3 Top Business Intelligence, Analytics Healthcare Trends for 2016
Commentary: Business intelligence and analytics continues to be one of the fastest-moving areas in the enterprise, and the world of healthcare is no exception.
Business intelligence (BI) and analytics continues to be one of the fastest-moving areas in the enterprise, and the world of healthcare is no exception. Considering the changes being driven by healthcare reform, the Affordable Care Act (ACA), the increased use of electronic health records (EHRs) and the general increase in data volumes, this should come as little surprise.
Penetration of EHRs among providers is projected to hit 95 percent by 2020, while data is expected to grow 48 percent annually over that time period.
But technology and data volume and variability aren’t the only things moving fast; the techniques people are using to drive adoption and get value from their data are multiplying as well. Among these trends are a growing appetite for more advanced analytics to answer deeper questions, and new approaches emerging for governance of self-service BI and analytics.
Here are three of the biggest business intelligence (BI) and analytics trends for healthcare this year.
Advanced analytics is no longer just for analysts. Visionary BI and analytics leaders in healthcare are already seeing diminishing returns related to the “report factory” model, which relies on IT to produce reports and thus becomes a bottleneck in report delivery. Relying on IT to perform analytics and reporting, instead of relying on end users, means slower delivery, reports that are often out-of-date and an often unreasonable IT workload. Empowering users, on the other hand, is faster, more effective and unleashes the potential to achieve value.
In fact, self-service data discovery and visualization can empower knowledge workers like clinicians, physicians, nurses and business analysts to gain important insights from governed data, which can enable life-impacting decisions while also unlocking millions of dollars in value.
This is especially true because knowledge workers across organizations are becoming more sophisticated. They’ve come to expect more than a chart on top of their data. They want a deeper, more meaningful analytics experience, as well as the ability to ask and answer their own questions independently.
Consider Mount Sinai Health System, for example. While Mount Sinai had more than a decade of experience working with EHRs, it was having difficulty deriving value for the resulting data, until it focused on self-service analytics. Within a year, end users were using analytics to ask and answer their own questions, needing little assistance to do so. They have developed systems like disease registries and are studying things like how to reduce variances in treatment. An ER physician working with denials data identified $ 125 million in potential revenue leakage, while a nurse practitioner helped create a solution to proactively identify and improve patient satisfaction.
Governance and self-service analytics become best friends. One common obstacle to self-service analytics is governance. Many people have long considered governance and self-service analytics to be natural enemies, and that’s especially true in the sensitive world of healthcare.
The good news is that the war between governance and self-service is over, and the cultural gap between business and technology is closing. Organizations like Mount Sinai Health System have learned and proven that data governance, when done right, can help nurture a culture of analytics and meet the needs of the business.
At Mount Sinai, the IT department manages their complex environment in an environment with multiple EHRs. Meanwhile, its clinical data warehouse collects data from 120 sources. The IT department handles and centralizes all this data, which is key. It’s a reasonable and logical workload, for one. Plus, people are more likely to dig into their data when they have centralized, clean and fast data sources, and when they know that someone (IT) is looking out for security and performance.
More healthcare providers are adopting a Center of Excellence (COE) approach. While an emphasis on analytics is important, many organizations are taking it to the next level and establishing Centers of Excellence (COE) to foster adoption.
Such centers, which include things like online forums, one-on-one training, data governance and more, play a critical role in implementing a data-driven culture. More specifically, leaders in the healthcare industry deploying operational analytics across the enterprise will use a COE model to prototype and syndicate best practices, enable data governance and evangelize self-service visual analytics for measurable outcomes.
A prime example of such excellence and adoption comes from Providence Health, the second largest healthcare provider in the U.S. The provider built out an enterprise wide Operational Analytics Platform called Vantage. It’s grown from 20 users to 24,000 users in a little over a year; in addition, it’s offering measurable health outcomes improvements.
For example, one goal of the platform was to increase rates for screening patients for colorectal, breast and cervical cancer using better tracking. Because the system was transparent in making data available, physicians could also benchmark themselves with their peers, enabling healthy competition and improved performance.
It paid off. Last summer, Providence Health achieved its target goal, which was more than double the rate just a few years ago, resulting in a significantly larger number of cancer patients positively impacted through proactive screening. This type of measurable adoption and success is crucial to refining best practices, getting users excited and continuing to drive new, tangible outcomes.
Another success involves Blue Cross Blue Shield of North-Carolina, which enjoyed millions in savings from fraud, abuse and waste reduction; faster medical expense reporting and re-admission reporting thanks to its adoption of new BI techniques and platforms. A 360-degree view of the life-covered solution for case managers (which would have taken more than a year to build with legacy BI tools), was delivered by the COE using a concurrent engineering approach and a visual analytics platform in 90 days.
It’s clear the data-driven revolution in healthcare and beyond is just getting started. With more data and more demand for insights, self-service analytics adoption will increasingly become the norm, with governance, best practices and more built in.
Andy De is managing director and global general manager of healthcare and life sciences for Tableau Software.
Penetration of EHRs among providers is projected to hit 95 percent by 2020, while data is expected to grow 48 percent annually over that time period.
But technology and data volume and variability aren’t the only things moving fast; the techniques people are using to drive adoption and get value from their data are multiplying as well. Among these trends are a growing appetite for more advanced analytics to answer deeper questions, and new approaches emerging for governance of self-service BI and analytics.
Here are three of the biggest business intelligence (BI) and analytics trends for healthcare this year.
Advanced analytics is no longer just for analysts. Visionary BI and analytics leaders in healthcare are already seeing diminishing returns related to the “report factory” model, which relies on IT to produce reports and thus becomes a bottleneck in report delivery. Relying on IT to perform analytics and reporting, instead of relying on end users, means slower delivery, reports that are often out-of-date and an often unreasonable IT workload. Empowering users, on the other hand, is faster, more effective and unleashes the potential to achieve value.
In fact, self-service data discovery and visualization can empower knowledge workers like clinicians, physicians, nurses and business analysts to gain important insights from governed data, which can enable life-impacting decisions while also unlocking millions of dollars in value.
This is especially true because knowledge workers across organizations are becoming more sophisticated. They’ve come to expect more than a chart on top of their data. They want a deeper, more meaningful analytics experience, as well as the ability to ask and answer their own questions independently.
Consider Mount Sinai Health System, for example. While Mount Sinai had more than a decade of experience working with EHRs, it was having difficulty deriving value for the resulting data, until it focused on self-service analytics. Within a year, end users were using analytics to ask and answer their own questions, needing little assistance to do so. They have developed systems like disease registries and are studying things like how to reduce variances in treatment. An ER physician working with denials data identified $ 125 million in potential revenue leakage, while a nurse practitioner helped create a solution to proactively identify and improve patient satisfaction.
Governance and self-service analytics become best friends. One common obstacle to self-service analytics is governance. Many people have long considered governance and self-service analytics to be natural enemies, and that’s especially true in the sensitive world of healthcare.
The good news is that the war between governance and self-service is over, and the cultural gap between business and technology is closing. Organizations like Mount Sinai Health System have learned and proven that data governance, when done right, can help nurture a culture of analytics and meet the needs of the business.
At Mount Sinai, the IT department manages their complex environment in an environment with multiple EHRs. Meanwhile, its clinical data warehouse collects data from 120 sources. The IT department handles and centralizes all this data, which is key. It’s a reasonable and logical workload, for one. Plus, people are more likely to dig into their data when they have centralized, clean and fast data sources, and when they know that someone (IT) is looking out for security and performance.
More healthcare providers are adopting a Center of Excellence (COE) approach. While an emphasis on analytics is important, many organizations are taking it to the next level and establishing Centers of Excellence (COE) to foster adoption.
Such centers, which include things like online forums, one-on-one training, data governance and more, play a critical role in implementing a data-driven culture. More specifically, leaders in the healthcare industry deploying operational analytics across the enterprise will use a COE model to prototype and syndicate best practices, enable data governance and evangelize self-service visual analytics for measurable outcomes.
A prime example of such excellence and adoption comes from Providence Health, the second largest healthcare provider in the U.S. The provider built out an enterprise wide Operational Analytics Platform called Vantage. It’s grown from 20 users to 24,000 users in a little over a year; in addition, it’s offering measurable health outcomes improvements.
For example, one goal of the platform was to increase rates for screening patients for colorectal, breast and cervical cancer using better tracking. Because the system was transparent in making data available, physicians could also benchmark themselves with their peers, enabling healthy competition and improved performance.
It paid off. Last summer, Providence Health achieved its target goal, which was more than double the rate just a few years ago, resulting in a significantly larger number of cancer patients positively impacted through proactive screening. This type of measurable adoption and success is crucial to refining best practices, getting users excited and continuing to drive new, tangible outcomes.
Another success involves Blue Cross Blue Shield of North-Carolina, which enjoyed millions in savings from fraud, abuse and waste reduction; faster medical expense reporting and re-admission reporting thanks to its adoption of new BI techniques and platforms. A 360-degree view of the life-covered solution for case managers (which would have taken more than a year to build with legacy BI tools), was delivered by the COE using a concurrent engineering approach and a visual analytics platform in 90 days.
It’s clear the data-driven revolution in healthcare and beyond is just getting started. With more data and more demand for insights, self-service analytics adoption will increasingly become the norm, with governance, best practices and more built in.
Andy De is managing director and global general manager of healthcare and life sciences for Tableau Software.
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