Analytics Remains Challenge in Healthcare
Earlier this year, HGS presented our trends forecast, comprising customer experience (CX) disrupter predictions, supported by practical strategies clients can use to succeed in the changing marketplace. This HGS ebook covers 11 trends, from those in self-service, mobile service, messaging, and social media, to artificial intelligence, robotic process automation and analytics. With this blog, we describe how the marketplace in 2019 has changed in line with the insight from, Ryan Howells, Principal, Leavitt Partners, who predicted how the healthcare industry has mastered the collection of data.
By Rawool Sahu, Director, Business Analytics, HGS Healthcare
This article was published in Internet Health Management.
At the turn of the century, electronic health information, large-scale data management and other digital systems gained mainstream acceptance and became core technology investments for most healthcare organizations. While there is little doubt that these advancements have allowed leaders on the delivery side to effectively improve the quality of care by providing a more personalized approach, data democratization has remained a concern and many important segments of business feel left out.
As an example, according to healthcare consulting firm Kaufman Hall, 96% of healthcare chief financial officers (CFOs) think that their organizations need to do more to leverage financial and operational data analytics, and 94% said they have experienced increased pressure to have more insight into how financial results impact business strategy. In fact, confidence among the country’s hospital CFOs is low, with just 13% saying their organization is “very prepared” to handle new healthcare payment and delivery models with current financial planning tools, according to Kaufman Hall’s 2019 CFO Outlook: Healthcare report. That’s down from 15% in 2018.
There’s no argument that the healthcare industry has mastered the collection of data. The challenge is making it actionable and easily available to its consumers. About 80% of healthcare data is unstructured, making it extremely difficult to apply against business or clinical challenges, including, population health management, countering fraud, waste and abuse and other administrative and financial transactions. Further, even the 20% that is structured presents enormous challenges in a value-based care world. For example, the datasets held by payers and providers can be different. Payers possess data on claims, reimbursement and risk models. Providers have administrative and clinical data that includes case histories and outcomes.
Each data set is valuable, but in isolation doesn’t provide a holistic and contextual perspective of the consumer. Providers need to leverage payer data in order to move from episodic care to delivering outcome-based care across the care continuum. Payers need access to patient information in order to work with providers to establish appropriate care plans for their members.
But there is hope
In the past healthcare has lagged other industries because of the exponentially greater complexity of analyzing the factors that contribute to human health. However, today a combination of vast data sets and innovative tools and services used to analyze them is making it increasingly possible to predict the actions that will deliver the best outcomes.
Propelled by cloud-based platforms and self-service business intelligence tools, healthcare organizations are increasingly adopting mature concepts in Machine Learning (ML) and Natural Language Processing (NLP) in their day-to-day operations, helping them uncover business-critical insights from oceans of structured and unstructured data. Sophisticated analytical models can make predictions, or generate recommendations based on patterns identified in the information gathered, thus allowing the organization to deliver services more efficiently. Additionally, artificial intelligence (AI) based systems are taking center stage in reducing administrative burden by providing cognitive decision-making capability previously dependent on human effort.
Getting started: Analytics as a service
The benefits of advanced analytics are no doubt profound in the healthcare world. However, such capabilities are not only expensive to develop and implement on a technical level; they also require expertise in data discovery and cleansing, training models, and how to interpret the output to gain meaningful and actionable insights.
The fastest way to get there is through partnership with a business process outsourcing (BPO) services provider who not only provides the analytical expertise but also intimately understands your processes and is capable of driving change within your organization. BPOs are uniquely positioned to provide analytics capabilities and strategic thinking far more effectively than home-grown solutions and much more affordably than other solution investments. Technology only implementation solutions are fraught with risk—from cost over-runs to poor adoption rates. BPO providers take a much more consultative, partner-oriented approach. They start by looking at the business goals and needs, and then develop a solution to meet it.
A forward-thinking BPO strategy considers all the new tools and technology, with a mindset to take an entire end-to-end process-driven approach with emerging metrics—not the typical piece-by-piece functional outsource placement approach. Ultimately, the goal is to develop a partnership which brings the talent, best practices and resources to the table. The key to building this successful relationship begins with the selection of a partner that shares the payer’s vision, and one that has a proven track record of successfully executing on that vision.
By selecting the right BPO partner, healthcare organizations can position themselves to gain a competitive advantage in the present, while positioning themselves and their consumers for an even brighter future. Acquiring the infrastructure, technology and brain trust needed to uncover insights from incomprehensibly large and continuously growing data sets is the industry’s next great challenge. Millions of lives and billions of dollars of revenue are at stake.