Ryan Brewster, MD
@RyanBrewsterMD
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Pediatrician via @BostonChildrens @The_BMC @HarvardMed • MD @StanfordMed • Building, designing and evaluating with @Harvard HealthTech and @AriadneLabs
Boston, MA
Joined April 2019
Ryan Brewster, MD. I leave @stanfordmed more resolved than ever to put service, humanism, & justice first. And to one day learn the urea cycle. I was privileged to give the student address on behalf of my incredible classmates: https://t.co/wIlaqAiQ3c
#StanfordMedGrad2021
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Childcare has become increasingly unaffordable for medical residents, with a widening disparity between resident salaries and childcare costs. https://t.co/aBDIgAr51M
@ryanbrewstermd
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���Our findings reinforce how medical training systematically disadvantages young MDs personally, professionally + financially. Responding to our previous housing study, @_Eric_Reinhart described the need for MD organizing and advocacy. His words ring truer than ever.
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📈Worse yet, the gap between inflation-adjusted childcare costs and resident salaries has been widening over the past 20 years.
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📈The burden is not evenly distributed. Coastal and metropolitan programs experience the highest levels of childcare unaffordability relative to their salaries. 🟠 = Higher childcare burden 🔵 = Lower childcare burden
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📈Using national data from the @AmerMedicalAssn, we showed that childcare is nearly universally unaffordable for resident physicians. Moreover, subsidized childcare and on-site childcare are sparsely available.
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💭We previously studied the state of housing affordability for resident physicians. Many of you were interested in a follow-up study about childcare. Well, we listened! https://t.co/sGchH5rcHD
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🚨 One in four resident physician will become parents during residency. Unfortunately, so many layers of our training are at odds with family building. In our latest national study in @JAMANetworkOpen, we show a bleak picture for childcare, in particular: https://t.co/c2SIyTFAtT
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Highlights include: 🤝 Targeted recruitment based on the age of a family's home and other risk factors 🏠 Virtual walkthroughs to visualize potential lead hazards 📕 Individualized counseling, community referrals, and kit distribution with supplies for home testing, mitigation
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In our latest publication in Academic Pediatrics, we describe the Take the Lead on Lead clinic, a virtual model (@BostonChildrens, @The_BMC ) to increase access to early outreach, intervention, and resource referral for families at-risk of lead poisoning. https://t.co/sObKtgTLXJ
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Yet, our current practice is reactive - We wait until a child's blood lead level reaches a certain threshold before we act. For the more than half of American children with detectable lead in their bodies, a more proactive, family-centered, and upstream approach is needed.
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"Children's bodies are the sentinels of environmental harm." This was one of the first of many backwards realities I encountered during peds training. For decades, we have known that any lead exposure can be harmful - and that it is entirely preventable https://t.co/sObKtgTLXJ
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Pediatric #AI and machine learning-enabled devices should be validated using representative datasets and should include complete and standard documentation on pediatric testing and authorization. https://t.co/UUxO4Fym7M
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Our study in @JAMAPediatrics finds that few AI/ML-enabled devices have been developed for use in children and many lack critical data on validation in pediatric patients: https://t.co/JnyqKEVdST
@HarvardMIT_CRS @RyanBrewsterMD
jamanetwork.com
This cross-sectional study analyzes the availability of artificial intelligence and machine learning–enabled devices authorized for children by the US Food and Drug Administration (FDA) and assesses...
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Many thanks to @statnews and @KatieMPalmer for covering our @JAMAPediatrics study on AI/ML-enabled devices for pediatrics!
A new study resurfaces long-simmering concerns about the lack of transparency in data used to train and validate AI and ML-enabled devices.
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A new study resurfaces long-simmering concerns about the lack of transparency in data used to train and validate AI and ML-enabled devices.
statnews.com
A new study resurfaces long-simmering concerns about the lack of transparency in data used to train and validate AI and ML-enabled devices.
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Key takeaways: 🖊️ There must be complete and transparent documentation on pediatric testing and validation for any AI-enabled device indicated for children 💻 The clinical, technology, and regulatory communities must commit to representative datasets in algorithm development
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🤔 The AI innovation gap also reflects longstanding market, scientific, and ethical barriers to developing medical technologies for children. @JuanEspinozaMD
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