With real-time analytics and visual tools, healthcare data is more accessible to all healthcare providers–not just data engineers—and is helping them find efficiencies and make informed decisions.
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.
From Data Wrangling to Insights
The ability to capture—and use—data at an enterprise level is starting to happen in healthcare. Delivery organizations are making progress, 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 clinical, financial, claims, operational, research, 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. If 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 further 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. On the major impediments is the inability to accurately gauge quality across the care continuum, 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 focusing on ways to provide the best-quality care in a cost-effective fashion. 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 their data and become more willing to share across institutions. Providers are moving toward more open data access, as they realize the value of sharing data. Healthcare data of all types are increasingly digitized as the norm. New data sources, such as genomics and home monitoring recorded automatically from wearables are becoming more commonplace. Patients’ electronic health records (EHRs) are also becoming ever more available, discoverable, and understandable by analytics systems.
Using Analytics to Derive Better Outcomes
Analytics are key to a value-based care system. Real-time analytics allow providers to react more organically to what is happening. The emergence of predictive analytics allows providers to move from reactive to proactive. With an in-memory computing platform that can be accessed by all departments, users can ask a question, apply their own lens to the data filters, and drill down to find answers. Visualization layers provide GUI-based views of data, making it more accessible and usable to all people in the healthcare ecosystem.
By combining clinical, financial, claims, and operational data, organizations can understand care delivery variances. Usage of unstructured data is increasing. For example, a tumor marker noted in free text in a pathology report can be mapped using natural language processing into structured data elements. That information is then usable to better inform care decisions based on real world evidence.
This improvement allows a physician to use real-world data from a similar case at the point of decision for treatment. Overall, this shift can provide better outcomes, improved efficiency and better value, helping the health delivery system more efficiently drive toward value-based care.
Case Study: Mercy St. Louis
A prime example of a transition to an evidence-based care model is Mercy in St. Louis. The seventh largest Catholic health care system in the U.S., Mercy serves millions of patients annually from 35 acute care centers, 11 specialty hospitals, and more than 700 physician practices and outpatient facilities in multiple states.
Mercy realized that delivering evidence-based and personalized medicine is crucial to improving care. To do this, they had to fully leverage not only their data, but external data. Mercy needed a health IT infrastructure with integrated analytics that would enable it to support complex business and clinical processes. These initiatives were critical for the transition to a value-based payment environment of improved quality and reduced costs, which led them to On-Demand Patient Data with SAP HANA.
Mercy is leveraging the SAP HANA platform and analytics solutions to analyze data in real-time across its network, helping to improve patient care and drive millions in savings. The working model at Mercy is yielding remarkable results: from $1.2 million in savings for total knee replacement costs in the first fiscal year to $13 million in overall savings in less than two years.
Curtis Dudley, Vice President of Performance Solutions at Mercy, explains that Mercy has now organized millions of records from multiple sources—EHRs, financial systems, and external data. What used to take weeks to deliver, it embeds where and when it is needed—back into the EHR or through custom dashboards, which provide on-the-spot exploration for instant answers to the Mercy staff.
Standards for Interoperability
To build a functional data-sharing as efficient as Mercy, a common standard is essential. One emerging example is Fast Healthcare Interoperability Resources (FHIR), which is a draft standard for exchanging healthcare information electronically. FHIR allows for data exchange between healthcare applications and supports multiple data types. It is less expensive than previous models and has a strong focus on implementation. With the capability of providing Web-based queries, the shared data is more accessible to practitioners than ever before.
Automated clinical decision support and machine-based processing requires structured data formats, which FHIR can provide. Machine-based learning and artificial intelligence (AI) tools will augment the value of collected data sets even further. Machine learning helps analysis, as machine can look faster, deeper, and to broader datasets to learn from massive amounts of collected healthcare information.
For health providers to thrive in an increasingly value-based payment world, we need broad, integrated datasets to understand care delivery at a service line level and then begin the work to manage care variation and cost. You can’t manage what you don’t measure so these capabilities will increasingly be table stakes for survival going forward.
But the real game changer isn’t just being able to create insight from an integrated dataset. It is the ability to democratize this capability at the point-of-decision to take insight and create impact at a broad organizational level. In-memory computing and visual discovery tools, are key components in achieving the ability to do deep analytics at-scale in a democratized fashion. Ultimately, that is the engine that will help shift healthcare from a cottage industry to a data-driven, learning enterprise.
*TechCrunch, “Target and Cartwheel apps to merge starting this summer, mobile payments and improved maps to follow.”
**Forbes, “Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says”