Big Data in Healthcare

by

Dr. Aggelos Liapis

Research & Innovation Coordinator Athens University of Economics and Business

E-Business Research Center - [ELTRUN]

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?

When is Data ‘Big’?

Volume Velocity VeracityVariety Value

Data at Rest

Terabytes to exabytes of

existingdata to process

Data in Motion

Streaming data, requiring

mseconds to respond

Data in Many Forms

Structured, unstructured, text,

multimedia,…

Data in DoubtUncertainty due to data inconsistency & incompleteness,

ambiguities, latency, deception

€€

€

€

€

€ €

€

Data into Money

Business models can be associated

to the data

Adapted by a post of Michael Walker on 28 November 2012

Big Data in Healthcare Today – [Data]

• EMRs alone collect huge amounts of data however research has shown that most of the data is for recreational purposes.

• 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.

Big Data in Healthcare Today – [Systems]

• Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs.

• 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.

Existing Barriers for Using Big Data (1)

Two roadblocks to the general use of big data in healthcare:• Lack of technical expertise required to use it • Lack of robust, integrated security surrounding it.

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 learning curve and other complexities surrounding big data.

Existing Barriers for Using Big Data (2)

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.

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, could indeed add real value to healthcare analytics in the future.

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

• 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. (e.g. Wearables, mirrors, smart homes etc.)

The Fun Stuff:

• Predictive Analytics• Prescriptive Analytics• Drug Discovery

The Fun Stuff: Predictive Analytics (1)

• 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 health organisations simply haven’t had enough data to work with.

Big data can help fill that gap!!!

The Fun Stuff: Predictive Analytics (1)

• 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.

The Fun Stuff: 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.

• 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.

The Fun Stuff: Drug Discovery

Useful ResourcesHadoop in Healthcare: A No-nonsense Q and AJared Crapo, Vice President

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

In Healthcare Predictive Analytics, Big Data Is Sometimes a Big MessDavid 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

Thank You!

Dr. Aggelos Liapis

Research & Innovation Coordinator Athens University of Economics and Business

E-Business Research Center - [ELTRUN]