Enhancing Clinical Depression Treatment Outcomes: A Comprehensive Approach using Measurement-based Care, Research Benchmarks, and Systematic Quality Improvements

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Evidence-based interventions, such as psychotherapy and pharmacotherapy, are effective in alleviating depressive symptoms; however, challenges regarding accessibility of good quality care persist. This study investigated a Continuous Quality Improvement (CQI) process that incorporated measurement-based care and benchmarking to enhance treatment outcomes for depression within specialized mental healthcare settings. Methods This study utilized data from two outpatient mental health clinics in Stockholm, Sweden. In a first step, the study employed patient-reported outcome measures (PROMs) to compare clinical treatment results with a meta-analytic benchmark (Öst et al. 2023). In a second step, a CQI framework was implemented to improve treatment outcomes. Results A total sample size of N = 415 patients treated for depression was included in the analyses. The clinical sample demonstrated higher pre-treatment depression severity scores, more appointments, and a lower data attrition rate compared to the meta-analytic benchmark. Year-to-year analyses indicated that clinical treatment effects were initially significantly lower (Hedge’s g = 0.87 to 1.01, 95% CI[0.58–1.16]) compared to the meta-analytic benchmark ( g = 1.51, 95% CI [1.36–1.65]). However, when the results from the CQI process were implemented, treatment effects improved and were found to be non-inferior to the meta-analytic benchmark ( g = 1.25 to1.27, 95% CI [0.94–1.61]). Conclusions The findings suggest that the integration of measurement-based care, benchmarking, and CQI have the potential to improve treatment outcomes for depression in specialized mental healthcare. Figures Figure 1 Figure 2 Contributions to the literature Depression is a leading cause of global burden of disease. However, challenges regarding accessibility of good quality care persist. In a specialized mental healthcare environment, we tested if a Continuous Quality Improvement (CQI) method, based on routinely collected patient-reported measures and meta-analytic benchmarking, could improve treatment outcomes for patients with depression. We found that the combination of measurement-based care, benchmarking and CQI had beneficial effects on depression treatment outcomes. Background Depression is among the leading causes of burden worldwide (1). The World Health Organization (WHO) has ranked clinical depression as the largest contributor to global disability, affecting over 300 million people and causing 7.5% of all years lived with disability (2). Moreover, depression is one of the primary factors contributing to suicide deaths (3). The impact of clinical depression on everyday life is profound and impairs an individual's ability to function in various domains, including work, social interactions, and personal relationships (4). It is associated with low educational attainment, unstable employment as well as a higher risk for both somatic and psychiatric comorbidities (4). Evidence-based treatment options for clinical depression include Cognitive Behavior Therapy (CBT) and pharmacotherapy, either as mono-therapies or in combination (5–7). A recent network meta-analysis reported a moderate standardized mean difference (MSD = 0.57 (95% confidence interval [0.08–1.07]) for CBT in reducing depressive symptoms (8), demonstrating its efficacy compared to control conditions. However, previous analyses of efficacy studies indicated that the effects of CBT for depression may be somewhat lower when implemented in routine clinical practice (9). Pharmacotherapy primarily involves Selective Serotonin Reuptake Inhibitors (SSRIs). Cipriani et al. (2018) found response rate odds ratios between OR = 1.37 to 2.13 for SSRIs compared to pill placebo, corresponding to an standardized effect size of Cohen’s d = 0.17 to 0.46 (11). Combining CBT with pharmacotherapy can enhance treatment outcomes, particularly in moderate to severe depression, with studies suggesting synergistic effects (12). While evidence-based treatment strategies have been proven effective for clinical depression, only a minority of individuals with the condition actually receive treatment, and the quality of care provided to the majority of those undergoing treatment remains unacceptably low (13). In a WHO-led study of 21 countries, only one in five people in high-income and one in 27 in low-/lower-middle-income countries received minimally adequate treatment (13). Importantly, quality of care for mental health conditions has not increased to the same extent as that for physical conditions (14). Thus, improvements in accessibility as well as the quality of care are crucial. The clinical application of routinely administered outcome measurements (measurement-based care, MBC) in combination with benchmarking and continuous quality improvements (CQI) methodologies has be advocated for as tools for improving clinical outcomes in mental healthcare (14–16). Core principles of MBC are the collection of clinical outcome measures (such as the PHQ-9 for depressive symptoms (17)), the systematic evaluation of patient-reported symptoms before or during an encounter, and a collaborative involvement of the patient in the follow-up and further planning of treatment, based on these measurements (18). The essence of benchmarking is to compare group- or clinic-level outcomes with a chosen gold standard, to monitor the current status of relevant indicators of quality and identify areas for improvement (19). Previous studies have demonstrated that benchmarking methodology can be used to compare outcomes between mental health services (20), different treatment modalities of psychological treatments (21) as well as comparisons on a national level (15). A recent meta-analysis from the somatic conditions field demonstrated that the use of benchmarking was significantly associated with improvements in process and treatment outcomes (22). However, results that demonstrate the value of benchmarking for improvements in mental healthcare are scarce. CQI involves a set of different methodologies with common features such as the identification of areas in need of improvement, the development and execution of a strategy aimed towards improvement and a follow-up of implemented actions or procedures. Previous research has demonstrated that systematic CQI approaches can improve clinical outcomes and overall quality of care in the treatment of depression in primary care (23). However, results of treatment of depression from specialized psychiatric settings, studying the combination of MBC, benchmarking and CQI, are currently not available. The aim of the current study was to evaluate a CQI process, guided by MBC and benchmarking data, targeting the improvement of depression treatment outcomes within a specialized mental healthcare environment. Methods This study is reported according to SQUIRE 2.0 standards (24). Setting The data used in this study originate from two outpatient mental health clinics, located in the inner-city area of Stockholm, Sweden. The clinics are operated by WeMind Psychiatry, a mental healthcare provider commissioned by Region Stockholm. Both clinics are specialized in the psychiatric treatment of depression as well as anxiety disorders, OCD and related disorders, and PTSD. Treatments were provided according to standard procedures within the two clinics. These clinical procedures were implemented in line with national clinical guidelines (6,25), supporting clinicians as to which evidence-based pharmacological and psychological treatments should be offered under different clinical scenarios. Typically, patients received cognitive behavior therapy (CBT), pharmacotherapy or a combination of both. Clinicians were experienced licensed psychologists and licensed psychiatrists. Procedures Data collection procedures followed the regular clinical assessment routines established at the participating clinics. No additional elements were added for research purposes. Patients were adult individuals referred from primary (general practitioner) and secondary (specialized) healthcare services to either clinic for initial diagnostic assessment. Prior to the first visit, patients were assessed with standardized, self-rating measurement batteries regarding overall symptoms and functional impairment. Patients were then invited to a first appointment at the clinic. During this intake visit, a semi-structured psychiatric assessment was conducted to establish the primary diagnosis and comorbidities, using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998). Patients were then offered treatment alternatives dependent on their primary mental health condition and comorbidity profile. In conjunction with the start of treatment, a standardized battery of PROMs was administered. PROMs were administered once per week during treatment but could be individually adjusted by the treating clinician. Each measurement battery included a primary diagnosis-specific PROM (see “Measures” section). Patients completed the measurements online via a personal, secure login, using two-way authentication. The post-treatment PROM assessment was defined as the measurement closest to the end of treatment, however with the requirement of at least 6 weeks separation from the pre-treatment measurement time point. A comprehensive model for the combination of MBC, benchmarking and CQI The CQI process was guided by a comprehensive model, combining principles of MBC, benchmarking and CQI (16). Figure 1 summarizes the model. MBC Since 2008, principles of MBC (26) had been implemented, including the continuous collection of diagnosis-specific patient-reported outcome measures (PROMs). For follow-up of the treatment of patients with depression the PHQ-9 (17) was the primary measure. Therefore, relevant outcome data were available with sufficient coverage on a year-to-year basis. Benchmarking A benchmarking audit was conducted during the years 2019 through 2021. Data from all available pre- and post-treatment measurements of PHQ-9 were compiled via an electronic assessment tool and visually presented to the clinic managers and staff. The meta-analysis of Hans & Hiller (2013) was used as a gold standard comparison of the clinical outcomes. However, as there was more comprehensive and updated data available in a more recent publication, we then used the meta-analysis by Öst and co-workers in our statistical analyses (27). This meta-analysis included both RCTs and pre-post trials in adult populations of depressed patients, which enabled effect size calculations of within-group change of disorder-specific depressive symptoms. This meta-analysis used both interviewer-based ratings and self-rating measures. However, since the clinical data included in the current study only used patient-reported measures, we included only studies from the meta-analysis that provided data on a reliable and valid patient report measure of depressive symptoms. For a detailed description, see the original publication (27). Quality control and improvement procedure During the benchmarking audit in 2019 to 2021, the clinic managers identified the treatment outcomes for depression as an area for improvement, as the outcomes were below the chosen benchmark (9). As a result of this decision, a quality improvement cycle was initiated. The CQI process that was used in the current case was the FOCUS-PDCA cycle (16), consisting of the following steps: Find a process to improve (F), organize a knowledgeable team (O), clarify current knowledge of the process (C), understand sources of process variations (U), and select improvements (S), plan an approach (P), do the activity (D), check the results (C) and act on the results (A). As a first step of the FOCUS-PDA cycle, the quality assurance manager (coauthor FL) together with the clinic managers (coauthors LWA, IW) identified that depression treatment outcomes were not in line with the research benchmark (9). A clinical team of three clinical psychologists and two psychiatrists was organized (O) to clarify the knowledge regarding current diagnostic and treatment routines for patients with depression (C). To gain a better understanding of process variations and areas for improvement (U), a systematic review of medical records of completed depression treatments was carried out by the clinical team. This review resulted in two insights: Instead of one patient population of individuals with clinical depression, when the onset and course of symptoms were considered more closely, a categorization into three distinct sub-groups appeared more appropriate: A. Patients with a primary diagnosis of major depression, B. Patients with depression as a comorbid condition to an anxiety disorder, C. Patients with depression as a comorbid condition to a personality disorder. If diagnostic routines were modified according to this subcategorization, the improved clinical assessments should lead to more appropriate allocation of patients to effective treatment options, and thus promote improved treatment outcomes. The team then selected the above two aspects as relevant improvements (S). The results from the systematic analysis of the medical records were presented to and jointly discussed with the clinical staff at the two clinics. A schedule was planned for the roll out of updated routines for diagnostic and clinical assessments as well as treatment allocation (P). The new routines were implemented in the second half of 2021 (D) and followed-up (C) in 2022 and 2023, see Results section below. Due to the positive results of this quality improvement cycle, the results were then implemented as standard routines at the clinics as well as shared with other clinics in the organization (A). Assessment Measures Participants were assessed before and after treatment. For the clinical outcome measurements of clinical depression the Patient Health Questionnaire-9 (PHQ-9 , Kroenke et al., 2001) was used. Background variables Gender . Since the lifetime prevalence for depressive disorders is higher for women than men we record this variable as percent females (28). Mean age . There is no indication that the age of the participants is a significant predictor of outcome among adults, but it is presented as a basic background variable. Number of treatment sessions . It is important when comparing studies in routine clinical care and research settings that the number of therapy sessions does not deviate substantially from what the developers of CBT methods designed, i.e., 12-16 sessions. Thus, it is important to record the number of therapy sessions. Pre-treatment severity . It is well-known that the pre-treatment score on the outcome measure is a significant predictor of the post-treatment score (27,29). We recorded the sample’s mean score as percent of the maximum score possible on the instrument in question. Statistical analyses Year-by-year treatment outcomes on the PHQ-9 were compared with the most recent meta-analytic benchmark (27). The meta-analytic benchmark study included different self-report measures. Since all the measures were reliable and valid as patient-reported measures of depression, we pooled their pre- and post-treatment results to derive a mean effect size for the respective disorder. Within-group effect size (ES) was calculated as (Mpre − Mpost)/SDpre according to recommendation by Lakens (2013), as there is good reason to assume that the interventions influence not only the means but also the standard deviations. The mean ES was computed by weighting each ES by the inverse of its variance. When a study presented intent-to-treat data these were used, if not completer data were used. The software Comprehensive Meta-Analysis v.4 (31) was used for all analyses, and to correct for small sample sizes, Hedges’ g was calculated. We compared the means on background variables and mean ES for the outcome variables in the following way. If the clinic’s mean was within the 95% confidence interval (CI) of the meta-analysis we considered it not a significant difference, and if the mean was below or above the CI we considered it a significant difference. Whether the clinical mean was better or worse depended on the individual background variable, whereas for ES a mean below the CI means a significantly worse, and above the CI a significantly better outcome. Regarding the year-by-year analysis of treatment outcomes, we considered a significant difference if the clinical and benchmark confidence intervals did not overlap and non-significant if they did overlap. Results Background data A comparison between the clinical and meta-analytic benchmark samples on background variables is presented in Table 1 . There was no difference regarding percent female patients and mean age of the participants. However, the clinical sample had a higher pre-treatment depression severity score, received more therapy sessions, and had a lower attrition rate than the meta-analyric benchmark. Table 1 . Background variables for the clinical and benchmark samples. Sample k N % females Mean age % Pre Tx severity Mean number of sessions % data attrition Clinical 1 415 63.4 38.2 (SD = 13.2) 58.7 H 16.1 (SD = 13.2) H 14.7 L Benchmark 32 3525 66.4 39.1 48.3 12.7 23.7 (95% CI) (62.8-70.0) (36.5-41.7) (45.1-51.4) (10.9-14.4) (17.9-29.5) Note: k = number of samples or studies. N = number of participants. CI = confidence interval. Tx = treatment. L = lower than benchmark, H = higher than benchmark. Year-by-year analysis Figure 2 shows the effects of depression treatments divided by year, in comparison with the mean pre-post ES of 1.51 for the effectiveness studies (see as well Supplementary materials Table S1). Clinical treatment effects were significantly lower than that of MA effectiveness studies in 2019 to 2021. For 2022 and 2023 the treatment effect was non-inferior to the MA studies. Discussion The aim of this study was to evaluate a quality improvement cycle targeted on depression treatment outcomes. The quality improvement process was based on MBC data collected in specialized outpatient mental healthcare and a meta-analytic research benchmark. We found that treatment outcomes improved as findings from the benchmarking and CQI phases were implemented in clinical practice. As a result, treatment outcomes were on par with the meta-analytic benchmark in 2022 and 2023. While the best available benchmark was used for the benchmarking audit in the initial phase of the project ( 9 ), we then used a more recent and more comprehensive meta-analysis for the analyses ( 27 ). In addition, the meta-analysis of Öst et al. (2023) provided a more conservative benchmark, with an effect size of d = 1.51, compared to that of Hans and Hiller (2013), g = 1.26. We think several insights can be drawn from this study. Firstly, as MBC was implemented several years before this study, good quality data on PROMs were readily available. This availability of relevant outcome data, in this case PHQ-9 measurements from pre- and post-treatment, made it possible for the team of clinic managers, quality assurance staff and researchers to conduct a benchmark with meta-analytic results. Thus, the routinely implemented collection of PROMs was a key prerequisite for a systematic comparison with a research benchmark, which in turn is a necessity to identify areas for improvement. However, previous studies indicated that the use of MBC and routinely collected measures is widely underutilized, with only 13.9% of clinicians using such measures at least monthly and the majority, 61.5%, never uses them ( 32 ). Efforts to better understand barriers for implementation and to facilitate the use of PROMs are therefore an important first step. Secondly, the application of a FOCUS-PDCA cycle to the sub-optimal clinical outcomes resulted in an effective improvement of the underlying care processes. As improvements in clinical routines were implemented, the clinical outcomes improved and approached the effect sizes of the meta-analytic benchmark. Whereas the study design does not allow causal inference, the improvements in outcome were in close temporal association with improvements made in the clinical routines. A possible next step would be to replicate the findings from our study with an experimental control group design, e.g., by randomizing clinics to a CQI condition and compare treatment outcomes to clinics without such an intervention. Thirdly, the improvements in clinical routines were mainly focused on a better case conceptualization and improved diagnostic understanding of the patient population. It appears that the improvement of diagnostics facilitated more appropriate allocation of patients to effective treatments and/or treatment planning. The design of our study does however not allow for a confirmation of this hypothesis. Future research should investigate the role of diagnostic accuracy and correct case conception on treatment outcome. Limitations The results from study are limited by several aspects. First, the study design does not allow causal inference, and therefore we cannot rule out that other factors have influenced or biased our findings. Studies involving rigorous control conditions are therefore warranted. Second, as hypothesized previously, it appears that an improvement in diagnostic assessments contributed to an improvement of treatment outcomes. However, the study design does not allow an evaluation of this hypothesis. Therefore, eloquently designed studies that test the importance of accurate diagnostic assessments for treatment outcomes are warranted. Thirdly, the current study focused on depression treatment due to the initially sub-optimal results. It would be important to test the comprehensive quality improvement method used in this study in other contexts and clinical populations to gain better generalizability. Conclusions Results from this study indicate that the combination of MBC, routinely collected PROMs, benchmarking and CQI promoted improved results for depression treatments in specialized mental healthcare. The findings warrant replication in well-controlled study designs. Abbreviations CQI Continuous Quality Improvements CBT Cognitive behavior therapy CI Confidence interval ES Effect size FOCUS-PDCA Find a process to improve (F), organize a knowledgeable team (O), clarify current knowledge of the process (C), understand sources of process variations (U), and select improvements (S), plan an approach (P), do the activity (D), check the results (C) and act on the results (A). k Number of studies MBC Measurement-based Care N Sample size / observations PROMs Patient-reported Outcome Measurements SSRIs Selective Serotonin Reuptake Inhibitors Declarations Ethics approval and consent to participate This study was approved by the Swedish Ethical Review Authority (application ID: 2024-08303-02) and was exempted from participant consent as all data was collected according to standard clinical routines. Consent for publication Not applicable. Availability of data and materials Data is not available due to Swedish and EU law applicable to sensitive information. Competing interests The authors declare that they have no competing interests. Funding This study did not receive dedicated funding. Authors' contributions FL contributed to the study design, data analysis and interpretation, manuscript writing and reviewing. LWA and IV coordinated the CQI process. EA contributed to manuscript writing and reviewing. LGÖ conducted the meta-analytic benchmark analyses and contributed to the manuscript writing and reviewing. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Depression WHO. and Other Common Mental Disorders, Global Health Estimates [Internet]. 2017 [cited 2024 Oct 16]. Available from: https://www.who.int/publications/i/item/depression-global-health-estimates Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171–8. Kessler RC. The Costs of Depression. Psychiatr Clin North Am. 2012;35(1):1–14. American Psychological Association. Clinical practice guideline for the treatment of depression across three age cohorts [Internet]. 2019 [cited 2024 Oct 24]. Available from: https://doi.apa.org/doi/10.1037/e505892019-001 National Board of Health and Welfare. National Board of Health and Welfare. 2019 [cited 2024 May 14]. National guidelines for treatment of depression and anxiety disorders. Available from: https://www.socialstyrelsen.se/kunskapsstod-och-regler/regler-och-riktlinjer/nationella-riktlinjer/riktlinjer-och-utvarderingar/depression-och-angest/ NICE. Depression in adults: treatment and management [Internet]. National Institute for Health and Care Excellence; 2022. Available from: https://www.nice.org.uk/guidance/ng222 Ciharova M, Furukawa TA, Efthimiou O, Karyotaki E, Miguel C, Noma H, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol. 2021;89(6):563–74. Hans E, Hiller W. Effectiveness of and dropout from outpatient cognitive behavioral therapy for adult unipolar depression: a meta-analysis of nonrandomized effectiveness studies. J Consult Clin Psychol. 2013;81(1):75–88. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet Lond Engl. 2018;391(10128):1357–66. Sánchez-Meca J, Marín-Martínez F, Chacón-Moscoso S. Effect-size indices for dichotomized outcomes in meta-analysis. Psychol Methods. 2003;8(4):448–67. Cuijpers P, van Straten A, Warmerdam L, Andersson G. Psychotherapy versus the combination of psychotherapy and pharmacotherapy in the treatment of depression: a meta-analysis. Depress Anxiety. 2009;26(3):279–88. Thornicroft G, Chatterji S, Evans-Lacko S, Gruber M, Sampson N, Aguilar-Gaxiola S, et al. Undertreatment of people with major depressive disorder in 21 countries. Br J Psychiatry. 2017;210(2):119–24. Kilbourne AM, Beck K, Spaeth-Rublee B, Ramanuj P, O’Brien RW, Tomoyasu N, et al. Measuring and improving the quality of mental health care: a global perspective. World Psychiatry. 2018;17(1):30. Bramesfeld A, Amaddeo F, Caldas-de-Almeida J, Cardoso G, Depaigne-Loth A, Derenne R, et al. Monitoring mental healthcare on a system level: Country profiles and status from EU countries. Health Policy. 2016;120(6):706–17. Endalamaw A, Khatri RB, Mengistu TS, Erku D, Wolka E, Zewdie A, et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv Res. 2024;24(1):487. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. Zhu M, Hong RH, Yang T, Yang X, Wang X, Liu J, et al. The Efficacy of Measurement-Based Care for Depressive Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Clin Psychiatry. 2021;82(5):21r14034. Bayney R. Benchmarking in mental health: an introduction for psychiatrists. Adv Psychiatr Treat. 2005;11(4):305–14. Delgadillo J, McMillan D, Leach C, Lucock M, Gilbody S, Wood N. Benchmarking Routine Psychological Services: A Discussion of Challenges and Methods. Behav Cogn Psychother. 2014;42(1):16–30. Wickberg F, Lenhard F, Aspvall K, Serlachius E, Andrén P, Johansson F, et al. Feasibility of internet-delivered cognitive-behavior therapy for obsessive-compulsive disorder in youth with autism spectrum disorder: A clinical benchmark study. Internet Interv. 2022;28:100520. Willmington C, Belardi P, Murante AM, Vainieri M. The contribution of benchmarking to quality improvement in healthcare. A systematic literature review. BMC Health Serv Res. 2022;22(1):139. Wells KB, Sherbourne C, Schoenbaum M, Duan N, Meredith L, Unützer J, et al. Impact of Disseminating Quality Improvement Programs for Depression in Managed Primary CareA Randomized Controlled Trial. JAMA. 2000;283(2):212–20. Davidoff F, Batalden P, Stevens D, Ogrinc G, Mooney SE. Publication guidelines for quality improvement studies in health care: evolution of the SQUIRE project. BMJ. 2009;338:a3152. Region Stockholm. Kunskapsstöd för vårdgivare [Internet]. 2024 [cited 2024 May 14]. Available from: https://kunskapsstodforvardgivare.se/omraden/psykisk-halsa Lewis CC, Boyd M, Puspitasari A, Navarro E, Howard J, Kassab H, et al. Implementing Measurement-Based Care in Behavioral Health: A Review. JAMA Psychiatry. 2019;76(3):324–35. Öst LG, Enebrink P, Finnes A, Ghaderi A, Havnen A, Kvale G, et al. Cognitive behavior therapy for adult depressive disorders in routine clinical care: A systematic review and meta-analysis. J Affect Disord. 2023;331:322–33. Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, et al. Epidemiology of Adult DSM-5 Major Depressive Disorder and Its Specifiers in the United States. JAMA Psychiatry. 2018;75(4):336–46. Öst LG, Enebrink P, Finnes A, Ghaderi A, Havnen A, Kvale G, et al. Cognitive behavior therapy for adult post-traumatic stress disorder in routine clinical care: A systematic review and meta-analysis. Behav Res Ther. 2023;166:104323. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol [Internet]. 2013 Nov 26 [cited 2024 Aug 14];4. Available from: https://www.frontiersin.org/journals/psychology/articles/ 10.3389/fpsyg.2013.00863/full Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. Introduction to Meta-Analysis. 2022. 1–421 p. Jensen-Doss A, Haimes EMB, Smith AM, Lyon AR, Lewis CC, Stanick CF, et al. Monitoring Treatment Progress and Providing Feedback is Viewed Favorably but Rarely Used in Practice. Adm Policy Ment Health Ment Health Serv Res. 2018;45(1):48–61. Supplementary Files SQUIRE2.0glossary150915.pdf Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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institutionen","correspondingAuthor":false,"prefix":"","firstName":"Lars-Göran","middleName":"","lastName":"Öst","suffix":""}],"badges":[],"createdAt":"2025-01-09 14:40:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5797396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5797396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80633488,"identity":"04f148f7-c8c9-47b7-a303-90bc082a1f3c","added_by":"auto","created_at":"2025-04-15 12:00:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145890,"visible":true,"origin":"","legend":"\u003cp\u003eA comprehensive model for the combination of measurement-based care, benchmarking, and continuous quality improvements.\u003c/p\u003e","description":"","filename":"FIGURE1Comprehensiveapproachmodel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5797396/v1/e4005eca3eb54b4628262951.jpg"},{"id":80631985,"identity":"03f4ce19-06f7-4380-8673-a4adc01c2374","added_by":"auto","created_at":"2025-04-15 11:52:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61816,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual effect sizes (ES) and 95% confidence intervals (CI) for depression treatments compared to the meta-analytic benchmark (Öst et al., 2023).\u003c/p\u003e","description":"","filename":"FIGURE2Results.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5797396/v1/e976b3f438bbff818d632da2.jpg"},{"id":90888242,"identity":"d438d41c-668f-45c3-ab05-725534655c9c","added_by":"auto","created_at":"2025-09-09 10:25:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":883407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5797396/v1/9face914-d9ba-4d79-8bd8-b7c8ffed6a43.pdf"},{"id":80631990,"identity":"75e7c10c-de83-47b2-b17c-9c2fce76baf7","added_by":"auto","created_at":"2025-04-15 11:52:44","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":332042,"visible":true,"origin":"","legend":"","description":"","filename":"SQUIRE2.0glossary150915.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5797396/v1/e5bb5e2f06401ffeffa901ce.pdf"},{"id":80631999,"identity":"426313c3-6402-4544-a570-be0b32ed757a","added_by":"auto","created_at":"2025-04-15 11:52:44","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":25078,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5797396/v1/048ee9be52f249b964820de6.docx"}],"financialInterests":"","formattedTitle":"Enhancing Clinical Depression Treatment Outcomes: A Comprehensive Approach using Measurement-based Care, Research Benchmarks, and Systematic Quality Improvements","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eDepression is a leading cause of global burden of disease. However, challenges regarding accessibility of good quality care persist.\u003c/li\u003e\n \u003cli\u003eIn a specialized mental healthcare environment, we tested if a Continuous Quality Improvement (CQI) method, based on routinely collected patient-reported measures and meta-analytic benchmarking, could improve treatment outcomes for patients with depression.\u003c/li\u003e\n \u003cli\u003eWe found that the combination of measurement-based care, benchmarking and CQI had beneficial effects on depression treatment outcomes.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eDepression is among the leading causes of burden worldwide (1). The World Health Organization (WHO)\u0026nbsp;has ranked clinical depression as the largest contributor to global disability, affecting over 300 million people and causing 7.5% of all years lived with disability (2). Moreover, depression is one of the primary factors contributing to suicide deaths (3). The impact of clinical depression on everyday life is profound and impairs an individual\u0026apos;s ability to function in various domains, including work, social interactions, and personal relationships (4). It is associated with low educational attainment, unstable employment as well as a higher risk for both somatic and psychiatric comorbidities (4).\u003c/p\u003e\n\u003cp\u003eEvidence-based treatment options for clinical depression include Cognitive Behavior Therapy (CBT) and pharmacotherapy, either as mono-therapies or in combination (5\u0026ndash;7). A recent network meta-analysis reported a moderate standardized mean difference (MSD = 0.57 (95% confidence interval [0.08\u0026ndash;1.07]) for CBT in reducing depressive symptoms (8), demonstrating its efficacy compared to control conditions. However, previous analyses of efficacy studies indicated that the effects of CBT for depression may be somewhat lower when implemented in routine clinical practice (9).\u003c/p\u003e\n\u003cp\u003ePharmacotherapy primarily involves Selective Serotonin Reuptake Inhibitors (SSRIs). Cipriani et al. (2018) found response rate odds ratios between OR = 1.37 \u0026nbsp;to 2.13 for SSRIs compared to pill placebo, corresponding to an standardized effect size of Cohen\u0026rsquo;s d = 0.17 to 0.46 (11). Combining CBT with pharmacotherapy can enhance treatment outcomes, particularly in moderate to severe depression, with studies suggesting synergistic effects (12).\u003c/p\u003e\n\u003cp\u003eWhile evidence-based treatment strategies have been proven effective for clinical depression, only a minority of individuals with the condition actually receive treatment, and the quality of care provided to the majority of those undergoing treatment remains unacceptably low (13). In a WHO-led study of 21 countries, only one in five people in high-income and one in 27 in low-/lower-middle-income countries received minimally adequate treatment (13). Importantly, quality of care for mental health conditions has not increased to the same extent as that for physical conditions (14). Thus, improvements in accessibility as well as the quality of care are crucial.\u003c/p\u003e\n\u003cp\u003eThe clinical application of routinely administered outcome measurements (measurement-based care, MBC) in combination with benchmarking and continuous quality improvements (CQI) methodologies has be advocated for as tools for improving clinical outcomes in mental healthcare (14\u0026ndash;16). Core principles of MBC are the collection of clinical outcome measures (such as the PHQ-9 for depressive symptoms (17)), the systematic evaluation of patient-reported symptoms before or during an encounter, and a collaborative involvement of the patient in the follow-up and further planning of treatment, based on these measurements (18).\u003c/p\u003e\n\u003cp\u003eThe essence of benchmarking is to compare group- or clinic-level outcomes with a chosen gold standard, to monitor the current status of relevant indicators of quality and identify areas for improvement (19). Previous studies have demonstrated that benchmarking methodology can be used to compare outcomes between mental health services (20), different treatment modalities of psychological treatments (21) as well as comparisons on a national level (15). A recent meta-analysis from the somatic conditions field demonstrated that the use of benchmarking was significantly associated with improvements in process and treatment outcomes (22). However, results that demonstrate the value of benchmarking for improvements in mental healthcare are scarce.\u003c/p\u003e\n\u003cp\u003eCQI involves a set of different methodologies with common features such as the identification of areas in need of improvement, the development and execution of a strategy aimed towards improvement and a follow-up of implemented actions or procedures. Previous research has demonstrated that systematic CQI approaches can improve clinical outcomes and overall quality of care in the treatment of depression in primary care (23). However, results of treatment of depression from specialized psychiatric settings, studying the combination of MBC, benchmarking and CQI, are currently not available.\u003c/p\u003e\n\u003cp\u003eThe aim of the current study was to evaluate a CQI process, guided by MBC and benchmarking data, targeting the improvement of depression treatment outcomes within a specialized mental healthcare environment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is reported according to SQUIRE 2.0 standards (24).\u003c/p\u003e\n\u003ch2\u003eSetting\u003c/h2\u003e\n\u003cp\u003eThe data used in this study originate from two outpatient mental health clinics, located in the inner-city area of Stockholm, Sweden. The clinics are operated by WeMind Psychiatry, a mental healthcare provider commissioned by Region Stockholm. Both clinics are specialized in the psychiatric treatment of depression as well as anxiety disorders, OCD and related disorders, and PTSD. Treatments were provided according to standard procedures within the two clinics. These clinical procedures were implemented in line with national clinical guidelines (6,25), supporting clinicians as to which evidence-based pharmacological and psychological treatments should be offered under different clinical scenarios. Typically, patients received cognitive behavior therapy (CBT), pharmacotherapy or a combination of both. Clinicians were experienced licensed psychologists and licensed psychiatrists.\u003c/p\u003e\n\u003ch2\u003eProcedures\u003c/h2\u003e\n\u003cp\u003eData collection procedures followed the regular clinical assessment routines established at the participating clinics. No additional elements were added for research purposes. Patients were adult individuals referred from primary (general practitioner) and secondary (specialized) healthcare services to either clinic for initial diagnostic assessment. Prior to the first visit, patients were assessed with standardized, self-rating measurement batteries regarding overall symptoms and functional impairment. Patients were then invited to a first appointment at the clinic. During this intake visit, a semi-structured psychiatric assessment was conducted to establish the primary diagnosis and comorbidities, using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998). Patients were then offered treatment alternatives dependent on their primary mental health condition and comorbidity profile. In conjunction with the start of treatment, a standardized battery of PROMs was administered. PROMs were administered once per week during treatment but could be individually adjusted by the treating clinician. Each measurement battery included a primary diagnosis-specific PROM (see \u0026ldquo;Measures\u0026rdquo; section). Patients completed the measurements online via a personal, secure login, using two-way authentication. The post-treatment PROM assessment was defined as the measurement closest to the end of treatment, however with the requirement of at least 6 weeks separation from the pre-treatment measurement time point.\u003c/p\u003e\n\u003ch2\u003eA comprehensive model for the combination of MBC, benchmarking and CQI\u003c/h2\u003e\n\u003cp\u003eThe CQI process was guided by a comprehensive model, combining principles of MBC, benchmarking and CQI (16). \u003cstrong\u003eFigure 1\u0026nbsp;\u003c/strong\u003esummarizes the model.\u003c/p\u003e\n\u003ch2\u003eMBC\u003c/h2\u003e\n\u003cp\u003eSince 2008, principles of MBC (26) had been implemented, including the continuous collection of diagnosis-specific patient-reported outcome measures (PROMs). For follow-up of the treatment of patients with depression the PHQ-9 (17) was the primary measure. Therefore, relevant outcome data were available with sufficient coverage on a year-to-year basis.\u003c/p\u003e\n\u003ch2\u003eBenchmarking\u003c/h2\u003e\n\u003cp\u003eA benchmarking audit was conducted during the years 2019 through 2021. Data from all available pre- and post-treatment measurements of PHQ-9 were compiled via an electronic assessment tool and visually presented to the clinic managers and staff. The meta-analysis of Hans \u0026amp; Hiller (2013) was used as a gold standard comparison of the clinical outcomes. However, as there was more comprehensive and updated data available in a more recent publication, we then used the meta-analysis by \u0026Ouml;st and co-workers in our statistical analyses (27). This meta-analysis included both RCTs and pre-post trials in adult populations of depressed patients, which enabled effect size calculations of within-group change of disorder-specific depressive symptoms. This meta-analysis used both interviewer-based ratings and self-rating measures. However, since the clinical data included in the current study only used patient-reported measures, we included only studies from the meta-analysis that provided data on a reliable and valid patient report measure of depressive symptoms. For a detailed description, see the original publication (27).\u003c/p\u003e\n\u003ch2\u003eQuality control and improvement procedure\u003c/h2\u003e\n\u003cp\u003eDuring the benchmarking audit in 2019 to 2021, the clinic managers identified the treatment outcomes for depression as an area for improvement, as the outcomes were below the chosen benchmark (9). As a result of this decision, a quality improvement cycle was initiated.\u003c/p\u003e\n\u003cp\u003eThe CQI process that was used in the current case was the FOCUS-PDCA cycle (16), consisting of the following steps: Find a process to improve (F), organize a knowledgeable team (O), clarify current knowledge of the process (C), understand sources of process variations (U), and select improvements (S), plan an approach (P), do the activity (D), check the results (C) and act on the results (A).\u003c/p\u003e\n\u003cp\u003eAs a first step of the FOCUS-PDA cycle, the quality assurance manager (coauthor FL) together with the clinic managers (coauthors LWA, IW) identified that depression treatment outcomes were not in line with the research benchmark (9). A clinical team of three clinical psychologists and two psychiatrists was organized (O) to clarify the knowledge regarding current diagnostic and treatment routines for patients with depression (C). To gain a better understanding of process variations and areas for improvement (U), a systematic review of medical records of completed depression treatments was carried out by the clinical team. This review resulted in two insights:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eInstead of one patient population of individuals with clinical depression, when the onset and course of symptoms were considered more closely, a categorization into three distinct sub-groups appeared more appropriate: A. Patients with a primary diagnosis of major depression, B. Patients with depression as a comorbid condition to an anxiety disorder, C. Patients with depression as a comorbid condition to a personality disorder.\u003c/li\u003e\n \u003cli\u003eIf diagnostic routines were modified according to this subcategorization, the improved clinical assessments should lead to more appropriate allocation of patients to effective treatment options, and thus promote improved treatment outcomes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe team then selected the above two aspects as relevant improvements (S). The results from the systematic analysis of the medical records were presented to and jointly discussed with the clinical staff at the two clinics. A schedule was planned for the roll out of updated routines for diagnostic and clinical assessments as well as treatment allocation (P). The new routines were implemented in the second half of 2021 (D) and followed-up (C) in 2022 and 2023, see Results section below. Due to the positive results of this quality improvement cycle, the results were then implemented as standard routines at the clinics as well as shared with other clinics in the organization (A).\u003c/p\u003e\n\u003ch2\u003eAssessment\u003c/h2\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eParticipants were assessed before and after treatment. For the clinical outcome measurements of clinical depression the Patient Health Questionnaire-9 (PHQ-9 , Kroenke et al., 2001) was used.\u003c/p\u003e\n\u003ch3\u003eBackground variables\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e. Since the lifetime prevalence for depressive disorders is higher for women than men we record this variable as percent females (28). \u003cem\u003eMean age\u003c/em\u003e. There is no indication that the age of the participants is a significant predictor of outcome among adults, but it is presented as a basic background variable. \u003cem\u003eNumber of treatment sessions\u003c/em\u003e. It is important when comparing studies in routine clinical care and research settings that the number of therapy sessions does not deviate substantially from what the developers of CBT methods designed, i.e., 12-16 sessions. Thus, it is important to record the number of therapy sessions. \u003cem\u003ePre-treatment severity\u003c/em\u003e. It is well-known that the pre-treatment score on the outcome measure is a significant predictor of the post-treatment score (27,29). We recorded the sample\u0026rsquo;s mean score as percent of the maximum score possible on the instrument in question.\u003c/p\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003cp\u003eYear-by-year treatment outcomes on the PHQ-9 were compared with the most recent meta-analytic benchmark (27). The meta-analytic benchmark study included different self-report measures. Since all the measures were reliable and valid as patient-reported measures of depression, we pooled their pre- and post-treatment results to derive a mean effect size for the respective disorder. Within-group effect size (ES) was calculated as (Mpre \u0026minus; Mpost)/SDpre according to recommendation by Lakens (2013), as there is good reason to assume that the interventions influence not only the means but also the standard deviations. The mean ES was computed by weighting each ES by the inverse of its variance. When a study presented intent-to-treat data these were used, if not completer data were used. The software \u003cem\u003eComprehensive Meta-Analysis v.4\u003c/em\u003e (31) was used for all analyses, and to correct for small sample sizes, Hedges\u0026rsquo; g was calculated.\u003c/p\u003e\n\u003cp\u003eWe compared the means on background variables and mean ES for the outcome variables in the following way. If the clinic\u0026rsquo;s mean was within the 95% confidence interval (CI) of the meta-analysis we considered it not a significant difference, and if the mean was below or above the CI we considered it a significant difference. Whether the clinical mean was better or worse depended on the individual background variable, whereas for ES a mean below the CI means a significantly worse, and above the CI a significantly better outcome. Regarding the year-by-year analysis of treatment outcomes, we considered a significant difference if the clinical and benchmark confidence intervals did not overlap and non-significant if they did overlap.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBackground data\u003c/h2\u003e\n\u003cp\u003eA comparison between the clinical and meta-analytic benchmark samples on background variables is presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. There was no difference regarding percent female patients and mean age of the participants. However, the clinical sample had a higher pre-treatment depression severity score, received more therapy sessions, and had a lower attrition rate than the meta-analyric benchmark.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Background variables for the clinical and benchmark samples.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.29139%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.78146%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.9073%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% females\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7285%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0795%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Pre Tx severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0662%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean number of sessions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% data attrition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.29139%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.78146%;\"\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.9073%;\"\u003e\n \u003cp\u003e63.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7285%;\"\u003e\n \u003cp\u003e38.2 (SD = 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0795%;\"\u003e\n \u003cp\u003e58.7 H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0662%;\"\u003e\n \u003cp\u003e16.1\u0026nbsp;\u003cbr\u003e(SD = 13.2) H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e14.7 L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBenchmark\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.29139%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.78146%;\"\u003e\n \u003cp\u003e3525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.9073%;\"\u003e\n \u003cp\u003e66.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7285%;\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0795%;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0662%;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.29139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.78146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.9073%;\"\u003e\n \u003cp\u003e(62.8-70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7285%;\"\u003e\n \u003cp\u003e(36.5-41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0795%;\"\u003e\n \u003cp\u003e(45.1-51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0662%;\"\u003e\n \u003cp\u003e(10.9-14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0728%;\"\u003e\n \u003cp\u003e(17.9-29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e \u003cem\u003ek\u003c/em\u003e = number of samples or studies. N = number of participants. CI = confidence interval. Tx = treatment. L = lower than benchmark, H = higher than benchmark.\u003c/p\u003e\n\u003ch2\u003eYear-by-year analysis\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e shows the effects of depression treatments divided by year, in comparison with the mean pre-post ES of 1.51 for the effectiveness studies (see as well Supplementary materials Table S1). Clinical treatment effects were significantly lower than that of MA effectiveness studies in 2019 to 2021. For 2022 and 2023 the treatment effect was non-inferior to the MA studies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to evaluate a quality improvement cycle targeted on depression treatment outcomes. The quality improvement process was based on MBC data collected in specialized outpatient mental healthcare and a meta-analytic research benchmark. We found that treatment outcomes improved as findings from the benchmarking and CQI phases were implemented in clinical practice. As a result, treatment outcomes were on par with the meta-analytic benchmark in 2022 and 2023.\u003c/p\u003e \u003cp\u003eWhile the best available benchmark was used for the benchmarking audit in the initial phase of the project (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), we then used a more recent and more comprehensive meta-analysis for the analyses (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In addition, the meta-analysis of \u0026Ouml;st et al. (2023) provided a more conservative benchmark, with an effect size of d\u0026thinsp;=\u0026thinsp;1.51, compared to that of Hans and Hiller (2013), g\u0026thinsp;=\u0026thinsp;1.26.\u003c/p\u003e \u003cp\u003eWe think several insights can be drawn from this study. Firstly, as MBC was implemented several years before this study, good quality data on PROMs were readily available. This availability of relevant outcome data, in this case PHQ-9 measurements from pre- and post-treatment, made it possible for the team of clinic managers, quality assurance staff and researchers to conduct a benchmark with meta-analytic results. Thus, the routinely implemented collection of PROMs was a key prerequisite for a systematic comparison with a research benchmark, which in turn is a necessity to identify areas for improvement. However, previous studies indicated that the use of MBC and routinely collected measures is widely underutilized, with only 13.9% of clinicians using such measures at least monthly and the majority, 61.5%, never uses them (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Efforts to better understand barriers for implementation and to facilitate the use of PROMs are therefore an important first step.\u003c/p\u003e \u003cp\u003eSecondly, the application of a FOCUS-PDCA cycle to the sub-optimal clinical outcomes resulted in an effective improvement of the underlying care processes. As improvements in clinical routines were implemented, the clinical outcomes improved and approached the effect sizes of the meta-analytic benchmark. Whereas the study design does not allow causal inference, the improvements in outcome were in close temporal association with improvements made in the clinical routines. A possible next step would be to replicate the findings from our study with an experimental control group design, e.g., by randomizing clinics to a CQI condition and compare treatment outcomes to clinics without such an intervention.\u003c/p\u003e \u003cp\u003eThirdly, the improvements in clinical routines were mainly focused on a better case conceptualization and improved diagnostic understanding of the patient population. It appears that the improvement of diagnostics facilitated more appropriate allocation of patients to effective treatments and/or treatment planning. The design of our study does however not allow for a confirmation of this hypothesis. Future research should investigate the role of diagnostic accuracy and correct case conception on treatment outcome.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThe results from study are limited by several aspects. First, the study design does not allow causal inference, and therefore we cannot rule out that other factors have influenced or biased our findings. Studies involving rigorous control conditions are therefore warranted. Second, as hypothesized previously, it appears that an improvement in diagnostic assessments contributed to an improvement of treatment outcomes. However, the study design does not allow an evaluation of this hypothesis. Therefore, eloquently designed studies that test the importance of accurate diagnostic assessments for treatment outcomes are warranted. Thirdly, the current study focused on depression treatment due to the initially sub-optimal results. It would be important to test the comprehensive quality improvement method used in this study in other contexts and clinical populations to gain better generalizability.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eResults from this study indicate that the combination of MBC, routinely collected PROMs, benchmarking and CQI promoted improved results for depression treatments in specialized mental healthcare. The findings warrant replication in well-controlled study designs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCQI \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Continuous Quality Improvements\u003cbr\u003eCBT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cognitive behavior therapy\u003cbr\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence interval\u003cbr\u003eES\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Effect size\u003cbr\u003eFOCUS-PDCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Find a process to improve (F), organize a knowledgeable team (O), clarify current knowledge of the process (C), understand sources of process variations (U), and select improvements (S), plan an approach (P), do the activity (D), check the results (C) and act on the results (A).\u003cbr\u003ek\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Number of studies\u003cbr\u003eMBC \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Measurement-based Care\u003cbr\u003eN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sample size / observations\u003cbr\u003ePROMs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Patient-reported Outcome Measurements\u003cbr\u003eSSRIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Selective Serotonin Reuptake Inhibitors\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Swedish Ethical Review Authority (application ID: 2024-08303-02) and was exempted from participant consent as all data was collected according to standard clinical routines.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eData is not available due to Swedish and EU law applicable to sensitive information.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study did not receive dedicated funding.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eFL contributed to the study design, data analysis and interpretation, manuscript writing and reviewing. LWA and IV coordinated the CQI process. EA contributed to manuscript writing and reviewing. LG\u0026Ouml; conducted the meta-analytic benchmark analyses and contributed to the manuscript writing and reviewing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepression WHO. and Other Common Mental Disorders, Global Health Estimates [Internet]. 2017 [cited 2024 Oct 16]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/depression-global-health-estimates\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/depression-global-health-estimates\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler RC. The Costs of Depression. Psychiatr Clin North Am. 2012;35(1):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychological Association. Clinical practice guideline for the treatment of depression across three age cohorts [Internet]. 2019 [cited 2024 Oct 24]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.apa.org/doi/10.1037/e505892019-001\u003c/span\u003e\u003cspan address=\"https://doi.apa.doi/10.1037/e505892019-001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Board of Health and Welfare. National Board of Health and Welfare. 2019 [cited 2024 May 14]. National guidelines for treatment of depression and anxiety disorders. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.socialstyrelsen.se/kunskapsstod-och-regler/regler-och-riktlinjer/nationella-riktlinjer/riktlinjer-och-utvarderingar/depression-och-angest/\u003c/span\u003e\u003cspan address=\"https://www.socialstyrelsen.se/kunskapsstod-och-regler/regler-och-riktlinjer/nationella-riktlinjer/riktlinjer-och-utvarderingar/depression-och-angest/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICE. Depression in adults: treatment and management [Internet]. National Institute for Health and Care Excellence; 2022. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ng222\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/guidance/ng222\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiharova M, Furukawa TA, Efthimiou O, Karyotaki E, Miguel C, Noma H, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol. 2021;89(6):563\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHans E, Hiller W. Effectiveness of and dropout from outpatient cognitive behavioral therapy for adult unipolar depression: a meta-analysis of nonrandomized effectiveness studies. J Consult Clin Psychol. 2013;81(1):75\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet Lond Engl. 2018;391(10128):1357\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez-Meca J, Mar\u0026iacute;n-Mart\u0026iacute;nez F, Chac\u0026oacute;n-Moscoso S. Effect-size indices for dichotomized outcomes in meta-analysis. Psychol Methods. 2003;8(4):448\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuijpers P, van Straten A, Warmerdam L, Andersson G. Psychotherapy versus the combination of psychotherapy and pharmacotherapy in the treatment of depression: a meta-analysis. Depress Anxiety. 2009;26(3):279\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornicroft G, Chatterji S, Evans-Lacko S, Gruber M, Sampson N, Aguilar-Gaxiola S, et al. Undertreatment of people with major depressive disorder in 21 countries. Br J Psychiatry. 2017;210(2):119\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilbourne AM, Beck K, Spaeth-Rublee B, Ramanuj P, O\u0026rsquo;Brien RW, Tomoyasu N, et al. Measuring and improving the quality of mental health care: a global perspective. World Psychiatry. 2018;17(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBramesfeld A, Amaddeo F, Caldas-de-Almeida J, Cardoso G, Depaigne-Loth A, Derenne R, et al. Monitoring mental healthcare on a system level: Country profiles and status from EU countries. Health Policy. 2016;120(6):706\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndalamaw A, Khatri RB, Mengistu TS, Erku D, Wolka E, Zewdie A, et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv Res. 2024;24(1):487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, Hong RH, Yang T, Yang X, Wang X, Liu J, et al. The Efficacy of Measurement-Based Care for Depressive Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Clin Psychiatry. 2021;82(5):21r14034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayney R. Benchmarking in mental health: an introduction for psychiatrists. Adv Psychiatr Treat. 2005;11(4):305\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelgadillo J, McMillan D, Leach C, Lucock M, Gilbody S, Wood N. Benchmarking Routine Psychological Services: A Discussion of Challenges and Methods. Behav Cogn Psychother. 2014;42(1):16\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickberg F, Lenhard F, Aspvall K, Serlachius E, Andr\u0026eacute;n P, Johansson F, et al. Feasibility of internet-delivered cognitive-behavior therapy for obsessive-compulsive disorder in youth with autism spectrum disorder: A clinical benchmark study. Internet Interv. 2022;28:100520.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillmington C, Belardi P, Murante AM, Vainieri M. The contribution of benchmarking to quality improvement in healthcare. A systematic literature review. BMC Health Serv Res. 2022;22(1):139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWells KB, Sherbourne C, Schoenbaum M, Duan N, Meredith L, Un\u0026uuml;tzer J, et al. Impact of Disseminating Quality Improvement Programs for Depression in Managed Primary CareA Randomized Controlled Trial. JAMA. 2000;283(2):212\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidoff F, Batalden P, Stevens D, Ogrinc G, Mooney SE. Publication guidelines for quality improvement studies in health care: evolution of the SQUIRE project. BMJ. 2009;338:a3152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegion Stockholm. Kunskapsst\u0026ouml;d f\u0026ouml;r v\u0026aring;rdgivare [Internet]. 2024 [cited 2024 May 14]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kunskapsstodforvardgivare.se/omraden/psykisk-halsa\u003c/span\u003e\u003cspan address=\"https://kunskapsstodforvardgivare.se/omraden/psykisk-halsa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis CC, Boyd M, Puspitasari A, Navarro E, Howard J, Kassab H, et al. Implementing Measurement-Based Care in Behavioral Health: A Review. JAMA Psychiatry. 2019;76(3):324\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;st LG, Enebrink P, Finnes A, Ghaderi A, Havnen A, Kvale G, et al. Cognitive behavior therapy for adult depressive disorders in routine clinical care: A systematic review and meta-analysis. J Affect Disord. 2023;331:322\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, et al. Epidemiology of Adult DSM-5 Major Depressive Disorder and Its Specifiers in the United States. JAMA Psychiatry. 2018;75(4):336\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;st LG, Enebrink P, Finnes A, Ghaderi A, Havnen A, Kvale G, et al. Cognitive behavior therapy for adult post-traumatic stress disorder in routine clinical care: A systematic review and meta-analysis. Behav Res Ther. 2023;166:104323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol [Internet]. 2013 Nov 26 [cited 2024 Aug 14];4. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/psychology/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/psychology/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2013.00863/full\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2013.00863/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. Introduction to Meta-Analysis. 2022. 1\u0026ndash;421 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen-Doss A, Haimes EMB, Smith AM, Lyon AR, Lewis CC, Stanick CF, et al. Monitoring Treatment Progress and Providing Feedback is Viewed Favorably but Rarely Used in Practice. Adm Policy Ment Health Ment Health Serv Res. 2018;45(1):48\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5797396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5797396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepression is a leading cause of global burden of disease. Evidence-based interventions, such as psychotherapy and pharmacotherapy, are effective in alleviating depressive symptoms; however, challenges regarding accessibility of good quality care persist. This study investigated a Continuous Quality Improvement (CQI) process that incorporated measurement-based care and benchmarking to enhance treatment outcomes for depression within specialized mental healthcare settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized data from two outpatient mental health clinics in Stockholm, Sweden. In a first step, the study employed patient-reported outcome measures (PROMs) to compare clinical treatment results with a meta-analytic benchmark (\u0026Ouml;st et al. 2023). In a second step, a CQI framework was implemented to improve treatment outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total sample size of N\u0026thinsp;=\u0026thinsp;415 patients treated for depression was included in the analyses. The clinical sample demonstrated higher pre-treatment depression severity scores, more appointments, and a lower data attrition rate compared to the meta-analytic benchmark. Year-to-year analyses indicated that clinical treatment effects were initially significantly lower (Hedge\u0026rsquo;s \u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.87 to 1.01, 95% CI[0.58\u0026ndash;1.16]) compared to the meta-analytic benchmark (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.51, 95% CI [1.36\u0026ndash;1.65]). However, when the results from the CQI process were implemented, treatment effects improved and were found to be non-inferior to the meta-analytic benchmark (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25 to1.27, 95% CI [0.94\u0026ndash;1.61]).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings suggest that the integration of measurement-based care, benchmarking, and CQI have the potential to improve treatment outcomes for depression in specialized mental healthcare.\u003c/p\u003e","manuscriptTitle":"Enhancing Clinical Depression Treatment Outcomes: A Comprehensive Approach using Measurement-based Care, Research Benchmarks, and Systematic Quality Improvements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 11:52:39","doi":"10.21203/rs.3.rs-5797396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1b9c1f5-2cf7-43d5-b3d5-a55c0145c14e","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T10:17:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 11:52:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5797396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5797396","identity":"rs-5797396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}