Preventing medication dispensing errors in pharmacy practice with interpretable machine intelligence

项目摘要 医疗差错是美国仅次于癌症和心血管疾病的第三大死亡原因。 疾病最大比例的医疗差错涉及药物。用药错误导致300万人死亡 门诊医疗预约、100万次急诊就诊和12.5万次住院 每年.令人惊讶的是，仅在美国，每年就有超过40亿张处方。 尽管分配错误率通常较低，为0.06%，但分配的药物的绝对量 相当于每年有240万次错误配药。在药房，配药错误 当药剂师没有检测到填充在处方小瓶内的药物不同于 处方标签上的药物。这些分配错误可能导致患者伤害，增加压力 对医疗保健系统的影响，以及对药店采取昂贵的法律的行动。 机器智能（MI）可用于协助验证过程，以帮助避免危险和 昂贵的药房配药错误。4 -6然而，为了使人类-MI伙伴关系发挥最佳作用，MI 应该能够传达准确的信息，鼓励提供者做出合理的认知 决策，以保持最佳信任，并减少时间和认知需求。势在必行 这一目标是设计MI，从中可以提取可解释的信息，以 有效方式和校准用户对MI的信任，因为过度信任或信任不足都可能导致险些错过， 事故错误。 这个拟议的项目将进一步提高我们的知识，设计可解释的MI输出，并告知 开发MI模型，鼓励药房工作人员做出合理的临床决策， 患者的治疗结果，同时以更低的护理成本改善工作生活。本研究开发了可解释的MI 方法的背景下，药物图像分类和设计有效的MI建议和推理， 导致认知需求降低和对MI的信任增加。我们的假设是，可解释的MI将导致 与无法解释的M相比，工作绩效得到改善，信任度得到提高。这一目标 建议是：1）设计可解释的机器智能，以仔细检查分配的药物图像， 实时; 2）评估由于长期使用可解释机器而导致的药房工作人员信任的变化 智能; 3）确定可解释的机器智能对长期药房工作人员工作的影响 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.