Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
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DOI：
10.1038/s41746-019-0205-y
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发表时间：
2020-01-20
影响因子：
15.2
通讯作者：
Venkatesh, S.
中科院分区：
文献类型：
--
作者：
Allender, S.;Hayward, J.;Venkatesh, S.
复杂的卫生问题需要多战略、多目标的干预措施。我们提出了一种方法，该方法使用机器学习技术从一组可能的干预措施中选择最佳干预措施，旨在增加全科医生（GP）与患者对体育活动（PA）的讨论。干预措施是根据澳大利亚吉朗13家诊所26名全科医生的因果循环图制定的。全科医生从80多种潜在干预措施中优先选择了8种，以增加全科医生与患者对PA的讨论。在2周基线后，采用多臂强盗算法为全科医生诊所分配最佳策略，目标结果为全科医生PA讨论率。算法每周更新一次，迭代过程直到更有希望的策略出现（持续7周）。前三名执行策略持续3周，以提高每个策略与基线相比的有效性假设检验的能力。全科医生共记录了11176次关于PA的谈话。全科医生确定了15个影响全科医生助理与患者讨论率的因素，包括全科医生的技能和意识、护理的碎片化和对不良后果的恐惧。两种最有效的策略在基于算法的策略分配的七周内被正确识别。这些是诊所接待人员在登记时向患者提供PA信息，以及在候诊室完成PA筛选问卷。本研究展示了一种从多个可能的解决方案中测试和识别最佳策略的有效方法。
Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.