Across the world, today’s healthcare organizations are sharing one critical challenge: the rising costs associated with improvement in quality care. The underlying mandate is that all stakeholders must keep a sharp focus on making healthcare more effective, efficient, and affordable. To meet this new demand, both payers and providers have made adjustments to service delivery—and technology is increasingly playing a pivotal role.
How can payers and providers leverage technology to better collaborate? In a word: data. The amount of data attached to every patient has grown exponentially—and all of it is gathered, integrated, and interpreted according to compliance guidelines and processes that can vary widely between payers and providers. Additionally, the data sets held by payers and providers can be significantly different. For example, payers possess data on claims, financial analytics, and risk models. Providers have administrative and clinical data that includes case histories and outcomes. Providers need to leverage health plan data in order to move from episodic care to delivering outcomes-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. In the past, both stakeholders have attempted to bridge these data gaps through costly and time-consuming manual processes. The good news is that today’s more advanced analytics leverage data and improve collaboration of payers and providers—for enhanced experience and decreased costs.
While the healthcare industry has mastered data collection, the challenge is making it actionable. According to research, 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. And even the 20% of structured data presents enormous challenges in a value-based care world—with different data sets kept by payers and providers. Emerging cognitive capabilities increasingly address issues of unstructured data. And implementation of advanced analytics techniques and usage of new visualization tools provide the ability to pull information from disparate data warehouses while keeping data quality measures in place to make the data ready for analytics, with uniform and up-to-date information available across the organization.
Another area of data mining evolution is today’s predictive analytics models, which lean heavily on data and machine learning algorithms to project the likelihood of future outcomes. These predictive analytics can be used to predict more accurate payment and identify intake weaknesses and care to improve both healthcare delivery and patient experience. These models can 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 can reduce administrative burden by providing cognitive decision-making capabilities previously dependent on human effort.
Whether on the front end of patient treatment or downstream, at propensity-to-pay, these advanced capabilities bring many advantages, including:
- Poor communication and inaccurate data contribute to provider abrasion. Today’s CRMs, as a goldmine of patient, physician, and health plan data, serve as a common ground for both providers and payers—a source of accurate information that plays a major role in a more streamlined, efficient payment process.
- The preauthorization required to approve a procedure usually consumes significant time and effort for payers and physicians. The advantage of machine learning and natural language processing enables today’s enhanced image processing to dramatically reduce the number of incorrect approvals and decrease incorrect denials. This preserves high-tier resources and avoids costly redundancy of claims reprocessing.
- A more predictive, proactive analytics approach can reduce new medical record requests by singling out process breakdowns to identify error rates for each provider.
- Predictive data can find and address key inefficiencies in the operational management of healthcare business operations.
Today’s BPO organizations are applying innovation to the modern challenges facing healthcare, including data management and integration and predictive analytics. BPOs not only have claims data from provider groups, but also, payers. Armed with this intelligence, providers can positively affect a patient’s health outcomes, through PHM processes that also bend the cost curve.
BPOs can also bring the strategy and best practices gleaned from years of capturing, processing, transforming healthcare data from all states of the process–from pre-authorization and claims submission to customer care insights. By choosing the right BPO partner, healthcare organizations can align with tools and expertise to reduce abrasion and better navigate the changing ecosystem, while positioning themselves and their consumers for optimized engagement and outcomes.
This article was originally published on HGS Digital and is republished here with permission.