Our goal is to provide a forum for discussion about analysis and application of individualized data
Data is playing an increasingly large role in our daily lives. In nearly every online transaction or internet search we perform, data is collected and analyzed by third parties for the specific purpose of making money off of us. The information contained in this data has the potential to reveal more about our lifestyles, interests, and habits, than anything we could hope to obtain from questionnaires or interviews, and in the hands of a doctor could help him or her to inform us about what changes are needed to live a healthier lifestyle. However, more often than not, this data is never analyzed due to a lack of consensus about how it should be used, and unfamiliarity with the methods needed to obtain valuable insights. The purpose of our organization is to provide a means to connect the data with clinical decisions in a manner that improves health for our patients. Although much of the content may be directed toward a more technical audience, the overall goal will be to provide a forum for exchanging ideas about how we can 'analyze my data’.
Contact
➤ LOCATION
12401 E. 17th Avenue, Leprino Bld. Aurora, CO, 80045
☎ CONTACT
Email: idao@ucdenver.edu
Phone: 720-848-6563
Analytical Domains
Wearable Device Data
Despite the wide adoption of wearable activity monitors (e.g., Fitbit watches), there is little data available about how to use the information they provide in a clinically informative way. One of our major focuses is on how the data from wearable devices about activity, sleep, and exercise can be used as a tool to get a better understanding of a person's overall health and any role in cause or prevention of individual health conditions, and to provide feedback about interventions.
comparative-effectiveness research
Comparative-effectiveness research seeks to understand the 'real world' effectiveness of various medical therapies. Historically, investigators in comparative-effectiveness research have had to resort to use of registry data, and other low-quality, summary level data. Our approach of measuring interventions and outcomes on each individual will thus unlock a whole new world of analyzing the relative effectiveness of each treatment on each person.
Tailored Lifestyle Modification
Biological monitors, and other data collection methods, allow for an unprecedented glimpse into our lifestyles. However, methods to utilize this data to draw inferences related to health and medical decision-making have largely lagged behind. Among the immediate application of our approach will be the quantitative use of this data in a manner that allows people to learn their lifestyle tendencies and make and measure adjustments.
Remote Monitoring and telemedicine
Real-time data collection from biological monitors allows for the ability of providers to assess and evaluate patients hundreds of miles away from the clinic. The use of technology to expand the coverage of healthcare into rural and underserved areas holds great potential. However, applications that are efficient, safe, and highly adopted remain largely elusive. Our work, particularly on automation of data analysis, will thus open the door to development of outreach telemedicine and remote monitoring programs.
Genetic and -omic data
Advances in gene sequencing, and methods to collect and analyze gene expression, protein expression, and metabolism, hold the potential to unlock the inner biology of disease. However, many of these methods have been applied to bulky, artificial, and often biased clinical datasets created from electronic health data and billing data. Our approach of using lifestyle and biological monitors holds great potential for application of -omic data, since ultimately, this data is reflective of biology, not physician data entry or diagnostic coding.
improving clinical efficiency
In contrast to the gain in efficiency that has accompanied nearly every other technology advancement into our daily lives, the implementation of electronic health record technology into the clinical setting has largely decreased efficiency, and made the process of care delivery more about entering data into a computer than face-to-face contact with patients. Through use of machine-learning technologies, and other big data methods, we aim to create a setting where doctors and nurses can spend more time talking to patients about disease than collecting and entering data.
“The good physician treats the disease; the great physician treats the patient who has the disease.”
We want your feedback!
The goal of our organization is to provide a forum for everyone, patients and investigators, scientists and clinicians, data analysts and users, to exchange information and methods about how to use big data in modern healthcare. We depend on thoughtful input from everyone. Please enter your comments below.