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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.

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I n the 15 yr since the publication of the Institute of Medicine report highlighting the need to reduce medical errors and improve patient safety, 1 complications after childbirth have become more common, not less common. 2,3 The number of pregnancy-related deaths in the United States increased from 7.2 to 17.3 per 100,000 between 1987 and 2013. 4 Many pregnancy-related deaths, such as those due to hemorrhage and preeclampsia, are preventable 5,6 and the quality of obstetrical care across U.S. hospitals is uneven. 7,8 Rising rates of maternal deaths and severe morbidity led the American College of Obstetricians and Gynecologists and the American Society of Anesthesiologists to create the Maternal Quality Improvement Program (Washington, D.C.) outcomes registry to serve as a platform for reporting risk-adjusted outcome metrics and improving the quality of obstetrical care. 9 Currently available outcome measures for obstetrical care 10-13 are limited because they are not risk-adjusted and do not account for differences in hospital case mix. To differentiate between obstetrical teams that provide aBStract Background: The number of pregnancy-related deaths and severe maternal complications continues to rise in the United States, and the quality of obstetrical care across U.S. hospitals is uneven. Providing hospitals with performance feedback may help reduce the rates of severe complications in mothers and their newborns. The aim of this study was to develop a risk-adjusted composite measure of severe maternal morbidity and severe newborn morbidity based on administrative and birth certificate data. Methods: This study was conducted using linked administrative data and birth certificate data from California. Hierarchical logistic regression prediction models for severe maternal morbidity and severe newborn morbidity were developed using 2011 data and validated using 2012 data. The composite metric was calculated using the geometric mean of the risk-standardized rates of severe maternal morbidity and severe newborn morbidity. results: The study was based on 883,121 obstetric deliveries in 2011 and 2012. The rates of severe maternal morbidity and severe newborn morbidity were 1.53% and 3.67%, respectively. Both the severe maternal morbidity model and the severe newborn models exhibited acceptable levels of discrimination and calibration. Hospital risk-adjusted rates of severe maternal morbidity were poorly correlated with hospital rates of severe newborn morbidity (intraclass correlation coefficient, 0.016). Hospital rankings based on the composite measure exhibited moderate levels of agreement with hospital rankings based either on the maternal measure or the newborn measure (κ statistic 0.49 and 0.60, respectively.) However, 10% of hospitals classified as average using the composite measure had below-average maternal outcomes, and 20% of hospitals classified as average using the composite measure had below-average newborn outcomes. conclusions: Maternal and newborn outcomes should be jointly reported because hospital ...

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