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Background Personally controlled health records (PCHRs) are accessible over the Internet and allow individuals to maintain and manage a secure copy of their medical data. These records provide a new opportunity to provide customized health recommendations to individuals based on their record content. Health promotion programs using PCHRs can potentially be used in a variety of settings and target a large range of health issues. Objectives The aim was to assess the value of a PCHR in an employee health promotion program for improving knowledge, beliefs, and behavior around influenza prevention. Methods We evaluated a PCHR-based employee health promotion program using a randomized controlled trial design. Employees at Hewlett Packard work sites who reported reliable Internet access and email use at least once every 2 days were recruited for participation. PCHRs were provided to all participants for survey administration, and tailored, targeted health messages on influenza illness and prevention were delivered to participants in the intervention group. Participants in the control group received messages addressing cardiovascular health and sun protection. The main outcome measure was improvement in knowledge, beliefs, and behavior around influenza prevention. Secondary outcomes were influenza vaccine rates among household members, the impact of cardiovascular health and sun protection messages on the control group, and the usability and utility of the PCHR-based program for employees. Results The intervention did not have a statistically significant effect on the influenza knowledge elements we assessed but did impact certain beliefs surrounding influenza. Participants in the intervention group were more likely to believe that the influenza vaccine was effective (OR = 5.6; 95% CI = 1.7-18.5), that there were actions they could take to prevent the flu (OR = 3.2; 95% CI = 1.1-9.2), and that the influenza vaccine was unlikely to cause a severe reaction (OR = 4.4; 95% CI = 1.3-15.3). Immunization rates did not differ between the intervention and control groups. However, participants in the intervention group were more likely to stay home during an infectious respiratory illness compared with participants in the control group (39% [16/41] vs 14% [5/35], respectively; P = .02). The program also succeeded in improving recognition of the signs of heart attack and stroke among participants in the control group. Overall, 78% of participants rated the PCHR as “extremely/very” easy to use, and 73% responded that they would be “extremely/very” likely to participate again in a PCHR-based health promotion system such as this one. Conclusions With a small sample size, this study identified a modest impact of a PCHR-based employee health program on influenza prevention and control. Employees found the PCHR acceptable and easy to use, suggesting that it should be explored as a common medium for health promotion in the workplace. Trial RegistrationClinicalTrials.gov NCT00142077

Objectives To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models. Materials and Methods SHRINE queries are designed to run on the Informatics for Integrating Biology & the Bedside (i2b2) data model. We created functionality in Leaf to interoperate with a SHRINE data network and dynamically translate SHRINE queries to other data models. We randomly selected 500 past queries from the SHRINE-based national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) network for evaluation, and an additional 100 queries to refine and debug Leaf's translation functionality. We created a script for Leaf to convert the terms in the SHRINE queries into equivalent structured query language (SQL) concepts, which were then executed on 2 other data models. Results and Discussion 91.1% of the generated queries for non-i2b2 models returned counts within 5% (or ±5 patients for counts under 100) of i2b2, with 91.3% recall. Of the 8.9% of queries that exceeded the 5% margin, 77 of 89 (86.5%) were due to errors introduced by the Python script or the extract-transform-load process, which are easily fixed in a production deployment. The remaining errors were due to Leaf's translation function, which was later fixed. Conclusion Our results support that cohort discovery applications such as Leaf and SHRINE can interoperate in federated data networks with heterogeneous data models. [ABSTRACT FROM AUTHOR]