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Patient-powered research networks (PPRNs) are a valuable source of patient-generated information. Diagnosis code-based algorithms developed by PPRNs can be used to query health plans' claims data to identify patients for research opportunities. Our objective was to implement privacy-preserving record linkage processes between PPRN members' and health plan enrollees' data, compare linked and nonlinked members, and measure disease-specific confirmation rates for specific health conditions.

The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network.

Our study team adapted the MyPEEPS (Male Youth Pursuing Empowerment, Education, and Prevention around Sexuality) curriculum, an evidence-based human immunodeficiency virus (HIV) prevention intervention, from a face-to-face, group-based intervention to an individual-level mobile responsive web-based intervention to improve HIV risk behaviors in very young men, aged 13-18 years.

To investigate the impact of health information technology (IT) systems on clinicians' work practices and patient engagement in the management and follow-up of test results.

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated [...]

We sought to present a model of privacy disposition and its development based on qualitative research on privacy considerations in the context of emerging health technologies.

In the context of patient broad consent for future research uses of their identifiable health record data, we compare the effectiveness of interactive trust-enhanced e-consent, interactive-only e-consent, and standard e-consent (no interactivity, no trust enhancement).

Adoption of health information technology (HIT) is often assessed in surveys of organizations. The validity of data from such surveys for ambulatory clinics has not been evaluated. We compared level of agreement between 1 ambulatory statewide survey and 2 other data sources: a second survey and interviews with survey respondents.

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.