Prediction of Anti-Cancer Medication Discontinuation via Patient Portal Messages and Structured Electronic Medical Records

通过患者门户消息和结构化电子病历预测抗癌药物停药

• 批准号: 10398881
• 负责人: Zhijun Yin
• 金额: $ 38.69万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398881
• 关键词: Academic Medical Centers; Adjuvant; Area; Attention; Back; Biometry; Cancer Patient; Cessation of life; Clinical; Code; Communication; Computer software; Computerized Medical Record; Consumption; Data; Diagnosis; Future; Health; Health Care Costs; Health Care Research; Health Personnel; Healthcare; Human Characteristics; Intervention; Interview; Investigation; Knowledge; Knowledge Discovery; Label; Lead; Learning; Machine Learning; Malignant Neoplasms; Manuals; Medical; Methods; Modeling; Morbidity - disease rate; Oncology; Oral; Patients; Pharmaceutical Preparations; Probability; Process; Recurrence; Regimen; Research; Research Personnel; Resources; Risk; Sampling Biases; Self Administration; Semantics; Services; Societies; Sociology; Statistical Models; Supervision; Surveys; Survival Rate; Technology; Test Result; Testing; Text Messaging; Time; Treatment Protocols; United States; Work; anti-cancer; cancer recurrence; cancer therapy; compliance behavior; cost; design; electronic structure; expectation; experience; health care delivery; health care service utilization; high risk; hormone receptor-positive; hormone therapy; human subject; improved; machine learning model; malignant breast neoplasm; mobile computing; mortality; patient engagement; patient portal; patient-clinician communication; prevent; recruit; side effect; social cognitive theory; text searching; treatment adherence; unsupervised learning

Summary Cancer is a leading cause of morbidity in the United States, with more than half a million deaths estimated in 2019. Systemic cancer therapies are increasingly being designed as long-term oral anti-cancer medications, given the increased convenience of a self-administered regimen. For instance, patients with operable hormone-receptor-positive breast cancer are prescribed adjuvant oral hormonal therapy, with an expectation that they continue their regimen for a minimum of 5 years to maximize the benefits. Although many oral therapies have proven effective in mitigating cancer recurrence and mortality, discontinuation to these treatments are not uncommon. This is a concern because medication discontinuation before the completion of a prescribed treatment protocol leads to lower survival rates, increased risks of recurrence, and higher healthcare costs. To improve treatment adherence and promise better healthcare delivery, it is essential for healthcare providers to know when and why a cancer patient will discontinue their medications. While there have been various investigations into regimen discontinuation, the focus of these studies is either on knowledge discovery or intervention. While knowledge discovery focuses on characterizing the potential factors that lead to medication discontinuation, intervention aims to leverage discovered knowledge to design and test effective strategies to help patients adhere to treatments. Because there are thousands of cancer patients, it is impossible for healthcare providers to apply intervention to each of them. Limited medical resources need to be allocated efficiently, such that patients with a higher risk of discontinuing medications will receive greater, timely attention. Yet, the increasing integration of online communication and mobile computing technologies into the healthcare domain are generating massive quantities of patient-generated information. Thus, we propose to apply online patient-provider communications in a patient portal to supplement traditional EMR data to better understand a cancer patient’s medical experience. The central hypothesis of this project is that such communications together with structured EMRs can be applied to learn and forecast oral anti-cancer medication discontinuation. The specific aims of this project designed to test our central hypothesis are to 1) discover what has been communicated in a patient portal; 2) infer how patient portal messages and structured EMRs are associated with medication discontinuation; and 3) determine who are more likely to discontinue medications. To the best of our knowledge, this is the first study to apply the messages in a patient portal and structured EMRs to investigate medication discontinuation for cancer patients.

摘要 癌症是美国发病率的主要原因，有超过50万人死于癌症 预计在2019年。系统性癌症治疗越来越多地被设计为长期口服 抗癌药物，考虑到自我给药方案的便利性增加。为 例如，激素受体阳性的可手术乳腺癌患者被开出辅助剂 口服激素治疗，预计他们将继续他们的养生法至少5 几年来实现利益最大化。尽管许多口服疗法已被证明在缓解 癌症复发和死亡，停止这些治疗并不少见。这是 一个令人担忧的问题，因为药物在处方药治疗结束之前就停止了 方案导致更低的存活率，更高的复发风险和更高的医疗成本。 为了提高治疗依从性并承诺更好地提供医疗服务，至关重要的是 医疗保健提供者知道癌症患者何时以及为什么会停止服药。 虽然已经对停用方案进行了各种调查，但这些调查的重点 研究要么是关于知识发现，要么是关于干预。当知识发现专注于 为了描述导致停药的潜在因素，干预措施旨在 利用已发现的知识设计和测试有效的策略，帮助患者坚持 治疗。因为有成千上万的癌症患者，医疗保健是不可能的 提供者对其中的每一个都进行干预。需要分配有限的医疗资源 有效，这样，停药风险较高的患者将获得更大的， 及时关注。然而，在线通信和移动计算的日益融合 进入医疗保健领域的技术正在产生大量的患者生成的 信息。因此，我们建议在患者门户中应用在线患者-提供者通信 补充传统的电子病历数据，以更好地了解癌症患者的医疗经验。 该项目的中心假设是，这种通信与结构化的EMR一起 可应用于学习和预测口服抗癌药物停药。具体的 这个项目旨在检验我们的中心假说，目的是1)发现 在患者门户中进行通信；2)推断患者门户消息和结构化EMR是如何 与停药有关；以及3)确定谁更有可能停药 药物。据我们所知，这是第一项将这些信息应用于患者的研究 门户和结构化急诊室以调查癌症患者的停药情况。