BLOG: Uncovering Patterns in Big Data

Embedded in the many data sets that comprise it,Big Datamay provide an understanding of how health care can be improved. But because its volumes of information can overwhelm traditional means of analysis, valuable patterns may emerge only through the use ofdeep learning (DL) algorithms. This form ofartificial intelligencecan transform Big Data into actionable information, giving providers the insights through which they might reduce health care costs and improve care.

With its DL algorithms, theImalogix这个平台展示了如何利用大数据来影响医疗保健。

“You can see how you — yourself — are doingor你可以看到自己和同龄人相比表现如何。”saidThomas Griglock, Ph.D., chief diagnostic imaging physicist atOregon Health and Science University(OHSU). “You can find out if your doses are high for some types of CT protocols compared to the national average; how the uptime on your scanners compares; how your machines are being utilized compared to others.”

Improved patient positioning during CT, for example, can be achieved through DL analyses. DL algorithms may be leveraged at a single site or across multiple sites within an enterprise to improve image quality and reduce patient radiation dose. This can impact diagnostic accuracy and make monitoring more effective, just as it can improve patient safety.

例如，医生可以使用DL分析来量化病人在某一特定过程中受到的辐射量。这可能有助于推动行为的改变，可能导致最低剂量可以应用，同时仍然产生可接受的检查结果。

If achieved across an enterprise, DL analyses of Big Data may increase consistency and efficiency. This may boost patient satisfaction by making scheduling more reliable. Increased efficiency may also add to the facility’s financial bottom line.

By leveraging advanced analytic solutions that leverage Big Data and DL algorithms, providers can quickly apply what is learned from those analyses to get continuously better – clinically, operationally and financially. The Imalogix system, Griglock said, “provides real world, actionable feedback. (It is) as close as you can get to plug-and-play.”

Unleashing The Potential Of Big Data

Achieving this potential requires leveraging thecloud. Through the cloud, data sets are collected from multiple sites and transformed into Big Data. Through repeated dives into this data, DL algorithms learn to identify complex patterns. These analyses are then turned into actionable information about how performance can be more effective and efficient; how equipment can be maximized to ensure high throughput without comprising quality; and how high-quality images can be generated while patients are exposed to less radiation.

Imalogix DL algorithms can provide details about equipment utilization and staffing at a single site or enterprise, breaking down information into intuitive dashboards and graphs. (These may be broken down further by hour, site and number of exams.) Or analyses may compare CT techniques performed at one hospital to those performed at ones across the U.S.

At OHSU, Griglock and colleagues use Imalogix analyses to compare scanner operations in the enterprise to each other, as well as to ones at other institutions across the U.S. In these ways, the university uses analyses of Big Data – these comparative analyses -- to identify areas in which operations may be improved. Predictive analyses, according toJohn Heil他是Imalogix公司的创始人兼首席执行官，可以用来预测员工的变动如何导致业绩的改善。

OHSU also uses DL analyses to meet everyday requirements, for example, to comply with mandates. Griglock and colleagues use the cloud-based system to keep tabs on patient exposure to radiation during CT exams to meet requirements set by theJoint Commission.

“The Joint Commission mandated a few years ago that everybody needed to keep track of dose,” Griglock said. “This (the Imalogix system) is probably the best way that I have seen to achieve compliance with those regulations.”

How Imalogix Leverages Big Data

Importantly, Big Data is not only processed in the cloud to provide actionable analyses, it is how DL algorithms learn to uncover actionable patterns. Just as DL algorithms are trained on this Big Data, they continue to learn during their analyses as new information is added. This allows the algorithms to improve continuously, bringing new discoveries to users.

The goal is not just to use Big Data efficiently. It is to make Big Data usable — “to deliver insights that support new opportunities in cost savings and better patient care,” Heil said.

Analyses using Imalogix DL algorithms may identify opportunities that a site may have to improve patient safety and image quality, for example, by identifying improvements in patient positioning. An enterprise will receive an overall score as to how well its CT technologists center patients in the scanner. That score is compared to those of peers. Users of the Imalogix system receive information on how to improve their scores, information that can benefit the overall team as well as specific technologists.

Identifying areas of opportunity is the first step toward improved performance. The next is to use DL algorithms to analyze Big Data so as to forecast how staff actions might improve the site’s overall performance compared to that of its peers.

“Based on last month’s performance, we predict how well you will do if you followed through with any of the recommendations Imalogix uncovers,” Heil said. “That really gets to the answers of how to change a business and what the performance impact is going to be.”

This process, he said, is never ending: “We engage with our customers daily, watching the data, learning how their businesses are going, figuring out how to help by delivering intelligent and actionable insights.”

Underlying it all is Big Data.

Editor’s Note:This is the third in a four-part series about the value of the cloud in today’s health care. The first blog,How Healthcare Can Benefit From the Cloud, can be readhere. The second blog,What AI Does in the Cloud, can be foundhere.

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