Clinical Decision Support System for Guidelines-Based Treatment of Gonococcal Infections, Screening for HIV, and Prescription of Pre-Exposure Prophylaxis: Design and Implementation Study
Bibliographic record
Abstract
BACKGROUND: The syndemic nature of gonococcal infections and HIV provides an opportunity to develop a synergistic intervention tool that could address the need for adequate treatment for gonorrhea, screen for HIV infections, and offer pre-exposure prophylaxis (PrEP) for persons who meet the criteria. By leveraging information available on electronic health records, a clinical decision support (CDS) system tool could fulfill this need and improve adherence to Centers for Disease Control and Prevention (CDC) treatment and screening guidelines for gonorrhea, HIV, and PrEP. OBJECTIVE: The goal of this study was to translate portions of CDC treatment guidelines for gonorrhea and relevant portions of HIV screening and prescribing PrEP that stem from a diagnosis of gonorrhea as an electronic health record-based CDS intervention. We also assessed whether this CDS solution worked in real-world clinic. METHODS: We developed 4 tools for this CDS intervention: a form for capturing sexual history information (SmartForm), rule-based alerts (best practice advisory), an enhanced sexually transmitted infection (STI) order set (SmartSet), and a documentation template (SmartText). A mixed methods pre-post design was used to measure the feasibility, use, and usability of the CDS solution. The study period was 12 weeks with a baseline patient sample of 12 weeks immediately prior to the intervention period for comparison. While the entire clinic had access to the CDS solution, we focused on a subset of clinicians who frequently engage in the screening and treatment of STIs within the clinical site under the name "X-Clinic." We measured the use of the CDS solution within the population of patients who had either a confirmed gonococcal infection or an STI-related chief complaint. We conducted 4 midpoint surveys and 3 key informant interviews to quantify perception and impact of the CDS solution and solicit suggestions for potential future enhancements. The findings from qualitative data were determined using a combination of explorative and comparative analysis. Statistical analysis was conducted to compare the differences between patient populations in the baseline and intervention periods. RESULTS: Within the X-Clinic, the CDS alerted clinicians (as a best practice advisory) in one-tenth (348/3451, 10.08%) of clinical encounters. These 348 encounters represented 300 patients; SmartForms were opened for half of these patients (157/300, 52.33%) and was completed for most for them (147/300, 89.81%). STI test orders (SmartSet) were initiated by clinical providers in half of those patients (162/300, 54%). HIV screening was performed during about half of those patient encounters (191/348, 54.89%). CONCLUSIONS: We successfully built and implemented multiple CDC treatment and screening guidelines into a single cohesive CDS solution. The CDS solution was integrated into the clinical workflow and had a high rate of use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".