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Purpose Patient-reported outcomes and experiences (PRO) data are an integral component of health care quality measurement and PROs are now being collected by many healthcare systems. However, hospital organizational capacity-building for the collection and sharing of PROs is a complex process. We sought to identify the factors that facilitated capacity-building for PRO data collection in a nascent quality improvement learning collaborative of 16 hospitals that has the goal of improving the childbirth experience. Description We used standard qualitative case study methodologies based on a conceptual framework that hypothesizes that adequate organizational incentives and capacities allow successful achievement of project milestones in a collaborative setting. The 4 project milestones considered in this study were: (1) Agreements; (2) System Design; (3) System Development and Operations; and (4) Implementation. To evaluate the success of reaching each milestone, critical incidents were logged and tracked to determine the capacities and incentives needed to resolve them. Assessment The pace of the implementation of PRO data collection through the 4 milestones was uneven across hospitals and largely dependent on limited hospital capacities in the following 8 dimensions: (1) Incentives; (2) Leadership; (3) Policies; (4) Operating systems; (5) Information technology; (6) Legal aspects; (7) Cross-hospital collaboration; and (8) Patient engagement. From this case study, a trajectory for capacity-building in each dimension is discussed. Conclusion The implementation of PRO data collection in a quality improvement learning collaborative was dependent on multiple organizational capacities for the achievement of project milestones., Author(s): Samia Saeb [sup.1] , Lisa M. Korst [sup.2] , Moshe Fridman [sup.2] , Jeanette McCulloch [sup.3] , Naomi Greene [sup.1] , Kimberly D. Gregory [sup.1] Author Affiliations: (1) grid.50956.3f, [...]

Description: The Women's Preventive Services Initiative (WPSI), a national coalition of women's health professional organizations and patient advocacy representatives, developed a recommendation for counseling midlife women aged 40 to 60 years with normal or overweight body mass index (BMI; 18.5 to 29.9 kg/m2) to maintain weight or limit weight gain to prevent obesity with the long-term goals of optimizing health, function, and well-being. This recommendation is intended to guide clinical practice and coverage of clinical preventive health services for the Health Resources and Services Administration and other stakeholders. Clinicians providing preventive health care to women in primary care settings are the target audience for this recommendation.Methods: The WPSI developed this recommendation after evaluating results of a systematic review of the effectiveness and harms of interventions to prevent weight gain and obesity in women aged 40 to 60 years without obesity. Seven randomized clinical trials including 51 638 participants and using various counseling and behavioral interventions were included. Trials indicated favorable weight changes with interventions that were statistically significantly different from control groups in 4 of 5 trials of counseling, but not in 2 trials of exercise. Few harms were reported.Recommendation: The WPSI recommends counseling midlife women aged 40 to 60 years with normal or overweight BMI (18.5 to 29.9 kg/m2) to maintain weight or limit weight gain to prevent obesity. Counseling may include individualized discussion of healthy eating and physical activity. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record., Study Design: A retrospective cohort study of deliveries at an academic, tertiary care hospital was conducted from 2013 to 2019 who had at least two cervical examinations. The population was divided into those delivered by physicians with nulliparous term singleton vertex (NTSV) cesarean delivery rates <23.9% (Partometer cohort) and the remainder (control cohort). The cesarean rate among this population of lower risk patients is a standard metric by which to compare provider rates; <23.9% was the Healthy People 2020 goal. A supervised automated ML approach was applied to generate a model for each population. The primary outcome was accuracy of the model developed on the Partometer cohort at 4 hours from admission to labor and delivery. Secondary outcomes included discrimination ability (receiver operating characteristics-area under the curve [ROC-AUC]), precision-recall AUC, and calibration of the Partometer. To assess generalizability, we compared the performance and clinical predictors identified by the Partometer to the control model., Results: There were 37,932 deliveries during the study period; after exclusions, 9,385 deliveries were included in the Partometer cohort and 19,683 in the control cohort. Accuracy of predicting vaginal delivery at 4 hours was 87.1% for the Partometer (ROC-AUC: 0.82). Clinical predictors of greatest importance in the stacked Intrapartum Partometer Model included the Admission Model prediction and ongoing measures of dilatation and station which mirrored those found in the control population., Conclusion: Using automated ML and intrapartum factors improved the accuracy of prediction of probability of a vaginal delivery over both previously published models based on logistic regression. Harnessing real-time data and ML could represent the bridge to generating a truly prescriptive tool to augment clinical decision-making, predict labor outcomes, and reduce maternal and neonatal morbidity., Key Points: · Our ML-based model yielded accurate predictions of mode of delivery early in labor.. · Predictors for models created on populations with high and low cesarean rates were the same.. · A ML-based model may provide meaningful guidance to clinicians managing labor.., Competing Interests: None declared., (Thieme. All rights reserved.)