Being transparent about the use of data collected during clinical care is important to establish trust relationships between patients and researchers. We propose to develop a system to elicit patient preferences for clinical data sharing that takes into account what data are going to be shared and who is going to be the recipient of shared data. Lessons learned from a pilot study indicate that providing such options in a real clinical setting does not result in massive patient withdrawal in data sharing. The proposed project will generate practical tools and knowledge to guide the development and implementation of informed consent management systems. We plan to conduct a large-scale study in which we will: (1) Determine the best way to present data sharing preferences to patients. Specifically, we will compare preferences elicited via a simple interface (where data categories, such as laboratory tests, and data recipients, such as researchers working in non-profit institutions, will be available) or a complex interface (where items within each data category and within each category of recipients will be available, such as genetic tests and researchers working in the pharmaceutical industry, respectively). These selections will be honored by the research data delivery team through links to our clinical data warehouse for research. (2) Study patient characteristics associated with data sharing preferences for 1,200 randomly sampled patients from 39 diverse general and specialty clinics. Where applicable, we will also compare patient selections for their own data to selections they would make as surrogate decision makers for others. We will conduct surveys where patients can indicate their subjective perception of disease, and we will objectively assess disease severity from EHR data for comparison. This will help us understand whether disease severity plays a role in data sharing preferences. (3) Statistically analyze the degree to which patient preferences affect shared data. This will be important so we can ascertain the quality of data that are shared for research.

Public Health Relevance

We will use an informed consent management system, called iCONCUR (informed CONsent for Clinical data and sample Use for Research), to survey 1200 patients recruited from the outpatient clinics of the UCSD Health System. Our goal is to better understand the factors that influence patients' clinical data sharing preferences. We will also investigate if patients' decision to opt-out of data sharing results in significant distortions i the data available for research.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG008802-03
Application #
9295058
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lockhart, Nicole C
Project Start
2015-09-21
Project End
2019-06-30
Budget Start
2017-07-01
Budget End
2019-06-30
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of California, San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Hindorff, Lucia A; Bonham, Vence L; Ohno-Machado, Lucila (2018) Enhancing diversity to reduce health information disparities and build an evidence base for genomic medicine. Per Med 15:403-412
Ohno-Machado, Lucila; Kim, Jihoon; Gabriel, Rodney A et al. (2018) Genomics and electronic health record systems. Hum Mol Genet 27:R48-R55
Wang, Meng; Ji, Zhanglong; Wang, Shuang et al. (2017) Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies. Bioinformatics 33:3716-3725
Kim, Yejin; El-Kareh, Robert; Sun, Jimeng et al. (2017) Discriminative and Distinct Phenotyping by Constrained Tensor Factorization. Sci Rep 7:1114
Sitapati, Amy; Kim, Hyeoneui; Berkovich, Barbara et al. (2017) Integrated precision medicine: the role of electronic health records in delivering personalized treatment. Wiley Interdiscip Rev Syst Biol Med 9:
Kim, Hyeoneui; Bell, Elizabeth; Kim, Jihoon et al. (2017) iCONCUR: informed consent for clinical data and bio-sample use for research. J Am Med Inform Assoc 24:380-387
Vaidya, Jaideep; Shafiq, Basit; Asani, Muazzam et al. (2017) A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis. AMIA Annu Symp Proc 2017:1695-1704
Wang, Lichang; Fang, Yong; Aref, Dima et al. (2016) PALME: PAtients Like My gEnome. AMIA Jt Summits Transl Sci Proc 2016:219-24
Yang, Lei; Wang, Shuang; Jiang, Xiaoqian et al. (2016) PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe. BMC Med Inform Decis Mak 16 Suppl 3:73