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

基本信息

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
项目类别：
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

项目摘要

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）：联邦政府对创建全国性电子健康记录 (EHR) 系统的激励措施，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群体的能力。这个过程，众所周知作为“EHR 表型分析”，将高通量算法应用于电子数据，根据精确的信息群（例如人口统计、诊断、程序、实验室值、生命体征、药物、生活方式和环境因素）对患者进行分类。 EHR 表型分析预计将带来更强大、更低成本的研究，并且是支持一系列临床、观察、结果和比较有效性研究的学习医疗保健系统愿景的关键组成部分。该企业的最终成功取决于建立和维持公众信任，而患者的投入至关重要。我们对患者出于研究目的分享数据的意愿、他们对此类使用的首选控制程度，或者他们对不同知情同意方法的必要性和可接受性的看法知之甚少。此外，电子病历数据远非完美，反映了医疗保健系统固有的噪音和复杂性，因此容易出现不完整、不准确和偏见的情况。使用电子病历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），并发现自己需要联系患者，要么告知他们严重的潜在健康问题，要么以其他方式解决差异。这是研究人员日益面临的新挑战。拟议研究的目的是通过收集美国东南部四个高度多样化县的患者的经验数据来帮助填补这些空白，并利用在这些县进行的两项现有研究来获取关于患者的丰富的、与政策相关的数据。意见和他们的实际行为。为了实现这一目标，我们将：（1）进行半结构化访谈，以评估患者分享其临床数据供研究使用的意愿，包括可接受的知情同意方法；（2）根据EHR表型分析结果，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接触的反应，以解决参与者自我报告的健康信息与参与者之间的差异。他们的电子病历数据； (3) 召开一系列协商民主活动，系统地制定有关同意和与患者接触的建议，以告知临床数据用于研究的道德使用。