VitalSign6: A Primary Care First (PCP-First) Model for Universal Screening and Measurement-Based Care for Depression
Open accessAbstract
Major depressive disorder affects one in five adults in the United States. While practice guidelines recommend universal screening for depression in primary care settings, clinical outcomes suffer in the absence of optimal models to manage those who screen positive for depression. The current practice of employing additional mental health professionals perpetuates the assumption that primary care providers (PCP) cannot effectively manage depression, which is not feasible, due to the added costs and shortage of mental health professionals. We have extended our previous work, which demonstrated similar treatment outcomes for depression in primary care and psychiatric settings, using measurement-based care (MBC) by developing a model, called Primary Care First (PCP-First), that empowers PCPs to effectively manage depression in their patients. This model incorporates health information technology tools, through an electronic health records (EHR) integrated web-application and facilitates the following five components: (1) Screening (2) diagnosis (3) treatment selection (4) treatment implementation and (5) treatment revision. We have implemented this model as part of a quality improvement project, called VitalSign6, and will measure its success using the Reach, Efficacy, Adoption, Implementation, and Maintenance (RE-AIM) framework. In this report, we provide the background and rationale of the PCP-First model and the operationalization of VitalSign6 project.
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The CBT-based intervention SPARX was assessed for noninferiority to treatment as usual in adolescents aged 12 to 19 years who were seeking help for depressive symptoms at primary care sites in a randomized controlled trial (n = 94 and n = 93, respectively).
“The CBT-based intervention SPARX was assessed for noninferiority to treatment as usual in adolescents aged 12 to 19 years who were seeking help for depressive symptoms at primary care sites in a randomized controlled trial (n = 94 and n = 93, respectively).77”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [77]
Digital health technologies for depression gained broad public attention after the release of the SuperBetter app, MoodHacker app, and SPARX, which are self-management tools to reduce depressive symptoms supported by randomized controlled trial data.
“Digital health technologies for depression gained broad public attention after the release of the SuperBetter app, 75 MoodHacker app, 76 and SPARX, 77 which are self-management tools to reduce depressive symptoms supported by randomized controlled trial data.”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [77]
SPARX was noninferior to treatment as usual following intervention and at the 3-month follow-up, as assessed by the observer-rated children's depression rating scale-revised.
“SPARX was noninferior to treatment as usual following intervention and at the 3-month follow-up, as assessed by the observerrated children's depression rating scale-revised.77”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [77]
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Digital health technologies and major depressive disorder
McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
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Self-citation
- Authors
- Trivedi, M., Jha, M., Kahalnik, F., Pipes, R., Levinson, S., Lawson, T., Rush, A., Trombello, J., Grannemann, B., Tovian, C., Kinney, R., Clark, E., & Greer, T.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Texas Tech University (US)
- Corresponding author
- Trivedi, Madhukar
- ORCID
- Venue
- Pharmaceuticals
- Publisher
- MDPI AG
- Published
- 2019-05-14
- Volume
- 12
- Issue
- 2
- Pages
- 71
- ISSN
- 1424-8247 (ISSN-L)
- PMC
- PMC6630588
- OpenAlex
- W2944888170
- Cited by
- 49
- References
- 74
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 13 · 2023: 7 · 2022: 3 · 2021: 9 · 2020: 10 · 2019: 1
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Pharmaceuticals (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Meadows Foundation (N/A)
- Hersh Foundation (N/A)
- Rees-Jones Foundation (N/A)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- major depressive disorder, depression screening, measurement-based care, primary care, electronic health records, web application, treatment selection, RE-AIM framework, quality improvement, mental health workforce shortage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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