Search

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., Competing Interests: None declared., (Thieme. All rights reserved.)

Clinical Practice Guidelines (CPG), meant to express best practices in healthcare, are commonly presented as narrative documents communicating care processes, decision making, and clinical case knowledge. However, these narratives in and of themselves lack the specificity and conciseness in their use of language to unambiguously express quality clinical recommendations. This impacts the confidence of clinicians, uptake, and implementation of the guidance. As important as the quality of the clinical knowledge articulated, is the quality of the language(s) and methods used to express the recommendations. In this paper, we propose the BPM+ family of modeling languages as a potential solution to this challenge. We present a formalized process and framework for translating CPGs into a standardized BPM+ model. Further, we discuss the features and characteristics of modeling languages that underpin the quality in expressing clinical recommendations. Using an existing CPG, we defined a systematic series of steps to deconstruct the CPG into knowledge constituents, assign CPG knowledge constituents to BPM+ elements, and re-assemble the parts into a clear, precise, and executable model. Limitations of both the CPG and the current BPM+ languages are discussed., (©2020 AMIA - All rights reserved.)

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., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

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 rates of maternal morbidity and newborn morbidity are poorly correlated. This can be done using a childbirth composite measure alongside separate measures of maternal and newborn outcomes.

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., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

Advancing the quality and safety of maternity care should be data-driven. Defining a standard set of clinical data elements, across electronic health record platforms and facilities, could accelerate performance measurement, benchmarking, and identification of better practices. In 2014, the American College of Obstetricians and Gynecologists and the American Society of Anesthesiologists launched the Maternal Quality Improvement Program, a data-driven national clinical registry for maternity care. Having an agreed-on set of discrete data elements related to labor and delivery will set the stage for analysis of this care. Through the use of clinical performance measures and data quality metrics, the Maternal Quality Improvement Program will provide an opportunity for health care providers to better understand the overall quality and safety of the maternity care provided within their institution.

The amount of data generated by health information technology systems is staggering, and using those data to make meaningful care decisions that improve patient outcomes is difficult. The purpose of this article is to describe the Maternal Health Information Initiative, a multidisciplinary group of maternity care stakeholders charged with standardizing maternity care data. Complementary strategies that practicing clinicians can use to support this initiative and improve the usability of maternity care data are provided., (Copyright © 2017 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses. Published by Elsevier Inc. All rights reserved.)

Objective: To estimate whether text messages sent to ambulatory pregnant women could improve influenza vaccine uptake., Methods: Obstetric patients at less than 28 weeks of gestation were enrolled in a randomized controlled trial from an academic center's outpatient clinic during two consecutive influenza seasons (2010-2011 and 2011-2012). Potential participants were excluded if they had already received that season's influenza vaccine. Participants were randomized to receive 12 weekly text messages encouraging general pregnancy health (General) or general pregnancy health plus influenza vaccination (Flu). Study participants completed preintervention and postintervention surveys about preventive health beliefs. Influenza vaccine receipt was assessed using prenatal record review. The study was powered to detect a 55% increase in the vaccination rate in the intervention group., Results: Two hundred sixteen women were enrolled, 204 of whom were available for intention-to-treat analysis (n=100 General, n=104 Flu). Participants were primarily African American (66%) with low educational attainment (90% equivalent to or less than high school education) and predominantly with either public or no insurance (88%). The overall influenza vaccination rate among participants was 32% with no difference between participants in the General (31% [n=31]) compared with Flu (33% [n=34]) groups (difference 1.7%, 95% confidence interval -11.1 to 14.5%)., Conclusion: Text messaging prompts were not effective at increasing influenza vaccination rates among a low-income, urban, ambulatory obstetric population. Ongoing efforts are needed to improve vaccine uptake among pregnant women unsure about or unwilling to receive influenza vaccination., Clinical Trial Registration: ClinicalTrials.gov, www.clinicaltrials.gov, NCT01248520., Level of Evidence: : I.

Over an 11-month period on a Children's Psychiatric Unit 5% of 60 first admissions for hospitalization were apparently associated with adverse effects of psychotropic medication. Forty (66%) of the first admissions had used, prior to hospitalization, a wide variety of drugs including stimulants, major and minor tranquilizers, anticonvulsants, antidepressants and over-the-counter drugs containing antihistamines and analgesics. It is suggested that inappropriate and injudicious use of psychotropic medications may be associated with unanticipated adverse behavioral effects, which can result in deterioration of a child's functioning to the point of necessitating psychiatric hospitalization. Early identification of these unwanted psychotropic effects has diagnostic, prognostic, economic, and legal implications.