Because demographic shifts will affect their labor forces in the immediate future, rich societies will have to face up to the challenge of integrating the children of low-status immigrants, such as Mexicans in the United States and Turks in western Europe. The performance of educational systems is critical to meeting this challenge. We consider how three features of such systems—the division of labor among schools, families, and communities; tracking; and inequalities among schools—impact immigrant-origin children. In general, children from low-status immigrant families lag behind the children from native families but for reasons that differ from one system to another. Each system can profit from the experiences of the others in attempting to ameliorate this disparity.

Background Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data. Results A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes. Conclusions This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.

PURPOSE: Electronic patient-reported outcomes (ePROs) can help clinicians proactively assess and manage their patients’ symptoms. Despite known benefits, there is limited adoption of ePROs into routine clinical care as a result of workflow and technologic challenges. This study identifies oncologists’ perspectives on factors that affect integration of ePROs into clinical workflows. METHODS: We conducted semistructured qualitative interviews with 16 oncologists from a large academic medical center, across diverse subspecialties and cancer types. Oncologists were asked how they currently use or could imagine using ePROs before, during, and after a patient visit. We used an inductive approach to thematically analyze these qualitative data. RESULTS: Results were categorized into the following three main themes: (1) selection and development of ePRO tool, (2) contextual drivers of adoption, and (3) patient-facing concerns. Respondents preferred diagnosis-based ePRO tools over more general symptom screeners. Although they noted information overload as a potential barrier, respondents described strong data visualization and ease of use as facilitators. Contextual drivers of oncologist adoption include identifying target early adopters, incentivizing uptake through use of ePRO data to support billing and documentation, and emphasizing benefits for patient care and efficiency. Respondents also indicated the need to focus on patient-facing issues, such as patient response rate, timing of survey distribution, and validity and reliability of responses. DISCUSSION: Respondents identified several barriers and facilitators to successful uptake of ePROs. Understanding oncologists’ perspectives is essential to inform both practice-level implementation strategies and policy-level decisions to include ePROs in alternative payment models for cancer care.

Care plans can reduce care fragmentation for children with medical complexity (CMC); however, implementation is challenging. Mobile health innovations could improve implementation. This mixed methods study’s objectives were to (1) evaluate feasibility of mobile complex care plans (MCCPs) for CMC enrolled in a complex care program and (2) study MCCPs’ impact on parent engagement, parent experience, and care coordination. MCCPs were individualized, updated quarterly, integrated within the electronic health record, and visible on parents’ mobile devices via an online portal. In 1 year (September 1, 2016, to August 31, 2017), 94% of eligible patients (n = 47) received 162 MCCPs. Seventy-four percent of parents (n = 35) reviewed MCCPs online. Forty-six percent of these parents (n = 16) sent a follow-up message, and the care team responded within 8 hours (median time = 7.2 hours). In interviews, parents identified MCCPs as an important reference and communication tool. MCCPs for CMC in a complex care program were feasible, facilitated parental engagement, and delivered timely communication.