What Is Predictive Analytics (and Why Do You Need It)?

Try this statistic on for size: The 500 petabytes of digital healthcare data that existed in 2012 is predicted to reach 25,000 petabytes by the year 2020. That’s an increase of nearly 50 times the amount of data from just eight years prior!Diagrams projecting from tablet

Healthcare marketers may be swimming in data, but what’s important is to not drown in it. The key, in other words, is to not just have the data – but to figure out what you’re going to do with the data.

As will be discussed during today’s Evariant webinar, analytics in healthcare is critical for making effective use of data. Predictive analytics can help drive improved health outcomes for patients and give you the biggest bang for your marketing buck.

But the question is: What, exactly, is predictive analytics? More

William Disch

William Disch

As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).

Understanding Extended 360° Views of Patients and Physicians

This is the first in a series of three blog posts that discusses how you can achieve Extended 360° (X-360°) views of patients and physicians through big data analytics. The second post in this series will discuss the three primary rules of X-360° views and best practices to develop them. The third post in the series will discuss how you can achieve Extended 360° (X-360°) views of patients and physicians through big data analytics.

Hospitals and health systems have some of the most complex and contextually rich data sets of any industry, yet these institutions struggle to optimize the power of their data to deliver a more patient-centric approach to care. Now, technology is available that
can efficiently process, manage, and analyze massive amounts of disparate data across a health system — what we call “big data” and “big data analytics.” Big data analytics will have an immense impact on the healthcare industry because it provides the ability to “uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information … <that> can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations, and other business benefits.” More

William Disch

William Disch

As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).

How We Use Big Data Analytics in Healthcare to Create Patient and Customer Intimacy

We’ve discussed in previous blogs the sheer volume of data that is available to – and underutilized by – healthcare organizations. However, this wealth of customer information can (and should) be used by health systems to build and foster customer and patient relationships, with the ultimate goal of optimizing business, marketing, and clinical outcomes.

In order for key healthcare players to truly engage their customers and create a unique, intimate experience on a large scale, they must take advantage of and contextualize “big data analytics.”

1. What is Big Data Analytics?

By 2020, the amount of information stored worldwide will equal 44 zettabytes. This number is unfathomable to most, and rightly so – it is 50 times larger than the amount stored today.

Big data analytics in healthcare is the process of dynamically deriving patterns and insights from consumer and patient data and using that information to make better healthcare decisions. More

William Disch

William Disch

As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).

How Predictive Modeling Promotes Healthcare Service Lines

This is the final piece of our three part series on using predictive models in healthcare. Be sure to check out parts one and two for more information on how predictive modeling can be used to attract and identify patients.

Predictive modeling allows healthcare marketers the inside view into their big data. With patient modeling, marketing departments have the ability to score potential patients based on a set of criteria, identify and target the ones who are most likely to respond and tailor communications to them.

The use of predictive modeling, or propensity modeling, helps marketing departments better utilize their marketing dollars and help cut down on inefficient marketing spend. Evariant has incorporated this into their engagement platform, allowing their users to make smarter marketing decisions.

More

William Disch

William Disch

As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).

How Healthcare Models Identify Potential Patients

This is the second post in our three part series on using predictive models in healthcare. See our first post and be sure to stay tuned for the final piece where we look at how predictive models can help in healthcare promotion.

When creating healthcare campaigns to attract potential patients, marketers are constantly in a bind to decide where and how to allocate their marketing dollars. When pulling a list of potential patients to communicate with, some lists contain any individual that fits within a certain, usually broad, criteria. Using this method can lead to a smaller return on marketing investment and a lower qualified response rate. More

William Disch

William Disch

As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).