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A computer-based questionnaire can generate responses that are equivalent to the responses to a traditional personal interview. In some cases, a computer may be more successful in eliciting risk factors. Further studies of the application of this technology for patient education and physician efficiency can now be carried out, knowing that subjects respond reproducibly to a computer interview format.

Objective This article describes lessons learned from the collaborative creation of logical models and standard Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) profiles for family planning and reproductive health. The National Health Service delivery program will use the FHIR profiles to improve federal reporting, program monitoring, and quality improvement efforts. Materials and Methods Organizational frameworks, work processes, and artifact testing to create FHIR profiles are described. Results Logical models and FHIR profiles for the Family Planning Annual Report 2.0 dataset have been created and validated. Discussion Using clinical element models and FHIR to meet the needs of a real-world use case has been accomplished but has also demonstrated the need for additional tooling, terminology services, and application sandbox development. Conclusion FHIR profiles may reduce the administrative burden for the reporting of federally mandated program data.

Background The Centers for Disease Control and Prevention (CDC) produced a 72-page document titled “U.S. Selective Practice Recommendations for Contraceptive Use” in 2016. This document contains the medical eligibility criteria (MEC) for contraceptive initiation or continuation based on a patient's current health status. Notations such as Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) might be useful to model such recommendations. Objective Our objective was to use BPMN and DMN to model and standardize the processes and decisions involved in initiating birth control according to the CDC's MEC for birth control initiation. This model could then be incorporated into an electronic health records system or other digital platform. Methods Medical terminology, processes, and decisions were modeled in coordination with the CDC to ensure correctness. Challenges in terminology bindings were identified and categorized. Results A model was successfully produced. Integration of clearly defined data elements proved to be the biggest challenge. Conclusion BPMN and DMN have strengths and weaknesses when modeling medical processes; however, they can be used to successfully create models for clinical pathways.