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Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: To qualify the use of patient clinical records as non-human-subject for research purpose, electronic medical record data must be de-identified so there is minimum risk to protected health information exposure. This study demonstrated a robust framework for structured data de-identification that can be applied to any relational data source that needs to be de-identified., Methods: Using a real world clinical data warehouse, a pilot implementation of limited subject areas were used to demonstrate and evaluate this new de-identification process. Query results and performances are compared between source and target system to validate data accuracy and usability., Results: The combination of hashing, pseudonyms, and session dependent randomizer provides a rigorous de-identification framework to guard against 1) source identifier exposure; 2) internal data analyst manually linking to source identifiers; and 3) identifier cross-link among different researchers or multiple query sessions by the same researcher. In addition, a query rejection option is provided to refuse queries resulting in less than preset numbers of subjects and total records to prevent users from accidental subject identification due to low volume of data. This framework does not prevent subject re-identification based on prior knowledge and sequence of events. Also, it does not deal with medical free text de-identification, although text de-identification using natural language processing can be included due its modular design., Conclusion: We demonstrated a framework resulting in HIPAA Compliant databases that can be directly queried by researchers. This technique can be augmented to facilitate inter-institutional research data sharing through existing middleware such as caGrid.

Objective: To determine the association between body mass index (BMI) and hospital mortality for critically ill adults., Design: Retrospective cohort study., Setting: One-hundred six intensive care units (ICUs) in 84 hospitals., Patients: Mechanically ventilated adults (n=1,488) with acute lung injury (ALI) included in the Project IMPACT database between December 1995 and September 2001., Interventions: None., Measurements and Main Results: Over half of the cohort had a BMI above the normal range. Unadjusted analyses showed that BMI was higher among subjects who survived to hospital discharge vs. those who did not (p<.0001). ICU and hospital mortality rates were lower in higher BMI categories. After risk-adjustment, BMI was independently associated with hospital mortality (p<.0001) when modeled as a continuous variable. The adjusted odds were highest at the lowest BMIs and then declined to a minimum between 35 and 40 kg/m2. Odds increased after the nadir but remained below those seen at low BMIs. With use of a categorical designation, BMI was also independently associated with hospital mortality (p=.0055). The adjusted odds were highest for the underweight BMI group (adjusted odds ratio [OR], 1.94; 95% confidence interval [CI], 1.05-3.60) relative to the normal BMI group. As in the analysis using the continuous BMI variable, the odds of hospital mortality were decreased for the groups with higher BMIs (overweight adjusted OR, 0.72; 95% CI, 0.51-1.02; obese adjusted OR, 0.67; 95% CI, 0.46-0.97; severely obese adjusted OR, 0.78; 95% CI, 0.44-1.38). Differences in the use of heparin prophylaxis mediated some of the protective effect of severe obesity., Conclusions: BMI was associated with risk-adjusted hospital mortality among mechanically ventilated adults with ALI. Lower BMIs were associated with higher odds of death, whereas overweight and obese BMIs were associated with lower odds.