Abstract：
OBJECTIVE: Clinical benefits result from electronic patient-reported outcome (ePRO) systems that enable remote symptom monitoring. Although clinically useful, real-time alert notifications for severe or worsening symptoms can overburden nurses. Thus, we aimed to algorithmically identify likely non-urgent alerts that could be suppressed.
METHODS: We evaluated alerts from the PRO-TECT trial (Alliance AFT-39) in which oncology practices implemented remote symptom monitoring. Patients completed weekly at-home ePRO symptom surveys, and nurses received real-time alert notifications for severe or worsening symptoms. During parts of the trial, patients and nurses each indicated whether alerts were urgent or could wait until the next visit. We developed an algorithm for suppressing alerts based on patient assessment of urgency and model-based predictions of nurse assessment of urgency.
RESULTS: 593 patients participated (median age = 64 years, 61% female, 80% white, 10% reported never using computers/tablets/smartphones). Patients completed 91% of expected weekly surveys. 34% of surveys generated an alert, and 59% of alerts prompted immediate nurse actions. Patients considered 10% of alerts urgent. Of the remaining cases, nurses considered alerts urgent more often when patients reported any worsening symptom compared to the prior week (33% of alerts with versus 26% without any worsening symptom, p = 0.009). The algorithm identified 38% of alerts as likely non-urgent that could be suppressed with acceptable discrimination (sensitivity = 80%, 95% CI [76%, 84%]; specificity = 52%, 95% CI [49%, 55%]).
CONCLUSIONS: An algorithm can identify remote symptom monitoring alerts likely to be considered non-urgent by nurses, and may assist in fostering nurse acceptance and implementation feasibility of ePRO systems.
METHODS: We evaluated alerts from the PRO-TECT trial (Alliance AFT-39) in which oncology practices implemented remote symptom monitoring. Patients completed weekly at-home ePRO symptom surveys, and nurses received real-time alert notifications for severe or worsening symptoms. During parts of the trial, patients and nurses each indicated whether alerts were urgent or could wait until the next visit. We developed an algorithm for suppressing alerts based on patient assessment of urgency and model-based predictions of nurse assessment of urgency.
RESULTS: 593 patients participated (median age = 64 years, 61% female, 80% white, 10% reported never using computers/tablets/smartphones). Patients completed 91% of expected weekly surveys. 34% of surveys generated an alert, and 59% of alerts prompted immediate nurse actions. Patients considered 10% of alerts urgent. Of the remaining cases, nurses considered alerts urgent more often when patients reported any worsening symptom compared to the prior week (33% of alerts with versus 26% without any worsening symptom, p = 0.009). The algorithm identified 38% of alerts as likely non-urgent that could be suppressed with acceptable discrimination (sensitivity = 80%, 95% CI [76%, 84%]; specificity = 52%, 95% CI [49%, 55%]).
CONCLUSIONS: An algorithm can identify remote symptom monitoring alerts likely to be considered non-urgent by nurses, and may assist in fostering nurse acceptance and implementation feasibility of ePRO systems.
摘要：
目的：能够实现远程症状监测的电子患者报告结果（ePRO）系统可带来临床益处。虽然临床上有用，严重或恶化症状的实时警报通知可能会使护士负担过重。因此，我们旨在通过算法识别可能被抑制的非紧急警报。
方法：我们评估了PRO-TECT试验（AllianceAFT-39）中肿瘤学实践实施远程症状监测的警报。患者每周完成在家ePRO症状调查，护士收到严重或恶化症状的实时警报通知。在部分审判期间，患者和护士各自指出警报是否紧急或可以等到下一次就诊.我们开发了一种基于患者对紧迫性的评估和基于模型的护士对紧迫性的评估预测来抑制警报的算法。
结果：593名患者参加（中位年龄=64岁，61%女性,80%白色,10%报告从未使用过电脑/平板电脑/智能手机)。患者完成预期每周调查的91%。34%的调查产生了警报,59%的警报促使护士立即采取行动。患者认为10％的警报是紧急的。在其余案件中，与前一周相比，当患者报告任何症状恶化时，护士认为警报更加紧急(33%的警报与26%的警报没有任何症状恶化，p=0.009)。该算法将38%的警报识别为可能的非紧急警报，可以通过可接受的辨别来抑制(灵敏度=80%，95%CI[76%，84%]；特异性=52%，95%CI[49%，55%]）。
结论：一种算法可以识别可能被护士认为是非紧急的远程症状监测警报，并可能有助于培养护士对ePRO系统的接受度和实施可行性。
方法：我们评估了PRO-TECT试验（AllianceAFT-39）中肿瘤学实践实施远程症状监测的警报。患者每周完成在家ePRO症状调查，护士收到严重或恶化症状的实时警报通知。在部分审判期间，患者和护士各自指出警报是否紧急或可以等到下一次就诊.我们开发了一种基于患者对紧迫性的评估和基于模型的护士对紧迫性的评估预测来抑制警报的算法。
结果：593名患者参加（中位年龄=64岁，61%女性,80%白色,10%报告从未使用过电脑/平板电脑/智能手机)。患者完成预期每周调查的91%。34%的调查产生了警报,59%的警报促使护士立即采取行动。患者认为10％的警报是紧急的。在其余案件中，与前一周相比，当患者报告任何症状恶化时，护士认为警报更加紧急(33%的警报与26%的警报没有任何症状恶化，p=0.009)。该算法将38%的警报识别为可能的非紧急警报，可以通过可接受的辨别来抑制(灵敏度=80%，95%CI[76%，84%]；特异性=52%，95%CI[49%，55%]）。
结论：一种算法可以识别可能被护士认为是非紧急的远程症状监测警报，并可能有助于培养护士对ePRO系统的接受度和实施可行性。
