A Clinical Decision Support Tool for Intimate Partner Violence Screening Among Women Veterans: Development and Qualitative Evaluation of Provider Perspectives
Bibliographic record
Abstract
BACKGROUND: Women veterans, compared to civilian women, are especially at risk of experiencing intimate partner violence (IPV), pointing to the critical need for IPV screening and intervention in the Veterans Health Administration (VHA). However, implementing paper-based IPV screening and intervention in the VHA has revealed substantial barriers, including health care providers' inadequate IPV training, competing demands, time constraints, and discomfort addressing IPV and making decisions about the appropriate type or level of intervention. OBJECTIVE: This study aimed to address IPV screening implementation barriers and hence developed and tested a novel IPV clinical decision support (CDS) tool for physicians in the Women's Health Clinic (WHC), a primary care clinic within the Veterans Affairs Palo Alto Health Care System. This tool provides intelligent, evidence-based, step-by-step guidance on how to conduct IPV screening and intervention. METHODS: Informed by existing CDS development frameworks, developing the IPV CDS tool prototype involved six steps: (1) identifying the scope of the tool, (2) identifying IPV screening and intervention content, (3) incorporating IPV-related VHA and clinic resources, (4) identifying the tool's components, (5) designing the tool, and (6) conducting initial tool revisions. We obtained preliminary physician feedback on user experience and clinical utility of the CDS tool via the System Usability Scale (SUS) and semistructured interviews with 6 WHC physicians. SUS scores were examined using descriptive statistics. Interviews were analyzed using rapid qualitative analysis to extract actionable feedback to inform design updates and improvements. RESULTS: This study includes a detailed description of the IPV CDS tool. Findings indicated that the tool was generally well received by physicians, who indicated good tool usability (SUS score: mean 77.5, SD 12.75). They found the tool clinically useful, needed in their practice, and feasible to implement in primary care. They emphasized that it increased their confidence in managing patients reporting IPV but expressed concerns regarding its length, workflow integration, flexibility, and specificity of information. Several physicians, for example, found the tool too time consuming when encountering patients at high risk; they suggested multiple uses of the tool (eg, an educational tool for less-experienced health care providers and a checklist for more-experienced health care providers) and including more detailed information (eg, a list of local shelters). CONCLUSIONS: Physician feedback on the IPV CDS tool is encouraging and will be used to improve the tool. This study offers an example of an IPV CDS tool that clinics can adapt to potentially enhance the quality and efficiency of their IPV screening and intervention process. Additional research is needed to determine the tool's clinical utility in improving IPV screening and intervention rates and patient outcomes (eg, increased patient safety, reduced IPV risk, and increased referrals to mental health treatment).
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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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".