• Mashup Score: 2

    Background De-implementation of low-value care can increase health care sustainability. We evaluated the reporting of direct costs of de-implementation and subsequent change (increase or decrease) in health care costs in randomized trials of de-implementation research. Methods We searched MEDLINE and Scopus databases without any language restrictions up to May 2021. We conducted study screening and data extraction independently and in duplicate. We extracted information related to study characteristics, types and characteristics of interventions, de-implementation costs, and impacts on health care costs. We assessed risk of bias using a modified Cochrane risk-of-bias tool. Results We screened 10,733 articles, with 227 studies meeting the inclusion criteria, of which 50 included information on direct cost of de-implementation or impact of de-implementation on health care costs. Studies were mostly conducted in North America (36%) or Europe (32%) and in the primary care context (70%). Th

    Tweet Tweets with this article
    • .@ImplementSci: De-implementation #RCTs typically did not report direct costs of de-implementation strategies (92%) or impacts on #HealthCare costs (81%). Lack of #cost information may limit the value of de-implementation trials to #decision-makers. Read: https://t.co/XBqvceOmF8

  • Mashup Score: 0

    Background The importance of accurately costing implementation strategies is increasingly recognised within the field of implementation science. However, there is a lack of methodological guidance for costing implementation, particularly within digital health settings. This study reports on a systematic review of costing analyses conducted alongside implementation of hospital-based computerised…

    Tweet Tweets with this article
    • .@ImplementSci: A systematic review of #Implementation costs of #hospital-based computerized #decision support systems shows lack of methodological support to cost implementation strategies, particularly within the context of #digital health. #ImpSci #CDSS https://t.co/pk0BfoRAtN https://t.co/Pp1Aya6HuI

  • Mashup Score: 4

    As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML’s human users or factors that influence user reliance. We argue for a systematic approach to identifying the…

    Tweet Tweets with this article
    • .@Nature #DigitalMedicine: New article cautions that before #AI tools are “released into the wild,” we must better understand their outcomes & impacts in the hands of imperfect human users. Systematic approach needed for tackling the challenge. #decision https://t.co/WVidQFKnM3 https://t.co/l05xruvIDJ