There is increasing concern that current informed consent models linnet the ability of patients to exercise their preferences for data sharing. In th context of healthcare data, this is particularly complicated because informed consent for care may include provisions for allowing data to be shared for research, as long as the use complies with state and federal laws. Some authors have declared that it is impractical to implement a system for tiered informed consent in which patients can indicate their preferences for whom they want to share their data with and for what types of research. The wide dissemination of electronic health record systems and derived clinical data warehouses has created an abundant pool of data that can be used in certain types of research. Clinical data warehouses could be used to store patient preferences and this would create an opportunity to verify whether tiered informed consent is indeed impractical. As patients start getting familiar with patient portals, itis possible to use them to elicit preferences. In this study, we will implement an electronic tiered informed consent system and evaluate it in a single center, single blinded randomized controlled trial. Specifically, we will develop iCONCUR (informed CONsent for Clinical record Use in Research), a software system to assist with patient informed consent of clinical data sharing for research. The system includes: a. A look-up registry for patients to inspect which studies used which parts of their data. b. A tiered informed consent option for patients to selectively opt-out partially or entirely from data sharing. Additionally, we will estimate financial costs of implementing a scalable system. We will also collect data on patient, provider, researcher, and institutional satisfaction. For this study, we will use an academic internal medicine clinic that treats patients a high proportion of ethnic minorities as the setting. The open-source software developed for this study will be made freely available. The results from our study will help elucidate whether the current consent for care forms are adequately capturing patient preferences.

Public Health Relevance

There is increasing concern that current informed consent processes do not allow patients to fully express their preferences for clinical data sharing. We will build and conduct a preliminary evaluation of a system designed to assist patients in selecting their preferences for use of their clinical data in research, such as which data can be shared, with whom, and for what purposes.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
3U54HL108460-03S1
Application #
8476332
Study Section
Special Emphasis Panel (ZRG1-HDM-B (90))
Program Officer
Larkin, Jennie E
Project Start
2010-09-20
Project End
2015-06-30
Budget Start
2012-09-24
Budget End
2015-06-30
Support Year
3
Fiscal Year
2012
Total Cost
$1,663,772
Indirect Cost
$590,371
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
Chen, Luyao; Aziz, Md Momin; Mohammed, Noman et al. (2018) Secure large-scale genome data storage and query. Comput Methods Programs Biomed 165:129-137
Groat, Danielle; Soni, Hiral; Grando, Maria Adela et al. (2018) Self-Reported Compensation Techniques for Carbohydrate, Exercise, and Alcohol Behaviors in Patients With Type 1 Diabetes on Insulin Pump Therapy. J Diabetes Sci Technol 12:412-414
Nguyen, Nghia H; Khera, Rohan; Ohno-Machado, Lucila et al. (2018) Annual Burden and Costs of Hospitalization for High-Need, High-Cost Patients With Chronic Gastrointestinal and Liver Diseases. Clin Gastroenterol Hepatol 16:1284-1292.e30
Weng, Wei-Hung; Wagholikar, Kavishwar B; McCray, Alexa T et al. (2017) Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med Inform Decis Mak 17:155
Groat, Danielle; Grando, Maria A; Thompson, Bithika et al. (2017) A Methodology to Compare Insulin Dosing Recommendations in Real-Life Settings. J Diabetes Sci Technol 11:1174-1182
Doan, Son; Ritchart, Amanda; Perry, Nicholas et al. (2017) How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health Surveill 3:e35
Grando, Maria Adela; Groat, Danielle; Soni, Hiral et al. (2017) Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy. J Diabetes Sci Technol 11:240-246
Burgoyne, Adam M; De Siena, Martina; Alkhuziem, Maha et al. (2017) Duodenal-Jejunal Flexure GI Stromal Tumor Frequently Heralds Somatic NF1 and Notch Pathway Mutations. JCO Precis Oncol 2017:
Chen, Feng; Wang, Shuang; Jiang, Xiaoqian et al. (2017) PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 33:871-878
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

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