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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

基本信息

批准号：
10616709
负责人：
Zhijun Yin
金额：
$ 37.91万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
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/10616709
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 Learning Machine Learning Malignant Neoplasms Manuals Medical Methods Modeling Morbidity - disease rate Oncology Oral Patients Pharmaceutical Preparations Probability Process Recurrence Recurrent Malignant Neoplasm Regimen Research Research Personnel Resources Risk Sampling Biases Self Administration Semantics Services Societies Sociology Statistical Models 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 language processing machine learning model malignant breast neoplasm mobile computing mortality patient engagement patient portal patient-clinician communication prevent recruit risk prediction side effect social cognitive theory text searching treatment adherence unsupervised learning

项目摘要

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.

总结癌症是美国发病率的主要原因，有50多万人死亡估计在2019年。全身性癌症治疗越来越多地被设计为长期口服抗癌药物，考虑到自我管理方案的便利性增加。为例如，可手术的乳腺癌受体阳性患者被处方辅助治疗，口服激素治疗，期望他们继续他们的方案至少5 年，以实现利益最大化。虽然许多口服疗法已被证明有效地减轻癌症复发和死亡，停止这些治疗并不罕见。这是由于在完成处方治疗前停药，方案导致生存率较低、复发风险增加和医疗费用较高。为了提高治疗依从性并保证更好的医疗保健服务，医疗保健提供者需要知道癌症患者何时以及为什么会停止药物治疗。虽然有各种各样的研究方案中断，这些研究的重点是研究是关于知识发现或干预。知识发现的重点是描述导致停药的潜在因素，干预的目的是利用发现的知识来设计和测试有效的策略，以帮助患者遵守治疗。因为有成千上万的癌症患者，供应商对他们每个人进行干预。有限的医疗资源需要分配有效地，使得具有较高中断药物治疗风险的患者将接受更大，及时注意。然而，在线通信和移动的计算的日益融合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EMR数据，以更好地了解癌症患者的医疗经历。该项目的中心假设是，这种通信与结构化电子病历一起，可应用于口服抗癌药物停药的学习和预测。具体这个项目旨在测试我们的中心假设的目的是1）发现什么是 2）推断患者门户消息和结构化EMR如何在患者门户中通信; 与停药相关; 3）确定谁更有可能停药药物治疗据我们所知，这是第一个将这些信息应用于患者的研究门户网站和结构化的电子病历，以调查癌症患者的停药情况。