Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.
Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.
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使用计算方法改善哮喘和慢性阻塞性肺疾病的综合疾病管理：二次分析方案。
DOI：
10.2196/27065
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发表时间：
2021-05-18
影响因子：
1.7
通讯作者：
Nkoy FL
中科院分区：
文献类型：
--
作者：
Luo G;Stone BL;Sheng X;He S;Koebnick C;Nkoy FL
哮喘和慢性阻塞性肺疾病（COPD）对卫生保健造成沉重负担。大约四分之一的哮喘患者和慢性阻塞性肺病患者容易出现病情恶化，通过综合疾病管理提供的预防性护理可大大减少病情恶化，但服务能力有限。要做到这一点，需要一个预测恶化倾向的模型，但目前还没有这样的模型。使用目前的哮喘和慢性阻塞性肺病模型构建方法构建此类模型将是次优的，由于很少考虑显示早期健康变化和总体方向的时间特征，因此存在两个空白。首先，其他哮喘和COPD结局的现有模型很少使用更高级的时间特征，如沙丁胺醇补充的天数斜率，并且是不准确的。其次，现有的模型很少显示患者被认为是高风险的原因和潜在的降低风险的干预措施，使得已经忙碌的临床医生花费更多的时间在图表审查上，而忽略了合适的干预措施。常规的自动解释方法不能很好地处理时态数据，不能很好地解决这一问题。为了使更多的哮喘和COPD患者能够获得合适和及时的护理，避免病情加重，我们的目标是实现可理解的计算方法，以准确预测病情加重的倾向，并推荐定制化的干预措施。我们将使用时间特征来准确预测病情恶化的倾向，自动为每个高危患者找到可修改的时间风险因素，并评估可操作的警告对临床医生决定使用综合疾病管理来预防病情恶化倾向的影响。我们从3个著名的美国卫生保健系统获得了大多数哮喘患者的临床和管理数据。我们正在检索研究所需的其他临床和管理数据，主要是COPD患者的数据。我们计划在6年内完成这项研究。我们的研究结果将有助于使哮喘和慢性阻塞性肺病的治疗更加主动、有效和高效，改善结果并节省资源。prr1 - 10.2196/27065
Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. PRR1-10.2196/27065
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地球是平的（p > 0.05）：重要性阈值和不可复制研究的危机。
影响因子：
2.7
作者：
Amrhein V;Korner-Nievergelt F;Roth T
通讯作者：
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影响因子：
5.6
作者：
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DOI：
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发表时间：
2015-01-01
影响因子：
2.8
作者：
Abascal-Bolado, Beatriz;Novotny, Paul J.;Benzo, Roberto P.
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影响因子：
8
作者：
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通讯作者：
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DOI：
10.2147/copd.s155773
发表时间：
2018-01-01
影响因子：
2.8
作者：
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通讯作者：
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