Clinical decision support

Irene Demarie, Michael E Bowen +9

Can a text-based treatment guideline be translated into executable logic inside an EHR? This implementation case study describes a multidisciplinary team using agile methods to convert the American Diabetes Association Standards of Care for type 2 diabetes with cardiovascular or renal disease into structured algorithms and ontology groupers for diagnoses, labs, and medications, built on SNOMED CT, LOINC, and RxNorm. The work yielded three tools: a real-time registry identifying patients eligible for guideline-directed therapy, filterable to population and individual-provider treatment gaps, plus two clinical decision support instruments embedded in clinician workflows. The authors describe the process as feasible but complex and labor-intensive, and call for guideline organizations to supply technical frameworks, regular updates, and vendor collaboration. The abstract reports no effect sizes or utilization outcomes.

Adesuwa Emovon, Lauren Driggers-Jones +5

What would clinicians and caregivers want from an AI-based clinical decision support tool for early autism detection, and where would it fit in the visit? This observational qualitative study used contextual inquiry with 8 clinicians and 20 caregivers during 18- to 24-month well-child visits at Duke-affiliated clinics, analyzed with rapid qualitative analysis. Workflow mapping identified 6 user tasks, 3 technology-user interactions, and 5 clinical decision points, plus 2 barriers (screening tool accuracy, follow-up implementation) and 3 facilitators (electronic screening, early intervention provider input, referral coordination support). Preferences included EHR-embedded, actionable outputs with prediction explanations, visual summaries, and caregiver-facing materials. The abstract reports no effect sizes.

Emma McCord, Nate C Apathy +5

What clinician characteristics moderate use of a chronic pain clinical decision support tool? Using electronic health record data from a pragmatic randomized controlled trial (October 2019–May 2022) covering 69 primary care clinicians with access to the OneSheet CDS, investigators modeled tool access within three days of an encounter, with generalized linear models testing clinician gender and years in practice as moderators. OneSheet was used in 959 of 145,511 encounters (0.7%). Use was lower for new-patient encounters (−0.42 percentage points; 95% CI −0.65 to −0.19) and higher for chronic pain diagnoses (3.48 pp) and long-term opioid therapy (4.70 pp). Associations were strongest among female clinicians with over 16 years in practice; prior use did not predict future use.

Huixun Jia, Bo Qian +35

Can an image-based AI narrow the genotype search for inherited retinal diseases before genetic testing? Retina4IRD, a RETFound-pretrained Vision Transformer predicting 17 genotype categories, was trained on fundus photographs and OCT from 1,843 genetically confirmed patients (3,376 eyes) in China, South Korea and Poland; top-5 accuracy was 0.904 internally and 0.856 externally. In a multicenter randomized trial, 300 patients with suspected IRD were assigned 1:1 to AI-assisted or specialist-only assessment (295 analyzed). Top-5 genetic accuracy was 88.5% versus 67.3% (P<0.001), top-1 37.8% versus 22.4%, and a composite downstream management score 37.7 versus 28.5 (P<0.001).

Justin C Zhang, Nirav R Bhakta +7

Can EHR-based clinical decision support safely curb excessive continuous pulse oximetry (CPO) monitoring? This quality improvement study at a 796-bed tertiary center used interrupted time series analysis across all non-ICU inpatient units, comparing June 2022–May 2023 (18,351 CPO-associated hospitalizations) with June 2023–May 2024 (18,713) after revised CPO orders, order sets, and a best practice advisory informed by semi-structured interviews. Against a pre-intervention monthly average of 120,771 CPO hours, the intervention was associated with an immediate reduction of 22,680 hours (95% CI −33,591 to −11,769), with monthly alarms down 332,819 and duration per admission down 34.2 hours. ICU transfers rose 9.6 per 1000 admissions; rapid responses, code blues, and mortality were unchanged.

Viswanadham, R. V. N., Jones, S. A. +2

Does provider engagement with clinical decision support alerts vary by patient race and sex? This retrospective study used EHR data on alert-based CDS during outpatient primary care at a New York City academic health system, applying logistic regression to model alert engagement by patient race and sex with adjustment for encounter and provider factors, and a generalized structural equation model to test mediation by alert type. Direct effects indicated differential provider response by patient demographics; indirect effects indicated unequal assignment of alert types across groups, so uneven exposure alone could yield inequitable outcomes. The abstract reports no effect sizes.</summary