trustedanalytics.org Trusted Analytics Platform (TAP) How TAP-Powered Big Data Analytics Improves Efficacy of Clinical Trials at OHSU In 2015, Oregon Health & Science University’s Knight Cardiovascular Institute used wearable devices as part of a clinical trial to study how patterns in sleep and heart rate may be associated with risk for diabetes and heart disease. What’s different about this project is that the team collected data about the everyday lives of volunteer participants 24x7, as well as electronic health records and laboratory results from clinics, to amass much more information than an average clinical study. The trial involved 359 participants over six months and produced more than 500 million data points. The volume, variety and velocity of this data put OHSU team solidly into the technically challenging world of Big Data. The OHSU team used the open source Trusted Analytics Platform™ (TAP) to collect patient data and prepare it for data scientists, who are building advanced Big Data analytics models aimed at discovering new insights. It also used TAP to monitor data flow from devices to identify anomalies and errors over the course of the study – an important capability for “adaptive” clinical trials in which researchers can learn in near-real-time and make adjustments to drive toward the most promising avenues of investigation. Data were ingested into a Cloudera Distribution of Hadoop. Using TAP, with Arcadia for visualization, SQL queries were run over datasets to establish baselines and identify trends. In addition, near real-time monitoring of trial data allowed the team to quickly identify and correct problems that would have skewed results. TAP was also used to examine the dataset to identify data that was wrong, missing or duplicated. With the TAP Analytics Toolkit, the team was able to use iPython to write scripts to do queries across multiple tables. For instance, they were able to join eight tables even though they weren’t balanced – the step data was minute-by-minute for 24 hours a day, but the sleep tables were around 8 hours of minute-by- minute rows. These unbalanced tables were able to be joined with six other metrics from patients’ Basis Peak™ smartwatch. “ The most powerful feature of TAP is the seamless integration of data storage, access and analytics. Previously, it is not unusual for me to spend considerable amount of time pulling different parts of data here and there, formatting and transforming before doing any analysis. TAP makes analysis of Big Data a lot easier without expert knowledge of the state-of-the-art technologies under the hood. ” Yuliang Wang, a data scientist at OHSU Several participants reported increasing their engagement in their own health as a result of viewing their data from the Basis Peak watch. In addition, researchers were able to test a new Big Data approach to gaining insights about cardiovascular and diabetes risk enabled by continuous data streams that provide context about the actual lives of people — information that is impossible to know in traditional studies. By allowing the OHSU team to capture and manage data from multiple sources in different formats, TAP increased the efficacy of the clinical trial and set a new bar for future research. Solution Brief

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Trusted Analytics Platform (TAP)

How TAP-Powered Big Data AnalyticsImproves Efficacy of Clinical Trials at OHSUIn 2015, Oregon Health & Science University’s Knight Cardiovascular Institute used wearable devices as part of a clinical trial to study how patterns in sleep and heart rate may be associated with risk for diabetes and heart disease.

What’s different about this project is that the team collected data about the everyday lives of volunteer participants 24x7, as well as electronic health records and laboratory results from clinics, to amass much more information than an average clinical study. The trial involved 359 participants over six months and produced more than 500 million data points. The volume, variety and velocity of this data put OHSU team solidly into the technically challenging world of Big Data.

The OHSU team used the open source Trusted Analytics Platform™ (TAP) to collect patient data and prepare it for data scientists, who are building advanced Big Data analytics models aimed at discovering new insights. It also used TAP to monitor data flow from devices to identify anomalies and errors over the course of the study – an important capability for “adaptive” clinical trials in which researchers can learn in near-real-time and make adjustments to drive toward the most promising avenues of investigation.

Data were ingested into a Cloudera Distribution of Hadoop. Using TAP, with Arcadia for visualization, SQL queries were run over datasets to establish baselines and identify trends. In addition, near real-time monitoring of trial data allowed the team to quickly identify and correct problems that would have skewed results.

TAP was also used to examine the dataset to identify data that was wrong, missing or duplicated. With the TAP Analytics Toolkit, the team was able to use iPython to write scripts to do queries across multiple tables. For instance, they were able to join eight tables even though they weren’t balanced – the step data was minute-by-minute for 24 hours a day, but the sleep tables were around 8 hours of minute-by- minute rows. These unbalanced tables were able to be joined with six other metrics from patients’ Basis Peak™ smartwatch.

“The most powerful feature of TAP is the seamless integration of data storage, access and analytics. Previously, it is not unusual for me to spend considerable amount of time pulling different parts of data here and there, formatting and transforming before doing any analysis. TAP makes analysis of Big Data a lot easier without expert knowledge of the state-of-the-art technologies under the hood.”Yuliang Wang, a data scientist at OHSU

Several participants reported increasing their engagement in their own health as a result of viewing their data from the Basis Peak watch. In addition, researchers were able to test a new Big Data approach to gaining insights about cardiovascular and diabetes risk enabled by continuous data streams that provide context about the actual lives of people — information that is impossible to know in traditional studies.

By allowing the OHSU team to capture and manage data from multiple sources in different formats, TAP increased the efficacy of the clinical trial and set a new bar for future research.

Solution Brief