RESUMO
BACKGROUND: The American College of Obstetricians and Gynecologists (ACOG) provides numerous narrative documents containing formal recommendations and additional narrative guidance within the text. These guidelines are not intended to provide a complete "care pathway" for patient management, but these elements of guidance can be useful for clinical decision support (CDS) in obstetrical and gynecologic care and could be exposed within electronic health records (EHRs). Unfortunately, narrative guidelines do not easily translate into computable CDS guidance. OBJECTIVE: This study aimed to describe a method of translating ACOG clinical guidance into clear, implementable items associated with specific obstetrical problems for integration into the EHR. METHODS: To translate ACOG clinical guidance in Obstetrics into implementable CDS, we followed a set of steps including selection of documents, establishing a problem list, extraction and classification of recommendations, and assigning tasks to those recommendations. RESULTS: Our search through ACOG clinical guidelines produced over 500 unique documents. After exclusions, and counting only sources relevant to obstetrics, we used 245 documents: 38 practice bulletins, 113 committee opinions, 16 endorsed publications, 1 practice advisory, 2 task force and work group reports, 2 patient education, 2 obstetric care consensus, 60 frequently asked questions (FAQ), 1 women's health care guidelines, 1 Prolog series, and 9 others (non-ACOG). Recommendations were classified as actionable (n = 576), informational (n = 493), for in-house summary (n = 124), education/counseling (n = 170), policy/advocacy (n = 33), perioperative care (n = 4), delivery recommendations (n = 50), peripartum care (n = 13), and non-ACOG (n = 25). CONCLUSION: We described a methodology of translating ACOG narrative into a semi-structured format that can be more easily applied as CDS in the EHR. We believe this work can contribute to developing a library of information within ACOG that can be continually updated and disseminated to EHR systems for the most optimal decision support. We will continue documenting our process in developing executable code for decision support.