A Standards‐Based, Cloud‐Hosted <scp>CDS</scp> System for Gonorrhea Treatment and <scp>HIV</scp> Screening
Objective: (or gonococcus, GC) infections have increased in incidence and antibiotic resistance, prompting updates to management guidelines. Additionally, patients with recent GC infections have been identified as a target population for human immunodeficiency virus (HIV) screening and prevention. With the rapidly changing nature of such biomedical knowledge, there is an imperative to adopt public health guidelines speedily to deliver knowledge to clinicians at the point of care. Therefore, the Public Health Informatics Institute and the United States Centers for Disease Control and Prevention sought to create a scalable clinical decision support (CDS) solution that could be easily integrated using interoperability standards with widely used electronic health record (EHR) systems in the United States. Methods: A project team including informaticians and clinical partners developed a cloud-hosted CDS system, with rule logic written in the Health Level Seven (HL7) Clinical Quality Language and using the HL7 CDS Hooks protocol. The system was designed to present alerts to clinicians who, based on available data, appeared to be treating uncomplicated GC with doses or agents that were not recommended based on the patient's weight, allergies, and other characteristics. Actionable suggestions were offered for the correct regimen, and HIV testing, if applicable. Results: After extensive testing, the system was successfully integrated with the EHR system at two academic medical institutions. Subsequently, a three-month pilot was conducted in emergency and urgent care settings. Conclusions: There were limitations related to functionality, workflow issues, and data quality. This successful implementation, however, demonstrates the feasibility of this approach for select use cases of CDS, and yields further understanding of the requirements for developing scalable solutions for disseminating public health guidance.
Authors
- Sanjat Kanjilal (ORCID: https://orcid.org/0000-0002-1221-5725)
- Ritche Hao
- Amrita Tailor (ORCID: https://orcid.org/0000-0003-0962-8454)
- Alejandro Pérez
- Saugat Karki (ORCID: https://orcid.org/0000-0003-2161-678X)
- Carlos Paredes (ORCID: https://orcid.org/0000-0002-6266-4473)
- Jeanne Ocampo (ORCID: https://orcid.org/0009-0005-7669-5296)
- Matthew Pooser
- Sarah Shaw (ORCID: https://orcid.org/0009-0006-3871-7463)
- Aziz Boxwala
- David Liebovitz (ORCID: https://orcid.org/0000-0002-2518-5940)
- Andre Berro
- Jane E. Yang
- Edna Shenvi (ORCID: https://orcid.org/0000-0002-2995-230X)
- Gema Dumitru
- Nitu Kashyap
Institutions
- Northwestern University (US)
- Centers for Disease Control and Prevention (US)
- Emory Healthcare (US)
- Harvard University (US)
- Cerritos College (US)
- Yale New Haven Health System (US)
- Georgia Department of Public Health (US)
Publication Details
- Journal
- Learning Health Systems
- Published
- 2026-07-30
- DOI
- https://doi.org/10.1002/lrh2.70104
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centers for Disease Control and Prevention