Ethical Approaches to Research Use of Clinical Records and Data

临床记录和数据研究使用的道德方法

• 批准号: 9118793
• 负责人: LAURA M. BESKOW
• 金额: $ 4.99万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-25 至 2017-06-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9118793
• 关键词: Adoption; Algorithms; Area; Attitude; Behavior; Clinical; Clinical Data; Code; Communities; Complement; Consent; County; Data; Development; Diagnosis; Disease; Electronic Health Record; Enrollment; Environmental Risk Factor; Ethics; Event; Focus Groups; Genomics; Goals; Health; Healthcare Systems; Incentives; Information Technology; Informed Consent; Interview; Laboratory Procedures; Learning; Link; Manuals; Mining; Noise; Outcome; Participant; Patient Self-Report; Patients; Persons; Pharmaceutical Preparations; Phenotype; Policies; Positioning Attribute; Privacy; Process; Protocols documentation; Public Health; Reaction; Recommendation; Records; Research; Research Personnel; Series; Site; Source; Structure; System; Techniques; Telephone; Vision; Writing; base; cohort; comparative effectiveness; cost; deliberative democracy; demographics; effectiveness research; electronic data; lifestyle factors; meetings; novel; public trust; success; tool; willingness

DESCRIPTION (provided by applicant): Federal incentives to create a nationwide system of electronic health records (EHRs), together with advances in information technology, are rapidly leading to the ability to identify cohorts of patients with precise attributes. This process, known as "EHR phenotyping", applies high-throughput algorithms to electronic data to classify patients based on exact constellations of information (e.g., demographics, diagnoses, procedures, laboratory values, vital signs, medications, lifestyle and environmental factors). EHR phenotyping is expected to result in studies with greater power and lower costs, and is a key component of the vision for learning healthcare systems that support an array of clinical, observational, outcomes, and comparative effectiveness research. The ultimate success of this enterprise depends on building and maintaining public trust, and patient input is vital. Little is known about patients' willingness to share their data for research purposes, their preferred level of control over such use, or their perspectives on the need for and acceptability of different approaches to informed consent. In addition, EHR data are far from perfect, reflecting the noise and complexity inherent in the healthcare system and thus subject to incompleteness, inaccuracies, and bias. Researchers using EHRs will almost certainly uncover discrepancies (e.g., between diagnosis codes and lab values), and find themselves in the position of needing to contact patients-either to inform them of a serious potential health concern, or to otherwise resolve the discrepancy. This is a novel challenge that researchers will increasingly confront. The objective of the proposed research is to help fill these gaps by gathering empirical data from patients in four highly diverse counties in the southeastern US, capitalizing on two existing studies taking place in these counties to obtain rich, policy-relevant data both on patients' opinions and their actual behavior. To attain this objective, we will: (1) Conduct semi-structured interviews to assess patients' willingness to share their clinical data for research use, including acceptable approaches to informed consent; (2) Investigate patients' reactions to researcher contact based on the results of EHR phenotyping, through focus group research as well as analyses of audio recordings of actual telephone calls made by researchers to participants to resolve discrepancies between participants' self-reported health information and their EHR data; and (3) Convene a series of deliberative democracy events to systematically develop recommendations concerning consent and contact with patients to inform the ethical use of clinical data for research.

描述（由申请人提供）：联邦鼓励创建全国性电子健康记录（EHR）系统，加上信息技术的进步，正在迅速提高识别具有精确属性的患者队列的能力。这个过程，众所周知 称为“EHR表型分析”，将高通量算法应用于电子数据以基于信息的精确星座对患者进行分类（例如，人口统计学、诊断、程序、实验室值、生命体征、药物、生活方式和环境因素）。EHR表型分析预计将导致研究具有更大的功率和更低的成本，并且是学习医疗保健系统的愿景的关键组成部分，支持一系列临床，观察，结果和比较有效性研究。这项事业的最终成功取决于建立和维持公众的信任，耐心的投入至关重要。关于患者是否愿意为研究目的分享其数据，他们对这种使用的首选控制水平，或者他们对知情同意的不同方法的必要性和可接受性的看法，我们知之甚少。此外，EHR数据远非完美，反映了医疗保健系统固有的噪音和复杂性，因此存在不完整性，不准确性和偏见。使用EHR的研究人员几乎肯定会发现差异（例如，诊断代码和实验室值之间），并且发现他们自己处于需要联系患者的位置-或者通知他们严重的潜在健康问题，或者以其他方式解决差异。这是研究人员将越来越多地面临的新挑战。拟议研究的目的是通过收集美国东南部四个高度多样化的县的患者的经验数据来帮助填补这些空白，利用这些县现有的两项研究来获得关于患者意见和实际行为的丰富的政策相关数据。为达致这个目的，我们会：（1）进行半结构式访问，以评估病人是否愿意分享其临床数据作研究用途，包括 （2）基于EHR表型分析的结果，通过焦点小组研究以及研究人员与参与者实际通话的录音分析，调查患者对研究人员联系的反应，以解决参与者自我报告的健康信息与其EHR数据之间的差异;（3）召开一系列协商民主活动，系统地制定关于同意和与患者接触的建议，以告知临床数据在研究中的伦理使用。