Mental Health Diagnoses, Health Care Utilization, and Placement Stability on Antipsychotic Prescribing Among Foster Care Youth.

OBJECTIVE: This study sought to examine how mental health diagnoses, health care utilization and foster care placement instability affect antipsychotic prescribing and how these factors may contribute to disproportionate antipsychotic prescribing among youth in foster care. METHODS: This retrospective cohort study utilized EHR data that were linked to administrative child welfare data. Two outcome variables were analyzed: 1) any antipsychotic prescription documented and 2) number of antipsychotic prescriptions documented. Predictor variables included foster care status, number of unique mental health diagnoses, counts of health care encounters over the study period, and counts of foster care placements. Covariates included gender, persons of color, and age in years. Models were estimated using logistic regression for the dichotomous outcome and Poisson regression for the count outcome. RESULTS: Increased antipsychotic prescribing among children in foster care persists even after accounting for mental health diagnoses and health care utilization. However, the number of placements modified the effect of foster care involvement on antipsychotic prescribing such that after 2 placement changes, the odds of being prescribed an antipsychotic surpassed the effect of foster care involvement. More mental health diagnoses, more inpatient and emergency health care encounters, and more foster care placements were associated with an increased odds of being prescribed an antipsychotic and an increased count of antipsychotic prescriptions. Decreased primary care encounters were associated with increased odds of antipsychotic prescriptions, and decreased specialty encounters were associated with higher counts of antipsychotic prescriptions. CONCLUSIONS: Placement instability is associated with disproportionate antipsychotic prescribing among youth in foster care.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.

BACKGROUND: Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. OBJECTIVE: This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. METHODS: In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. RESULTS: Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. CONCLUSIONS: Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

Improving Well-Being Among Trainees: A Partnership to Reduce Barriers to Primary Care Services.

BACKGROUND: Improved well-being is a focus for graduate medical education (GME) programs. Residents and fellows often express difficulty with visiting primary care physicians, and this issue has not been thoroughly investigated. OBJECTIVE: We reported implementation and utilization of a primary care concierge scheduling service and a primary care video visit service for GME trainees. METHODS: GME leaders collaborated with Duke Primary Care to offer trainees a concierge scheduling service and opportunity for primary care video visits. This quantitative evaluation included (1) analysis of the institutional GME survey results pre- and post-intervention, and (2) review of use of the concierge scheduling line. RESULTS: Comparison of the 2018 and 2019 internal GME surveys showed a decrease in perceived barriers accessing primary care (58% to 31%, P < .0001), a decrease in perceived delays to access primary care (27% to 21%, P = .023), and an increase in respondents who reported needing health care services in the past year (37% to 62%, P < .0001). Although increased need for health services was reported, there was no difference in the proportion reporting use of health services (63% and 65%, P = .43). Of the 142 concierge line calls reviewed, 127 (87%) callers requested clinic appointments, and 15 (10%) callers requested video appointments. Of callers requesting clinic appointments, 99 (80%) were scheduled. CONCLUSIONS: Providing resources to connect trainees to primary care greatly reduces their perception of barriers to health care and may provide a convenient mechanism to schedule flexible primary care appointments.

An atypical form of infantile meningococcal meningitis.

We describe a rare case of infantile meningococcal (serotype B) meningitis in a 3-month-old Chinese boy with an atypical indolent presentation with prolonged persistent fever despite appropriate antimicrobial therapy likely due to drug fever. The case highlights the need for continued vigilance in identifying similar cases in the future.

Serologic Responses After Hepatitis B Vaccination in Preterm Infants Born to Hepatitis B Surface Antigen-Positive Mothers: Singapore Experience.

BACKGROUND: The Advisory Committee on Immunization Practices (ACIP) recommends a 4-dose vaccination schedule for preterm low birth weight infants (<2 kg) and a 3-dose vaccination schedule for preterm infants (≥2 kg) born to hepatitis B surface antigen (HBsAg)-positive mothers. However, data remain limited for these high-risk infants, and the optimal dosing schedule in Asia is not well established. AIM: The aim of this study was to evaluate the serologic vaccine responses in preterm infants born to HBsAg-positive mothers using current vaccination guidelines. METHODS: Preterm babies of gestation less than 37 completed weeks born to HBsAg-positive mothers were prospectively recruited during 6 years (June 2009 to December 2015) and retrospectively recruited via convenience sampling in 2 years (June 2013 to April 2015) in 2 tertiary pediatric centers. The preterm infants were given 4 or 3 vaccine doses as per ACIP 2005 guidelines. Vaccine response was defined as achieving hepatitis B surface antibody values of >10 IU/L [Abbott Architect (Abbott Laboratories, Chicago, IL)] at 9 months of chronologic age. RESULTS: A total of 24 preterm infants were recruited. Four had a birth weight <2 kg. Of 23 surviving infants, all were negative for HBsAg. One baby (4.5%) did not achieve adequate vaccine response. All 4 infants with birth weight <2 kg achieved seroprotective values. CONCLUSION: The current ACIP-recommended vaccination schedule results in adequate antibody responses in preterm infants of HBsAg-positive mothers.