Introduction Problem description

Inadequate depression screening can lead to underdiagnosis and undertreatment of depression, which in turn can lead to increased symptoms of depression, poor mental health outcomes, and reduced physical health from the effects of poor mental health. At a suburban primary care clinic, adult patients were not receiving evidence-based depression screening and management. Data from 291 patients who were seen in the clinic in March 2021 revealed that 29 patients had a preexisting diagnosis of depression, less than 10% of the patient population of the clinic. A likely assumption is that depression was underdiagnosed at this clinic due to lack of adherence to evidence-based screening guidelines. Of the 291 patients audited for this project, none were screened during their last office visit for depression using a validated, evidence-based screening tool. Before implementing the quality improvement (QI) initiative, 3 of 291 patients were newly diagnosed with depression during March 2021.

Available knowledge

Depression carries a significant physical, mental, and societal burden. Those having depression have reduced quality of life and increased mortality rates (Ferenchick et al., 2019). Individuals with depression are also at an increased risk for other health issues including heart disease, diabetes, obesity, and stroke (Akincigil & Matthew, 2107; Ferenchick et al., 2019). The community experiences the economic impact of this lost productivity and increased medical expenditures with an estimated annual financial loss of $210.5 billion nationwide (Ferenchick et al., 2019).

Depression screening in primary care is well supported. Ferenchick et al. (2019) determined that screening alone does not significantly affect quality of depression care and care outcomes. Screening is only recommended in settings in which there are appropriate systems in place to ensure adequate treatment and follow-up (Bajracharya et al., 2016; Siu & US Preventive Services Task Force [USPSTF], 2016). Multiple screening tools have been validated for use in primary care (Kroenke et al., 2001,2003; Mulrow et al., 1995). Combining pharmacotherapy and psychotherapy is preferred over monotherapy (Arroll et al., 2016; Parikh et al., 2016: Ramanuj et al., 2019). Involving patients in shared decision making is also beneficial in determining appropriate treatments because the patient is included in treatment decisions (Jackson & Thase, 2021; Moise et al., 2018).

Multiple guidelines have been developed for depression screening and management but general consensus between guidelines has not been reached (Ferenchick et al., 2019). The USPSTF guidelines recommend validated depression screening for all adult patients in primary care clinics that have adequate means for ensuring appropriate diagnosis, treatment, and follow-up (USPSTF, 2016). The American College of Preventative Medicine recommends screening all adults for depression (Nimalasuriya et al., 2009). The Institute for Clinical Systems Improvement guidelines recommend only screening adults who present with symptoms or risk factors and encourage shared decision making and combined psychotherapy and pharmacotherapy for management (Trangle et al., 2016). Results from this project will demonstrate the effectiveness of screening in such primary care clinics and encourage others to use these guidelines in their own clinics.

Rationale

Watson's Theory of Human Caring integrated well with this project because it proposed that caring is a valuable and moral ideal (Zaccagnini & White, 2017). Watson's definition of caring regards the patient as a whole and emphasizes caring as central to the relationship between the nurse and the patient. Caring is foundational to nursing and healing and also valuable when interacting with patients with depression. Patients with depression better respond to caregivers who model caring and sincere concern for their health and welfare (Keller et al., 2016).

Along with an increased emphasis on caring, an improvement to the quality of health care delivery was desired. Quality encompasses the attainment of desired health outcomes (Moran et al., 2020) and is defined as evidence-based depression identification and management for this purpose. The Plan-Do-Study-Act (PDSA) model, which includes the four steps of plan, do, study, and act, guided the implementation of this project (Hall & Roussel, 2017). The “Plan” encompassed the delineation of the project aim, an appropriate timeline, measures of success, and the resources needed for project success. A depression screening and management protocol specific to this clinic was developed and prepared for implementation. In the “Do” phase, one author carried out staff education and three clinic providers implemented the depression screening and management protocol. During the “Study” phase, data were analyzed to see if benchmarks were met and to ascertain the outcome of project implementation. Finally, in the “Act” phase, determinations were made to either continue the implemented changes or to make changes to the process and develop a revised PDSA (Hall & Roussel, 2017). Because rates of depression screening, diagnosis, and management were improved through this project, this initiative should spread to the other clinic locations within this health care system and findings should be disseminated through publication. The PDSA model for QI allowed for changes to be both implemented in a logical manner and made as needed as the project unfolded.

Specific aims

Instituting evidence-based depression screening and management protocol more accurately identifies patients with depression and quantifies the severity of a patient's depression. This project contributes to the field of nursing by translating evidence for depression screening and management into practice at this clinic. By implementing current, evidence-based guidelines, this project aims to increase identification and appropriate management of depression.

Project objectives are as follows:

Within 2 months, 90% of adult primary care patients will be screened for depression using a validated screening tool. Within 2 months, 95% of patients that will be newly diagnosed with depression will be initiated on an antidepressant or referred for psychotherapy. Within 2 months, 85% of patients with depression will be managed with a 4- to 6-week follow-up appointment.

Follow-up appointments include depression reassessment, antidepressant titration as needed, and evaluation of need for referral. One hundred percent of patients who screen positive for suicidal ideations will immediately be referred to the community's 24/7 behavioral health services.

The project PICOT question is as follows: Are adult patients at the primary care clinic (P), through the use of an evidence-based depression screening and management protocol (I) and not just through the provider's clinical judgment (C), more accurately diagnosed and managed for depression (O), in a given 2-month period (T)?

Methods Context

The project setting is a suburban primary care clinic in Virginia. There are three providers, two triage nurses, and multiple ancillary staff. Organizational weaknesses that affected project implementation include an inefficient electronic charting system, a staff knowledge deficit in evidence-based depression screening and management, and lack of a current depression screening and management policy. Project participants were chosen by a convenience sample of adult patients at the clinic during the days of project implementation. Inclusion criteria for participants included English-speaking, primary care patients who are 18 years or older. Patients were excluded from the project if they were not fluent in English, were an urgent care patient in the clinic, or were younger than 18 years. Approximately 300–400 participants were anticipated to be included in this project.

Interventions

Intervention included screening, treating, referring, and managing patients with depression at this primary care clinic. All adult patients 18 years or older were screened for depression at each primary care appointment. The triage nurse completed the two questions on the 2-item Patient Health Questionnaire (PHQ-2) during triage. This score was documented in the chart and on a paper copy of the PHQ-2. The paper copy was identified by the date of screening and the patient's chart number. If the patient's PHQ-2 score was 2 or less, no further action was needed. If the patient's score was 3 or greater, the triage nurse gave the patient a paper copy of the 9-item Patient Health Questionnaire (PHQ-9) to be completed in the examination room before the provider examination. The PHQ-9 also had the date of screening and the patient's chart number on it for data collection purposes. The triage nurse instructed the patient to complete the PHQ-9 while waiting in the examination room. After the provider arrived in the examination room, he or she reviewed the PHQ-9 with the patient during the patient interview. The provider documented the PHQ-9 score in the chart under the objective assessment section. If the PHQ-9 score was 4 or less, no further action was required. If the PHQ-9 score was 5 or greater, the provider discussed depression diagnosis and treatment options with the patient. Depression diagnosis was charted under the diagnosis section of the chart with an appropriate International Classification of Diseases, Tenth Revision (ICD-10) code. Treatment options included pharmacotherapy with antidepressants, referral to psychotherapy, or both. Shared decision making between the provider and the patient was used to determine the best treatment course for each patient. The individual patient's charting was discussed and mutually agreed on by the provider and the patient for selection of treatments. Patients who were started on pharmacotherapy or referred for psychotherapy were appointed to follow-up with the primary care provider in 4–6 weeks. Follow-up appointments included reassessment with the PHQ-9 to measure any change in depression severity from patient's score at the initial appointment. The provider also evaluated need for pharmacotherapy adjustment or referral during follow-up (Figure 1).

Figure 1.:

Process map for depression screening and management.

If the PHQ-9 score was 10 or greater at any patient appointment, the patient also received the Columbia-Suicide Severity Rating Scale (C-SSRS) screening to evaluate for suicidal ideations and behaviors. Patients who answered yes to questions 3, 4, or 5, or yes to question 6 within the past 3 months, were immediately referred to the community's on-call behavioral health specialist. The provider documented C-SSRS score in the chart under the objective assessment section and the paper copy of the C-SSRS was collected and identified with the date of screening and patient's chart number. The project team involved in implementation include the triage nurses at the clinic and the three providers at the clinic, which included one MD and two family nurse practitioners (Figure 2).

Figure 2.:

Process map for Columbia-Suicide Severity Rating Scale.

Study of the interventions

Aggregate data were collected for number of patients screened for depression with PHQ-2, number of patients screened for depression with PHQ-9, number of patients screened for suicide with C-SSRS, number of patients newly diagnosed with depression, number of patients offered treatment or referral, and number of patients managed with a follow-up appointment. Preimplementation and postimplementation data were analyzed for statistical significance to determine if any changes in outcomes were observed.

Measures

The first tool used during project implementation was the PHQ-2, which screens for depression. Construct validity for the PHQ-2 was established with the 20-item Short-Form General Health Survey, and criterion validity was established from structured mental health professional interviews (Kroenke et al., 2003). A score of greater than or equal to 3 has a specificity of 92% and a sensitivity of 83% for major depression (Kroenke et al., 2003). The second measurement tool was the PHQ-9, which measures major depression. Construct validity for the PHQ-9 was established by Kroenke et al. (2001) through comparing PHQ-9 scores with established scales. A score of greater than or equal to 10 accurately identifies major depression with a specificity of 88% and a sensitivity of 88% (Kroenke et al., 2001). The PHQ-9 showed excellent internal reliability, revealing a Cronbach alpha of 0.89 (Kroenke et al., 2001). Test–retest reliability was 0.84 (Kroenke et al., 2001). The third tool used was the C-SSRS, which quantifies the severity of suicidality (Posner et al., 2011). The C-SSRS had a specificity of 96% and a sensitivity of 100% for actual and interrupted suicide attempts. Internal reliability of the C-SSRS intensity subscale was high, with a Cronbach alpha of 0.946 (Posner et al., 2011; Figure 3).

Figure 3.:

Nine-item Patient Health Questionnaire.

Sustainability will be accomplished by the development of a depression screening and management protocol with integration into the clinic's official policies and procedures. Sustainability is also dependent on continual support from stakeholders. Thus, effective and continual communication with clinic leadership and other stakeholders continues to be integral to the success and sustainability of the project.

Analysis

Analyzed data included number of patients screened with PHQ-2, number of patients screened with PHQ-9, number of patients screened with C-SSRS, number of patients with a preexisting diagnosis of depression, number of patients newly diagnosed with depression, number of patients offered treatment, and number of patients managed with follow-up. All collected data were organized in an Excel spreadsheet and analyzed using JMP Pro v 15.2.0. All numerical data such as age and PHQ scores were summarized using mean and standard deviation. All categorical data were summarized using percentages. Mean outcomes for numerical data from preimplementation and postimplementation groups were compared using 2-sample t-tests. Percent outcomes for categorical data from preimplementation and postimplementation groups were compared using Fisher's exact test. Results were considered significant at 5% level of significance.

Ethical considerations

Before interacting with potential participants, approval from the Institutional Review Board (IRB) at the University of South Alabama was obtained to ensure ethical principles were maintained. Because the risk posed to human participants was no more than the risk incurred from everyday life, an expedited review by the IRB was required. This primary care clinic does not have an IRB; therefore, no other approval was required. The authors report no conflicts of interest.

Results

The average age of all participants (n = 616) was 53.6 years old. Of all participants, 50.3% (n = 310) were female and 49.7% (n = 306) were male. Preimplementation participants (n = 286) were an average of 51.6 years old and were 53% female and 47% male. Postimplementation patients (n = 330) were an average of 55.2 years old and were 48% female and 52% male.

No preimplementation patients (n = 0) were screened for depression. In postimplementation patients, 94% (n = 311) were screened for depression with the PHQ-2. Of the postimplementation patients with a PHQ-2 score of 3 or greater (n = 39), 95% (n = 37) of them were further screened with the PHQ-9. Of the postimplementation patient with a PHQ-9 score of 10 or greater, 100% (n = 32) of them were screened for suicide using the C-SSRS. Of the 311 postimplementation patients who were screened for depression, 6% (n = 18) were given a new diagnosis of depression. All these 18 patients were offered treatment: 8 patients were started on an antidepressant, 3 were referred to psychotherapy, and the remaining 7 declined treatment, declined referral, or, using shared decision making between the patient and the provider, were determined to not benefit from treatment or referral at this time.

A statistically significant difference was observed in percent of preimplementation and postimplementation patients screened for depression. Percent of patients screened for depression was significantly higher among postimplementation patients (94%, n = 311) than preimplementation patients (0%, n = 0; Fisher's exact test, p < .0001).

No statistically significant difference was seen in percent of antidepressant adjustments in preimplementation patients and postimplementation patients with preexisting depression diagnosis (Fisher's exact test, p = .0464). A statically significant difference was seen in percent of referrals to psychotherapy in preimplementation patients and postimplementation patients with preexisting depression diagnosis (Fisher's exact test, p = .0181). No apparent increase in antidepressant adjustments was seen for patients with preexisting depression but psychotherapy referrals for patients with preexisting depression were increased. Future QI projects should address the need to monitor and improve antidepressant adjustments and psychotherapy referrals in patients with preexisting depression diagnoses.

A statistically significant difference was observed in percent of preimplementation and postimplementation patients newly diagnosed with depression. Percent of patients newly diagnosed with depression was significantly higher among postimplementation patients (5.8%, n = 18) than preimplementation patients (1%, n = 3; Fisher's exact test, p = .0015).

Mean age of preimplementation patients with new depression diagnosis was 44.7 years old and mean age of postimplementation patients with new depression diagnosis was 53.5 years old. Although mean age of postimplementation patients with new depression diagnosis was higher than preimplementation patients with new depression diagnosis by 8.8 years, the difference was not statistically significant (t-test, p = .5533). Furthermore, no significant difference was observed in the gender distribution of preimplementation and postimplementation patients with new depression diagnosis (Fisher's exact test, p = .5263).

No statistically significant difference was observed preimplementation and postimplementation for patients newly diagnosed with depression who were started on an antidepressant (Fisher's exact test, p = .5865). Furthermore, no statistically significant difference was observed preimplementation and postimplementation for patients newly diagnosed with depression who were referred to psychotherapy. In fact, a greater percent of preimplementation patients with newly diagnosed depression were either started on an antidepressant or referred to psychotherapy than postimplementation patients with newly diagnosed depression. One hypothesis could be that, before implementing depression screening protocol, only patients who self-report depression symptoms were newly diagnosed with depression. Patients who self-report depression symptoms and receive care from a provider for depression management are likely more aware of their symptoms and thus more interested in initiating antidepressants or being referred to psychotherapy.

No statistically significant difference was seen in the percent of follow-up appointments in preimplementation patients and postimplementation patients with newly diagnosed depression (Fisher's exact test, p = 1.000). No statistically significant difference was seen in percent of follow-up appointments between preimplementation patients and postimplementation patients who were started on an antidepressant. Sample sizes in these groups were too small to determine statistical significance.

Discussion Summary

Birthed from to a lack of appropriate identification of depression during routine primary care visits, this QI project was perceived as a means of resolving this problem. On completion, this primary care clinic was better able to identify, diagnose, and manage patients with depression. Screening patients routinely for depression accurately identified and diagnosed more patients with depression than that of sole reliance on provider clinical judgment. Although each patient who was newly diagnosed with depression was offered antidepressants or psychotherapy referral or both, no significant change was appreciated in the percentage of patients who actually used these treatments.

Interpretation

The first project objective was met. Within 2 months, 94% (n = 311) of adult primary care patients were screened for depression using the PHQ-2. The remaining 6% (n = 19) refused screening. The second project objective was not met. Within 2 months, only 61% of patient with newly diagnosed depression were either started on an antidepressant (n = 8) or referred for psychotherapy (n = 3). The remaining 39% (n = 7) declined treatment, declined referral, or, after discussion with the provider using shared decision making, decided medication or referral were not warranted at this time. This objective did not take into account that patients may not want treatment or referral or that shared decision making between patient and provider may determine that antidepressants or referral are not currently the best option for the patient (Trangle et al., 2016).

The third objective was met. Within 2 months, 87% (n = 34) of patients with PHQ-2 scores of 3 or greater were managed with a 4- to 6-week follow-up appointment. Furthermore, 83% (n = 15) of patients with a new depression diagnosis were managed with a 4- to 6-week follow-up appointment. Ninety percent (n = 9) of patients with depression who had an antidepressant adjustment were managed with a 4- to 6-week follow-up appointment and 100% (n = 10) of patients with depression who initiated an antidepressant were managed with a 4- to 6-week follow-up appointment. One patient reported thoughts of self-harm on the C-SSRS screening tool and was evaluated for suicidality and appropriately referred to behavioral health services.

Limitations

Statistical significance was not identifiable due to small sample sizes in several metrics, including percent of newly diagnosed patients started on antidepressants, percent of newly diagnosed patients referred to psychotherapy, and percent of patients managed with appropriate follow-up who were either newly diagnosed with depression or started on an antidepressant. The design did not include methods for measuring and tracking changes in depression severity over time. Further QI projects could be designed to determine if depression severity was decreased over time with continued implementation of this depression screening and management protocol.

Conclusions

Routine depression screening at this primary care clinic increased identification, diagnosis, and management of depression. Awareness and use of shared decision making in determining the best treatment options for patients with preexisting and newly diagnosed depression was accomplished. Sustainability was accomplished in that the identified procedures can continue and positively affect the patients in this clinic. Future benefits would be analysis over a greater time span and exploration of the limitations in this project. Additional follow-up in this clinic could assess and monitor changes in PHQ scores over time and determine if this depression screening and management protocol results in decreases to depression severity among patients at this clinic.

Acknowledgments: The authors wish to acknowledge Sue Nevels for her editorial assistance.

Funding: No funding was received for any components of the project or manuscript preparation. Personnel time was volunteered. Materials were donated by the clinic.

Authors' contributions: L. Sharp developed the project, collected data, conducted the data analysis, and wrote the initial draft of the manuscript. S. Montgomery contributed to the development of the quality improvement project and revised the manuscript for final submission. R. Williams oversaw on-site implementation of the quality improvement project.

References Akincigil A., Matthew E. B. (2017). National rates and patterns of depression screening in primary care: Results from 2012 and 2013. Psychiatric Services, 68(7), 660–666. https://doi.org/10.1176/appi.ps.201600096 Arroll B., Chin W., Martis W., Goodyear-Smith F., Mount V., Kingsford D., Humm S., Blashki G., MacGilllivray S. (2016). Antidepressants for treatment of depression in primary care: A systematic review and meta-analysis. Journal of Primary Health Care, 8(4), 325–334. https://doi.org/10.1071/hc16008 Bajracharya P., Summers L., Amatya A. K., DeBlieck C. (2016). Implementation of a depression screening protocol and tools to improve screening for depression in patients with diabetes in the primary care setting. The Journal for Nurse Practitioners, 12(10), 690–696. https://doi.org/10.1016/j.nurpra.2016.08.009 Ferenchick E. K., Ramanuj P., Pincus H. A. (2019). Depression in primary care: Part 1 – screening and diagnosis. BMJ, 365, l794. https://doi.org/10.1136/bmj.l794 Hall H. R., Roussel L. A. (2017). Evidence-based practice: An integrative approach to research, administration, and practice (2nd ed.). Jones & Bartlett Learning. Jackson W. C., Thase M. E. (2021). Setting the stage for major depressive disorder recovery: Strategies for the busy primary care provider. The Journal of Family Medicine, 70(1), S1–S19. https://doi.org/10.12788/jfp.0134 Keller A. O., Valdes C. R., Schwei R. J., Jacobs E. A. (2016). Disclosure of depression in primary care: A qualitative study of women's perceptions. Women’s Health Issues, 25(5), 529–536. https://doi.org/10.1016/j.whi.2016.07.002 Kroenke K., Spitzer R. L., Williams J. B. W. (2003). The patient health questionnaire-2: Validity of a two-item depression screener. Medical Care, 41(11), 1284–1292. https://doi.org/10.1097/01.mlr.0000093487.78664.3c Kroenke K., Spitzer R. L., Williams J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Moise N., Falzon L., Obi M., Ye S., Patel S., Gonzalez C., Bryant K., Kronish I. M. (2018). Interventions to increase depression treatment initiation in primary care patients: A systematic review. Journal of General Internal Medicine, 33(11), 1978–1989. https://doi.org/10.1007/s11606-018-4554-z Moran K., Burson R., Conrad D. (2020). The doctor of nursing practice project: A framework for success (3rd. Ed.). Jones & Bartlett Learning. Mulrow C. D., Williams J. W., Gerety M. B., Ramirez G., Montiel O. M., Kerber C. (1995). Case-finding instruments in depression in primary care settings. Annals of Internal Medicine, 122(12), 913–921. https://doi.org/10.7326/0003-4819-122-12-199506150-00004 Nimalasuriya K., Compton M. T., Guillory V. J. (2009). Screening adults for depression in primary care: A position statement of the American College of preventive Medicine. The Journal of Family Practice, 58(10), 535–538. https://www.mdedge.com/familymedicine/article/63746/mental-health/screening-adults-depression-primary-care-position Parikh S. V., Quilty L. C., Ravitz P., Rosenbluth M., Pavlova B., Grigoriadis S., Velyvis V., Kennedy S. H., Lam R. W., MacQueen G. M., Milev R. V., Ravindram A. V., Uher R., & the CANMAT Depression Work Group. (2016). Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 2. psychological treatments. The Canadian Journal of Psychiatry, 61(9), 524–539. https://doi.org/10.1177/0706743716659418 Posner K., Brown G. K., Stanley B., Brent D. A., Yershova K. V., Oquendo M. A., Currier G. W., Melvin G. A., Greenhill L., Shen S., Mann J. J. (2011). The Columbia-Suicide Severity Rating Scale: Initial validity and internal consistency findings from three multisite studies with adolescents and adults. American Journal of Psychiatry, 168(12), 1266–1277. https://doi.org/10.1176/appi.ajp.2011.10111704 Ramanuj P., Ferenchick E. F., Pincus H. A. (2019). Depression in primary care: Part 2 – management. BMJ, 365, l835. https://doi.org/10.1136/bmj.l835 Siu A. L., & US Preventive Services Task Force. (2016). Screening for depression in adults: US preventive Services Task Force recommendation. JAMA, 315(4), 380–387. https://doi.org/10.1001/jama.2015.18392 Trangle M., Gursky J., Haight R., Hardwig J., Hinnenkamp T., Kessler D., Mack N., Myszkowski M. (2016, March). Adult Depression in Primary Care. Institute for Clinical Systems Improvement. https://www.icsi.org/wp-content/uploads/2019/01/Depr.pdf Zaccagnini M. E., White K. W. (2017). The doctor of nursing practice essentials: A new model for advanced practice nursing (3rd ed.). Jones & Bartlett Learning.