Preventing medication dispensing errors in pharmacy practice with interpretable machine intelligence

项目总结 医疗差错是美国第三大致死原因，仅次于癌症和心血管疾病 疾病。医疗差错中涉及药物的比例最大。用药差错导致300万人死亡 门诊就诊，急诊科就诊100万人次，住院12.5万人次 每年。令人震惊的是，仅在美国，每年就有超过40亿张处方被分发。 尽管配药错误率通常很低，为0.06%，但配药的绝对数量 相当于每年有240万次不正确的药物分配。在药房，配药错误会出现 当药剂师没有检测到装在处方小瓶中的药物与 在处方标签上开的药。这些配药错误可能会导致患者受伤，增加了压力 医疗保健系统，以及针对药房的代价高昂的法律行动。 机器智能(MI)可以用来协助验证过程，以帮助避免危险和 昂贵的药房配药错误。4-6然而，为了使人-MI伙伴关系发挥最佳作用，MI 应该能够传达准确的信息，鼓励提供者使声音具有认知性 维持最佳信任，减少时间和认知需求的决策。必须…… 这个目标是设计可以从中提取可解释信息的MI，以一种 有效的方式和校准用户对MI的信任，因为过度信任或信任不足都会导致近乎错过和 事件错误。 这个拟议的项目将进一步加深我们设计可解释的MI输出的知识，并向 开发MI模型，鼓励药房员工做出合理的临床决策，从而获得更好的 在以更低的护理成本改善工作生活的同时，改善患者的结果。本研究开发了可解释的MI 方法在药物图像分类的背景下，设计有效的MI建议和推理 导致认知需求降低，对MI的信任度增加。我们的假设是，可解释的MI将导致 与无法理解的M相比，提高了工作绩效和更精确的信任。 建议是：1)设计可解释的机器智能，以在 实时；2)评估药房员工因长期使用可解释机器而产生的信任变化 智能；以及3)确定可解释的机器智能对药房长期工作人员的影响 性能。

PROJECT SUMMARY Medical errors are the 3rd leading cause of death in the United States behind cancer and cardiovascular disease. The largest proportion of medical errors involve medications. Medication errors result in 3 million outpatient medical appointments, 1 million emergency department visits, and 125,000 hospital admissions each year. Astoundingly, over 4 billion prescriptions are dispensed every year in the United States alone. Although dispensing error rates are generally low at 0.06%, the sheer volume of dispensed medications translates to 2.4 million incorrectly dispensed medications each year. In the pharmacy, dispensing errors arise when pharmacists do not detect that the medication filled inside a prescription vial is different from the medication ordered on the prescription's label. These dispensing errors can result in patient harm, added strain on the healthcare system, and costly legal action against the pharmacy. Machine intelligence (MI) can be employed to assist in the verification process to help avoid dangerous and costly pharmacy dispensing errors.4–6 However for the human-MI partnership to function optimally, the MI should be capable of conveying accurate information that encourages providers to make sound cognitive decisions such that optimal trust is maintained, and temporal and cognitive demand is reduced. Imperative to this goal is to design MI from which interpretable information can be extracted, convey this information in an effective manner and calibrate user's trust in MI as either over-trust or under-trust can lead to near miss and incident errors. This proposed project will further our knowledge for designing interpretable MI outputs and inform the development of MI models that encourage pharmacy staff to make sound clinical decisions that lead to better patient outcomes while improving work-life at lower costs of care. This study develops interpretable MI methods in the context of medication images classification and designs effective MI advice and reasoning that lead to lower cognitive demand and increased trust in the MI. Our hypothesis is that interpretable MI will lead to improved work performance and more calibrated trust compared to uninterpretable M. The objectives of this proposal are to: 1) design interpretable machine intelligence to double-check dispensed medication images in real-time; 2) evaluate changes in pharmacy staff trust due to the long-term use of interpretable machine intelligence; and 3) determine the effect of interpretable machine intelligence on long-term pharmacy staff work performance.