Randomistas fight poverty, nurses improve youth outcomes, and decision support struggles.

1. Jason Zweig tells the story of randomistas, who use randomized, controlled trials to pinpoint what helps people become self-sufficient around the globe. The Anti-Poverty Experiment describes several successful, data-driven programs, ranging from financial counseling to grants of livestock.

evidence for nurse home visitation

2. Can an early childhood program prevent child abuse, crime, drug abuse, and neglect? Yes, says the Nurse-Family Partnership, which introduces vulnerable first-time parents to maternal and child-health nurses. NFP (@NFP_nursefamily) refines its methodology with randomized, controlled trial evidence satisfying the Coalition for Evidence-Based Policy’s “Top Tier”, and producing a positive return on investment.

3. Is the “data-driven decision” a fallacy? Yes, says Stefan Conrady, arguing that the good alliteration is a bad motto. He explains on the BayesiaLab blog that the concept doesn’t adequately encompass casual models, necessary for anticipating “the consequences of actions we have not yet taken”. Good point.

4. Do recommendations from decision support technology improve the appropriateness of a physician’s imaging orders? Not necessarily. JAMA provides evidence of the limitations of algorithmic medicine. An observational study shows it’s difficult to attribute improvements to clinical decision support.

Posted by Tracy Allison Altman on 7-Apr-2017.

Photo credit: Damir Spanic on Unsplash

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Museum musings.

Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

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