Delivery of the HIV Service and Telemedicine Through Effective Patient-Reported Outcomes (+STEP) Intervention to Increase Screening and Treatment of Mental Health and Substance Use Disorders for People Living With HIV in Alabama: Protocol for an Effectiveness-Implementation Study
Bibliographic record
Abstract
BACKGROUND: The syndemic of mental health (MH) and substance use disorders (SUDs) is common among persons living with HIV and jeopardizes HIV treatment adherence, engagement in care, and viral load suppression. Electronic patient-reported outcomes (ePROs), completed through tablet or computer, and telemedicine are evidence- and technology-based interventions that have been used to successfully increase screening and treatment, respectively, a model that holds promise for persons living with HIV. To date, there is limited guidance on implementing ePROs and telemedicine into HIV clinical practice even though it is well known that these evidence-based tools improve diagnosis and access to care. OBJECTIVE: To address this, we aim to conduct a multicomponent intervention for persons living with HIV, including the delivery of HIV services and telemedicine through effective ePROs (+STEP), to increase screening and treatment of MH and SUD in Ryan White HIV/AIDS Program (RWHAP)-funded clinics in Alabama. METHODS: Through this intervention, we will conduct a readiness, acceptability, and accessibility assessment and implement +STEP to improve the diagnosis and treatment of MH and SUD at RWHAP clinics in Alabama. To describe implementation strategies that address barriers to the uptake of +STEP in RWHAP clinics, we will conduct qualitative interviews in years 1 (early implementation), 2 (scale up), and 4 (maintenance) with patients and key staff to evaluate barriers, facilitators, and implementation strategies. Our Results will enable us to modify strategies to enhance +STEP penetration over time and inform the implementation blueprint, which we will develop for both RWHAP clinics in Alabama and future sites. We will assess the impact of implementing +STEP on diagnoses, referrals, and health care use related to MH, SUD, and HIV by comparing clinical outcomes from patients receiving these interventions (ePROs and telemedicine) with historical controls. RESULTS: The first study site began implementation in April 2022. A total of 2 additional sites have initiated ePROs. Final results are expected in 2026. The results of this study will provide a foundation for future research expanding access to ePROs for improved diagnosis linked to telemedicine access to accelerate patients along the continuum of care from MH and SUD diagnosis to treatment. CONCLUSIONS: Achieving the end of the HIV epidemic in the United States necessitates programs that accelerate movement across the MH and SUD care continuum from diagnosis to treatment for persons living with HIV. Scaling these services represents a path toward improved treatment outcomes with both individual health and population-level prevention benefits of sustained HIV viral suppression in the era of undetectable=untransmittable (U=U). This study will address this evidence gap through the evaluation of the implementation of +STEP to establish the necessary systems and processes to screen, identify, and better treat substance use and MH for people living with HIV. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40470.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".