Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going

Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going

© 2014 Health Catalystwww.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation.

The Big Data Questions

Big data is generating a lot of hype in every industry including healthcare.Leaders in the industry all want to know about the importance of Big Data. They ask questions such as:

• When will I need big data?• What should I do to prepare for big data?• What’s the best way to use big data?• What is Health Catalyst doing with big data?


Big Data in Healthcare Today

A number of use cases in healthcare are well suited for a big data solution. Some academic- or research-focused healthcare institutions are either experimenting with big data or using it in advanced research projects. This presentation will examine what’s being done to simplify big data and make it more accessible.


Big Data in Healthcare TodayA Brief History of Big Data in Healthcare

In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data:

• Volume• Velocity• Variety

Other analysts argued that this is too simplistic but for this purpose let’s start here.

http://www.gartner.com/it-glossary/big-data

http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf



EMRs alone collect huge amounts of data, but according to Brent James of Intermountain Healthcare most of the data is for recreational purposes. Our work with health systems shows that only a small fraction of the tables in an EMR database (perhaps 400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases.

http://intermountainhealthcare.org/Pages/home.aspx

http://intermountainhealthcare.org/Pages/home.aspx

https://intermountainphysician.org/UrologySeminar/Documents/10_James.pdf

https://intermountainphysician.org/UrologySeminar/Documents/10_James.pdf



There is certainly variety in the data, but most systems collect very similar data objects with an occasional tweak to the model. That said, new use cases that support genomics will certainly require a big data approach.


Big Data in Healthcare TodayHealth Systems Without Big Data

Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs.We haven’t come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and using these databases effectively is a more valuable focus than worrying about big data.

http://www.healthcatalyst.com/healthcare-analytics-adoption-model/


Big Data in Healthcare TodayHealth Systems Without Big Data

Most healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards. As basic needs are met and some of the initial advanced applications are in place, new use cases will arrive (e.g. wearable medical devices and sensors) driving the need for big-data-style solutions.

http://www.healthcatalyst.com/value-of-healthcare-dashboards


Barriers Exist for Using Big DataExpertise and Security

Several challenges with big data have yet to be addressed in the current big data distributions. Two roadblocks to the general use of big data in healthcare are the technical expertise required to use it and a lack of robust, integrated security surrounding it.


Barriers Exist for Using Big DataExpertise

The value for big data in healthcare today is largely limited to research because using big data requires a very specialized skill set. Hospital IT experts familiar with SQL programming languages and traditional relational databases aren’t prepared for the steep learning curve and other complexities surrounding big data.



Data scientists are usually Ph.D.-level thinkers with significant expertise.These experts are hard to come by and expensive, and only research institutions usually have access to them. Data scientists are in huge demand across industries like banking and internet powers with deep pockets.



The good news is, thanks to changes with the tooling, people with less-specialized skillsets will be able to easily work with big data in the future. Big data is coming to embrace SQL as the lingua franca for querying. And when this happens, it will become useful in a health system setting.


Barriers Exist for Using Big DataSecurity

In healthcare, HIPAA compliance is non-negotiable. Nothing is more important than the privacy and security of patient data.Unfortunately, security hasn’t been a priority up to this point and there aren’t many good, integrated ways to manage security in big data. When opening up access to a large, diverse group of users, security cannot be an afterthought.

https://www.healthcatalyst.com/HIPAA-security-compliance-culture-technology

https://www.healthcatalyst.com/HIPAA-security-compliance-culture-technology


Barriers Exist for Using Big DataSecurity

The best option for healthcare organizations looking to implement big data is to purchase a well-supported, commercial distribution rather than starting with a raw Apache distribution. Another option is to select a cloud-based solution like Azure HDInsight to get started quickly.

http://azure.microsoft.com/en-us/services/hdinsight/

http://azure.microsoft.com/en-us/services/hdinsight/


Big Data Differs from Current Systems It’s Unlike Typical Relational Databases

Big data differs from a typical relational database. This is obvious to a CIO or an IT director, but a brief explanation of how the two systems differ will show why big data is currently a work in progress—yet still holds so much potential.


Big Data Differs from Current Systems Big Data Has Minimal Structure

The biggest difference between big data and relational databases is that big data doesn’t have the traditional table-and-column structure found in relational databases.In contrast, big data has hardly any structure at all. Data is extracted from source systems in its raw form stored in a massive, somewhat chaotic distributed file system.


Big Data Differs from Current Systems Big Data Is Raw Data

By convention, big data is typically not transformed in any way. Little or no “cleansing” is done and generally, no business rules are applied. Some people refer to this raw data in terms of the “Sushi Principle” (i.e. data is best when it’s raw, fresh, and ready to consume). Interestingly, the Health Catalyst Late-Binding™ Data Warehouse follows the same principles.

https://www.healthcatalyst.com/late-binding-data-warehouse-platform

https://www.healthcatalyst.com/late-binding-data-warehouse-platform


Big Data Differs from Current Systems Big Data Is Less Expensive

Due to its unstructured nature and open source roots, big data is much less expensive to own and operate than a traditional relational database.A Hadoop cluster is built from inexpensive, commodity hardware, and it typically runs on traditional disk drives in a direct-attached (DAS) configuration rather than an expensive storage area network (SAN).


Big Data Differs from Current Systems Big Data Has No Roadmap

The lack of pre-defined structure means a big data environment is cheaper and simpler to create.So what’s the catch? The difficulty with big data is that it’s not trivial to find needed data within that massive, unstructured data store. A structured relational database essentially comes with a roadmap—an outline of where each piece of data exists.



With a relational database, a simple, structured query language (i.e. SQL) pulls the needed data using a sophisticated query engine optimized for finding data.With big data, the query languages are much more complicated. A data scientist is needed to find the subset of data required for applications.



Creating the required MapReduce algorithms for querying big data instances isn’t for the faint of heart. Fortunately, that’s changing at a fairly rapid pace with tools like SparkSQL and other query tools that leverage conventional SQL for querying. In short, big data is cheap but more difficult to use. Relational databases are expensive but very usable.

https://spark.apache.org/sql/


It’s Coming: Big Data in Healthcare

When healthcare organizations envision the future of big data, they often think of using it for analyzing text-based notes.Big data indexing techniques, and some of the new work finding information in textual fields, could indeed add real value to healthcare analytics in the future.


It’s Coming: Big Data in HealthcareBig Data and the Internet of Things

Big data will become valuable to healthcare in what’s known as the internet of things (IoT). SAS describes the IoT as:

a growing network of everyday objects from industrial machines to consumer goods that can share information and complete tasks while you are busy with other activities, like work, sleep, or exercise.

http://online.barrons.com/articles/tech-outlook-the-next-industrial-revolution-1413002738




It’s Coming: Big Data in HealthcareBig Data and the Internet of Things

For healthcare, any device that generates data about a person’s health and sends that data into the cloud will be part of this IoT.Wearables are perhaps the most familiar example of such a device. Many people now can wear a fitness device that tracks their heartrate, their weight, how it’s all trending, and then their smartphone sends that data to a cloud service.


It’s Coming: Big Data in HealthcareBig Data and Care Management

ACOs focus on managed care and want to keep people at home and out of the hospital.Sensors and wearables will collect health data on patients in their homes and push all of that data into the cloud. Healthcare institutions and care managers, using sophisticated tools, will monitor this massive data stream and the IoT to keep their patients healthy.


The Fun Stuff: Predictive Analytics, Prescriptive Analytics, and Genomics

Real-time alerting is just one important future use of big data. Another is predictive analytics.The use cases for predictive analytics in healthcare have been limited up to the present because we simply haven’t had enough data to work with. Big data can help fill that gap.

http://www.healthcatalyst.com/predictive-analytics



One example of data that can play a role in predictive analytics is socioeconomic data. Socioeconomic data might show that people in a certain zip code are unlikely to have a car. There is a good chance, therefore, that a patient in that zip code who has just been discharged from the hospital will have difficulty making it to a follow-up appointment at a distant physician’s office.



This and similar data can help organizations predict missed appointments, noncompliance with medications, and more. That is just a small example of how big data can fuel predictive analytics. The possibilities are endless.


The Fun Stuff: Patient Flight Paths and Prescriptive Analytics

Another use for predictive analytics is predicting the “flight path” of a patient. Leveraging historical data from other patients with similar conditions, predictive algorithms can be created using programming languages such as R and big data machine learning libraries to faithfully predict the trajectory of a patient over time.

https://www.healthcatalyst.com/patient-flight-path-analytics-airline-operations-healthcare-outcomes


The Fun Stuff: Patient Flight Paths and Prescriptive Analytics

Once we can accurately predict patient trajectories, we can shift to the Holy Grail–Prescriptive Analytics. Intervening to interrupt the patient’s trajectory and set him on the proper course will become reality. Big data is well suited for these futuristic use cases.


The Fun Stuff: Genomic Sequencing and Big Data

The use of genomic data is on the rise in patient treatment. The cost of sequencing an individual’s full genome has plunged in recent years.Sequencing will become commonplace and eventually become a commodity lab test.Genomic sequences are huge files and the analysis of genomes generates even more data.


The Future of Healthcare Data Warehousing And the Transition to Big Data

With the present limitations for big data in healthcare and the truly fascinating future possibilities that big data enables. An important question to address at this point is: What should a health system do in the meantime?



Today, health systems’ need for data-driven quality and cost improvement is urgent. Healthcare organizations cannot afford to wait for big data technology to mature before diving into analytics. The important factor will be choosing a data warehousing solution that can easily adapt to the future of big data.

http://www.healthcatalyst.com/5-Deming-Principles-For-Healthcare-Process-Improvement

http://www.healthcatalyst.com/5-Deming-Principles-For-Healthcare-Process-Improvement



A Late-Binding™ enterprise data warehouse (EDW) architecture is ideal for making the transition from relational databases to unstructured big data. The late-binding approach is very similar to the big data approach. In a Late-Binding EDW like Health Catalyst’s, data from source systems are placed into source marts.The data remains in its raw state until someone needs it.

https://www.healthcatalyst.com/best-healthcare-data-warehouse-model




The Future of Healthcare Data Warehousing Real World Example Healthcare’s Transition to Big Data

In conclusion, here is a brief example of how the transition from relational databases to big data is happening in the real world.We are working with one of our large health system clients and Microsoft to create a massively parallel data warehouse in a Microsoft APS Appliance that also includes a Hortonworks Hadoop Cluster.

http://www.microsoft.com/en-us/server-cloud/solutions/modern-data-warehouse/

http://www.microsoft.com/en-us/server-cloud/solutions/modern-data-warehouse/



This means we can run a traditional relational database and a big data cluster in parallel.We can query both data stores simultaneously, which improves data processing power. Together, we are beginning to experiment with big data in important ways, such as performing natural language processing (NLP) with physician notes, predictive analytics, and other use cases.



The progression from today’s symmetric multiprocessing (SMP) relational databases to massively parallel processing (MPP) databases to big data in healthcare is underway


More about this topicHadoop in Healthcare: A No-nonsense Q and AJared Crapo, Vice President

Big Data in Healthcare: Separating the Hype from the Reality Jared Crapo, Vice President

In Healthcare Predictive Analytics, Big Data Is Sometimes a Big Mess David Crockett, Ph.D., Senior Director of Research and Predictive Analytics

Using Predictive Analytics in Healthcare: Technology Hype vs. RealityDavid Crockett, Ph.D., Senior Director of Research and Predictive Analytics

3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve Healthcare’s Problems Dale Sanders, Senior Vice President of Strategy

Link to original article for a more in-depth discussion.

Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going

https://www.healthcatalyst.com/Hadoop-in-healthcare

https://www.healthcatalyst.com/healthcare-big-data-realities

https://www.healthcatalyst.com/predictive-analytics-big-data-big-mess

https://www.healthcatalyst.com/predictive-analytics-healthcare-technology

https://www.healthcatalyst.com/3-reasons-why-comparative-analytics-predictive-analytics-and-nlp-wont-solve-healthcares-problems/

https://www.healthcatalyst.com/3-reasons-why-comparative-analytics-predictive-analytics-and-nlp-wont-solve-healthcares-problems/

https://www.healthcatalyst.com/big-data-in-healthcare-made-simple




For more information:

https://www.healthcatalyst.com/ebooks/healthcare-data-warehouse-ebook/


Other Clinical Quality Improvement ResourcesClick to read additional information at www.healthcatalyst.com

Douglas Adamson joined Health Catalyst in June 2012 as Vice President of Architecture. Prior to joining Catalyst, Doug worked for GE Healthcare in a number of roles including Chief Technologist, Chief Architect and General Manager of Engineering. Doug also spent 14 years working as a software engineer on the Human Genome Project. He holds a Bachelor of Science degree in Computer Science from Purdue University in West Lafayette, Indiana with additional graduate work in Computer Science and Math.

http://www.healthcatalyst.com/