Analytics Remains A Significant Challenge in Healthcare

Analytics Remains a Significant Challenge in Healthcare

As per a healthcare consulting firm Kaufman Hall, 96 percent of healthcare chief financial officers (CFOs) think that their companies need to do more to grasp financial and operational data analytics, and 94 percent stated they had experienced increased requirement to have more insight into how financial results influence business strategy. In fact, determination among the country’s healthcare institution CFOs is low, with just 13 percent saying their business is all set to handle current healthcare payment and presentation models with existing financial preparation tools.

There is no dispute that the healthcare industry has comprehended the collection of data. The difficulty is making it actionable and readily available to its customers. About 80 percent of healthcare information is unstructured, presenting it remarkably challenging to apply fronting business or clinical challenges, involving, population health management, countering deception, waste and mismanagement, and other administrative and financial matters. Further, even the 20 percent that is structured brings forth enormous difficulties in a value-based care world. For instance, the datasets held by patients and providers can be distinctive. Payers control data on claims, compensation, and risk methodologies. Providers have regulatory and clinical data that involves case histories and consequences.

Proportionately data set is essential, but in isolation does not provide a complete and contextual perspective of the customer. Providers require to leverage payer data to shift from episodic care to producing outcome-based care beyond the care continuum. Payers need to look into the patient information in succession to work with providers to stabilize appropriate care plans for their constituents.

The advantages of advanced analytics are, without doubt, intense in the healthcare world. However, such abilities are not only expensive to produce and execute on a technical level; they also need expertise in data discovery and cleansing, training standards, and how to interpret the production to gain meaningful and actionable insights.

The most expeditious way to get there is via a 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 affordable than other solution investments. Technology only implementation solutions are fraught with risk—from cost over-runs to reduced 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.

Boosted by cloud-based platforms and self-service business intelligence tools, healthcare establishments 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.