Population measurement for health systems.

How can health systems make good use of digital medicine? For healthcare infrastructure, the answer is population measurement, monitoring people to compute status for clustering cohorts. In chronic care, most effective is measuring all the time, to track health status as it gradually changes. Passive monitors run in the background, without additional tasks to activate monitors, especially on mobile phones.

National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors.

At the core of the healthcare crisis is fundamental lack of actionable data. Such data could stratify individuals within populations to predict which persons have which outcomes. If baselines existed for all variations of all conditions, then managing health could be improved by matching the measuring of individuals to their cohort in the population.

Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages.

Patient outcomes to drugs vary, but physicians currently have little data about individual responses. We designed a comprehensive system to organize and integrate patient outcomes utilizing semantic analysis, which groups large collections of personal comments into a series of topics. A prototype implementation was built to extract situational evidences by filtering and digesting user comments provided by patients.

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states.

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model.

Feasibility of mobile phone-based management of chronic illness.

According to the CDC, chronic conditions such as heart disease, cancer, and diabetes cause 75% of healthcare spending in the United States and contribute to nearly seven in ten American deaths. However, despite the prevalence and high-cost of chronic disease, they are also among the most preventable of health problems1. How can we use technology to improve self-care, reduce costs, and lessen the burden on medical professionals? Devices to help manage chronic illness have been marketed for years, but are these specialized devices really necessary? In this paper, the authors identify the aspects of the major chronic illnesses that most need to be controlled and monitored in the US today and explore the feasibility of using current mobile phone technology to improve the management of chronic illness.