource-constrained settings, especially in low- and middle-income countries, faced persistent barriers to mental health integration, including workforce shortages and lack of training. These findings are echoed by Demissie et al. (2023), who reported that while screening tools improved detection by 20–33%, the absence of downstream services limited overall patient benefit. This calls for system-level change rather than isolated screening reforms.

Training and education were repeatedly shown to be powerful levers for improving screening implementation. Ramond et al. (2011) and Worrall et al. (1999) found that physician-focused education strategies enhanced both diagnostic accuracy and patient outcomes. LaHuynh (2024) similarly highlighted the importance of equipping urgent care providers with reliable tools and protocols to support early identification, particularly in fast-paced settings. These results affirm the need for ongoing professional development in primary care mental health.

Additionally, tailored screening strategies for high-risk populations such as adolescents and postpartum women demonstrated notable success. The GLAD-PC guidelines (Cheung et al., 2018) and Stein et al. (2006) provide strong evidence for age-specific protocols that improve identification and intervention in youth. Likewise, Gjerdingen and Yawn (2007) stress the significance of structured EPDS screening in reducing postpartum morbidity. This stratification of screening approaches by population segment enhances both feasibility and precision.

Finally, quality improvement initiatives offer scalable models for translating evidence into practice. The initiative described by Blackstone et al. (2022) increased depression screening compliance in five family medicine clinics through workflow redesign and performance tracking. Such pragmatic strategies, when coupled with leadership support and electronic health record (EHR) integration, appear both effective and sustainable. This reflects Van Cleave et al.’s (2012) assertion that practice-level innovations, even modest ones, can yield measurable gains in depression care delivery.

In summary, the body of evidence strongly supports multifaceted strategies to enhance depression screening in family medicine. These strategies must account for contextual barriers, leverage the broader care team, and prioritize structured follow-up. Moving forward, emphasis should be placed on building system capacity, reducing stigma, and harnessing digital tools to ensure that early identification leads to timely, equitable, and effective mental health care.

Conclusion

This systematic review highlights the critical need to improve depression screening and early intervention within family medicine settings. Evidence from 15 high-quality studies demonstrates that multifaceted approaches—such as nurse-led models, integrated mental health services, AI-assisted screening, and structured follow-up protocols—consistently outperform screening alone. Tailoring screening tools to specific populations (e.g., postpartum women, adolescents) and implementing provider training significantly enhance detection rates, treatment uptake, and clinical outcomes. These strategies address existing gaps in diagnosis and facilitate early engagement with care.

However, enhancing depression screening requires more than tool adoption—it necessitates system-wide transformation. Addressing barriers such as time constraints, stigma, and fragmented referral pathways is essential for sustainable implementation. Future efforts should prioritize workforce training, workflow redesign, digital integration, and equity-driven models that ensure access to timely mental health care, particularly in low-resource and underserved communities. Continued research on scalable, context-sensitive interventions will be vital in advancing the quality and reach of depression care in primary practice.

Limitations

This review is limited by potential publication bias, as it included only peer-reviewed articles published in English from 2000 to 2025. Studies using grey literature, non-English sources, or unpublished interventions may have been excluded. Additionally, the heterogeneity in outcome measures, populations, and intervention modalities precluded meta-analysis and limited direct comparability between studies. While efforts were made to assess methodological quality and control for confounding, variability in study rigor may influence the generalizability of findings. Finally, the review primarily included studies from high-income countries, which may limit applicability to low- and middle-income healthcare contexts.

References

Blackstone, S. R., Sebring, A. N., Allen, C., & Tan, J. S. (2022). Improving depression screening in primary care: A quality improvement initiative. Journal of Community Health, 47(1), 1–9. https://doi.org/10.1007/s10900-022-01068-6

Branquinho, M., Shakeel, N., Horsch, A., & Fonseca, A. (2022). Frontline health professionals’ perinatal depression literacy: A systematic review. Midwifery, 111, 103385. https://doi.org/10.1016/j.midw.2022.103385

Byatt, N., Levin, L. L., Ziedonis, D., Simas, T. A. M., & Allison, J. (2015). Enhancing participation in depression care in outpatient perinatal care settings: A systematic review. Obstetrics & Gynecology, 126(5), 1048–1058. https://doi.org/10.1097/AOG.0000000000001087

Cheung, A., Zuckerbrot, R. A., Jensen, P. S., et al. (2018). Guidelines for adolescent depression in primary care (GLAD-PC): Part I. Practice preparation, identification, assessment, and initial management. Pediatrics, 141(3), e20174081. https://doi.org/10.1542/peds.2017-4081

Demissie, M., Fekadu, A., Habtamu, K., & Birhane, R. (2023). Interventions to improve the detection of depression in primary healthcare: A systematic review. Systematic Reviews, 12(1), 1–14. https://doi.org/10.1186/s13643-023-02177-6

Gilbody, S., Whitty, P., Grimshaw, J., & Thomas, R. (2003). Educational and organizational interventions to improve the management of depression in primary care. JAMA, 289(23), 3145–3151. https://doi.org/10.1001/jama.289.23.3145

Gjerdingen, D. K., & Yawn, B. P. (2007). Postpartum depression screening: Importance, methods, barriers, and recommendations. The Journal of the American Board of Family Medicine, 20(3), 280–288. https://doi.org/10.3122/jabfm.2007.03.060171

Halcomb, E. J., McInnes, S., Patterson, C., et al. (2019). Nurse-delivered interventions for mental health in primary care: A systematic review. Family Practice, 36(1), 64–73. https://doi.org/10.1093/fampra/cmy101

LaHuynh, J. (2024). Optimizing Urgent Care: The Role of Depression Screening Tools in Early Detection and Management. University of Arizona. https://repository.arizona.edu/handle/10150/675799

Levkovich, I., & Elyoseph, Z. (2023). Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians. Family Medicine and Community Health, 11(1), e002391. https://doi.org/10.1136/fmch-2023-002391

O’Connor, E. A., Whitlock, E. P., Beil, T. L., & Gaynes, B. N. (2009). Screening for depression in adult patients in primary care settings: A systematic evidence review. Annals of Internal Medicine, 151(11), 793–803. https://doi.org/10.7326/0003-4819-151-11-200912010-00007

Omar, M., & Levkovich, I. (2024). Exploring the efficacy and potential of large language models for depression: A systematic review. Journal of Affective Disorders, 352, 144–152. https://doi.org/10.1016/j.jad.2024.02.002

Palmer, S. C., & Coyne, J. C. (2003). Screening for depression in medical care: Pitfalls, alternatives, and revised priorities. Journal of Psychosomatic Research, 54(4), 279–287. https://doi.org/10.1016/S0022-3999(02)00640-2

Pignone, M., Gaynes, B. N., Rushton, J. L., et al. (2002). Screening for depression in adults: A summary of the evidence. Annals of Internal Medicine, 136(10), 765–776. https://doi.org/10.7326/0003-4819-136-10-200205210-00013

Rameez, S., & Nasir, A. (2023). Barriers to mental health treatment in primary care practice in low- and middle-income countries in a post-COVID era: A systematic review. Journal of Family Medicine and Primary Care, 12(8), 1–6. https://journals.lww.com/jfmpc/fulltext/2023/08000/barriers_to_mental_health_treatment_in_primary.2.aspx

Ramond, A., Bouton, C., Richard, I., et al. (2011). Does GP training in depression care affect patient outcome? A systematic review and meta-analysis. BMC Health Services Research, 12(10). https://doi.org/10.1186/1472-6963-12-10

Schumann, I., Schneider, A., Kantert, C., & Löwe, B. (2012). Physicians’ attitudes, diagnostic process and barriers regarding depression diagnosis in primary care: A systematic review of qualitative studies. Family Practice, 29(3), 255–263. https://doi.org/10.1093/fampra/cmr092

Seelig, M. D., & Katon, W. (2008). Gaps in depression care: Why primary care physicians should hone their depression screening skills. Journal of Occupational and Environmental Medicine, 50(4), 378–385. https://doi.org/10.1097/JOM.0b013e31816a9f8c

Stein, R. E. K., Zitner, L. E., & Jensen, P. S. (2006). Interventions for adolescent depression in primary care. Pediatrics, 118(2), 669–682. https://doi.org/10.1542/peds.2006-1035

Van Cleave, J., Kuhlthau, K., & Bloom, S. (2012). Interventions to improve screening and follow-up in primary care: A systematic review of the evidence. Academic Pediatrics, 12(4), 267–277. https://doi.org/10.1016/j.acap.2012.02.004

Vistorte, A. O. R., Ribeiro, W. S., Jaen, D., et al. (2018). Stigmatizing attitudes of primary care professionals towards people with mental disorders: A systematic review. International Journal of Psychiatry in Medicine, 53(4), 317–338. https://doi.org/10.1177/0091217418778620

Whitlock, E. P., O’Connor, E. A., Beil, T. L., & Gaynes, B. N. (2009). Screening for depression in adults in primary care settings: A systematic evidence review. Annals of Internal Medicine, 151(11), 793–803. https://doi.org/10.7326/0003-4819-151-11-200912010-00007

Williams, J. W., Rost, K., Dietrich, A. J., & Ciotti, M. C. (1999). Primary care physicians’ approach to depressive disorders: Effects of physician specialty and practice structure. Archives of Family Medicine, 8(1), 58–67. https://triggered.stanford.clockss.org/ServeContent?url=http%3A//archfami.ama-assn.org/cgi/content/full/8/1/58

Worrall, G., Angel, J., Chaulk, P., et al. (1999). Effectiveness of an educational strategy to improve family physicians' detection and management of depression: A randomized controlled trial. CMAJ, 161(1), 37–40. https://www.cmaj.ca/content/161/1/37