OBJECTIVE: This study explores patient motivations and preferences for sharing medical data with researchers using the iAgree platform. We examine how study characteristics, including data type requested and data-sharing arrangements, influence consent decisions, and assess the role of demographic factors, privacy concerns, and perceived benefits in shaping data-sharing behavior. MATERIALS AND METHODS: We conducted a mixed-methods study with 527 US adults (≥18 years) recruited via advisory boards, social media, clinics, and newsletters. Participants completed 3 of 4 simulated studies on iAgree, each varying by data elements requested and data-sharing scope. Participants provided consent and data-sharing decisions and completed a post-simulation survey capturing demographics, data-sharing motivations, privacy concerns, and patient activation. We used logistic regressions to examine associations between demographics, privacy concerns, and patient activation and: (1) consent status and (2) willingness to share particular data elements. Finally, we applied thematic analysis to open-ended responses. RESULTS: Consent status did not significantly vary by data type or study design. However, participants citing altruism, personal benefit, and patient solidarity were more likely to share data. Higher privacy concerns were linked to lower willingness to share family health and mental health information. Participants with higher patient activation were also less likely to share data. DISCUSSION: Demographic factors were not significantly associated with consent or willingness to share data, countering common assumptions about disparities in sharing preferences. CONCLUSION: Altruism and perceived benefit drive willingness to share health data, while privacy concerns and patient activation may reduce it, emphasizing the need for patient-centered, transparent consent models.

Patient-controlled data-sharing systems are increasingly promoted as a way to empower patients with greater autonomy over their health data. Yet it remains unclear how different stakeholders, especially patients and health system leaders, perceive the benefits and challenges of enabling granular control over the sharing of de-identified medical data for research. To address this gap, we developed a high-fidelity prototype of a patient-controlled, web-based consent platform and conducted a two-phase mixed-methods study: semi-structured interviews with 16 health system leaders and a survey with 523 patient participants. While both groups appreciated the potential of such a platform to enhance transparency and autonomy, their views diverged in meaningful ways. Leaders viewed transparency and granular control through the lens of informed consent and institutional ethics, whereas patients interpreted these factors as safeguards against potential risks and uncertainties. Our findings underscore critical tensions such as individual control and research integrity. We offer design implications for building trustworthy, context-aware systems that support flexible granularity, provide ongoing benefit‑centered transparency, and adapt to diverse literacy and privacy needs.

OBJECTIVES: Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. MATERIALS AND METHODS: We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. RESULT: We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20 680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. DISCUSSION: The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. CONCLUSION: Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.

OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

OBJECTIVE: This qualitative study aimed to understand patient and researcher perspectives regarding consent and data-sharing preferences for research and a patient-centered system to manage consent and data-sharing preferences. MATERIALS AND METHODS: We conducted focus groups with patient and researcher participants recruited from three academic health centers via snowball sampling. Discussions focused on perspectives on the use of electronic health record (EHR) data for research. Themes were identified through consensus coding, starting from an exploratory framework. RESULTS: We held two focus groups with patients (n = 12 patients) and two with researchers (n = 8 researchers). We identified two patient themes (1-2), one theme common to patients and researchers (3), and two researcher themes (4-5). Themes included (1) motivations for sharing EHR data, (2) perspectives on the importance of data-sharing transparency, (3) individual control of personal EHR data sharing, (4) how EHR data benefits research, and (5) challenges researchers face using EHR data. DISCUSSION: Patients expressed a tension between the benefits of their data being used in studies to benefit themselves/others and avoiding risk by limiting data access. Patients resolved this tension by acknowledging they would often share their data but wanted greater transparency on its use. Researchers expressed concern about incorporating bias into datasets if patients opted out. CONCLUSIONS: A research consent and data-sharing platform must consider two competing goals: empowering patients to have more control over their data and maintaining the integrity of secondary data sources. Health systems and researchers should increase trust-building efforts with patients to engender trust in data access and use.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

INTRODUCTION: Existing record linkage methods do not handle missing linking field values in an efficient and effective manner. The objective of this study is to investigate three novel methods for improving the accuracy and efficiency of record linkage when record linkage fields have missing values. METHODS: By extending the Fellegi-Sunter scoring implementations available in the open-source Fine-grained Record Linkage (FRIL) software system we developed three novel methods to solve the missing data problem in record linkage, which we refer to as: Weight Redistribution, Distance Imputation, and Linkage Expansion. Weight Redistribution removes fields with missing data from the set of quasi-identifiers and redistributes the weight from the missing attribute based on relative proportions across the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields rather than imputing the missing data value. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in a linkage field. We tested the linkage methods using simulated data sets with varying field value corruption rates. RESULTS: The methods developed had sensitivity ranging from .895 to .992 and positive predictive values (PPV) ranging from .865 to 1 in data sets with low corruption rates. Increased corruption rates lead to decreased sensitivity for all methods. CONCLUSIONS: These new record linkage algorithms show promise in terms of accuracy and efficiency and may be valuable for combining large data sets at the patient level to support biomedical and clinical research.

Lisa M. Schilling, Jonathan D. Wren, Robert P. Dellavalle; Letter to the Editor, Bioinformatics, Volume 20, Issue 17, 22 November 2004, Pages 2903, https://doi.