The Democratization of Data Analytics

Dr. David Delaney, Chief Medical Officer, SAP Health
AUGUST 07, 2017
value-based care, big data, SAP Health, healthcare analytics news, democratization of data, david delaney SAP
The healthcare ecosystem is becoming more integrated and data centric, as payments shift to reward outcomes across the continuum of care rather than just performance of activities in a non-integrated fashion. The goal is to achieve high quality care at affordable cost in a reproducible fashion. Of course, this is more a journey rather a destination.

The good news is that other information-intensive sectors have faced and overcome similar challenges. In any industry, once data is gathered and measured, it can be personalized and used in beneficial ways. Retail industry giant Target is a good example. Target’s mobile apps, including Cartwheel, gather customer data and preferences, giving users a very personalized shopping experience. The app has been an enormous success and has been downloaded 40 million times, saving consumers around $1 billion on deals via customized discounts. By smartly using and personalizing data, Target is rewarded with more sales, along with a deepening engagement and loyalty from its base.

Patients should expect that same kind of personalized approach to their healthcare. Providers and hospitals have the opportunity to borrow from the learnings in other industries, rather than reinventing each wheel along the way.

From Data Wrangling to Insights
The ability to capture—and use—data is starting to come together in healthcare. Delivery organizations are making progress in finding ways to improve patient outcomes, value, and quality of care. Initial work has been slow, as hospitals and healthcare organizations work to integrate different types of data from fragmented sources, including research, diagnostic and clinical imaging, and a growing array of Internet of Things (IoT) devices and wearables.

The work of interfacing, cleansing, and integrating this data has been far from trivial. In fact, data scientists and analysts often spend 80% or more of their time in data prep work rather than actual data science, where value is created. This has made analytics time consuming, expensive, and therefore largely reserved for the most pressing and strategic of questions. In essence, analytics has been the purview of the corner office, leaving the front lines to make decisions only with the data they have at hand, supplemented with gut and intuition.

However, evolving standards are easing interoperability. This, together with the speed and simplifying power of in-memory computing, are revolutionizing the use analytics across the enterprise. Data scientists and analysts are able to shift from time-consuming data wrangling to creating insights. This shift is key, since the current approach to analytics simply cannot be scaled to the enterprise level, especially given the shortage of people who are skilled in data science and analysis.

Perhaps even more revolutionary is the ability to leverage a graphical user interface (GUI) as a visual interface to the data, enabling democratization of data-driven decision-making across the enterprise. Embedding analytics in frontline decision-maker’s workflow--and allowing them to easily ask and answer questions--will have as profound and far-reaching impact as any preceding breakthrough in healthcare. Ultimately, with easier access and better data query tools, all types of practitioners and administrators will be able to use data analytics routinely to make informed decisions at the point of care.

This interoperability of data, empowerment of data analysts, and democratization of analytics at scale will power wholesale transformation of care delivery at the system level. As participants become more adept at leveraging analytics in their daily workflow, the next wave of capability leverages machine learning tools, to help healthcare organizations glean deep insights from ever larger, more complex, and disparate data sources. These tools will help them optimize systems and make evidence-based decisions, truly moving the meter toward cost savings and delivery of value-based care.

Focusing on Value-based Care
The healthcare ecosystem cannot move to value-based care without overcoming several challenges. One of the major impediments is the inability to accurately gauge the quality of care on all levels, as providers can’t manage what is not measured. The ability to integrate clinical and financial claims and operational data at the service-line level is an essential building block that we didn’t previously have.

Adding to that is the fact that patients and caregivers are becoming more involved in the management of their health, and hospitals are now looking for ways to provide the best-quality care at a reasonable cost. The ability to set, understand and improve pricing relies on the collective ability to understand care delivery and care variation.

We have already had key wins in data sharing, but we need more. Hospitals need to progress in their thinking beyond holding on to data silos and become more willing to share. Providers are moving toward open data access, as they realize the value of sharing data via the cloud. Healthcare records of all types are increasingly digitized. New data sources, such as genome profiles and personal health information recorded automatically from wearables and other IoT devices are becoming more commonplace. Patients’ electronic health records (EHRs) are also becoming more available, discoverable, and understandable by analytics systems.

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