Healthcare financial management through business intelligence tools

Analytics may make it easier for healthcare organizations to diagnose the most troublesome aspects of the revenue cycle management process and avoid claims denials.

Despite all the reforms and adjustments to healthcare payment models, the top post-patient care priority for health...

organizations is still maintaining the claim flow and ensuring that the revenue streams have as little interruption as possible. Revenue and claims management play important roles in the financial stability of healthcare organizations.

Many groups have healthcare financial management problems involving claim denials and reimbursement compliance. New healthcare business intelligence and analytics tools are enabling many of these facilities to easily reach new levels of actionable insights into payment data with ease and use that information to add tremendous value to their organizations.

The focus of a revenue cycle management (RCM) system is used to help healthcare organizations reduce denied claims and prevent delayed or lowered reimbursement payments. An RCM system alone can't do all the work. Its findings must be observed by an employee or department within a healthcare organization, who then must take preventive measures to stabilize or reduce the rate of denials and oversee the overall financial health of the overall healthcare system.

Many of the focus areas of RCM can greatly benefit from analytics tools. Finance is also a space where business intelligence tools can be used to mine through existing claims data for meaningful insights. The following list expands upon a few areas where analytics tools can be deployed in combination with RCM.

How analytics fits into healthcare financial management

Predictive models: A feature in analytics systems allows healthcare organizations to forecast certain aspects of the healthcare financial management process. By leveraging existing and historical data and applying predictive models, health groups can foresee and lay out future reimbursement payments, patient volume and resource utilization. Predictive models are used today in various industries, ranging from weather forecasting to anticipating how many of a company's products will be sold during a specific period of time. These examples highlight how organizations can use analytical capabilities to envision and control the long-term financial wellbeing of their businesses.

Analysis of current denial trends: The most significant goals of claims management are that claims always go out on time and have the lowest possible rate of denial. Any delays in payments mean reduced revenues, something that could affect the entire organization. In the past, managing and reviewing claims denials required reviewing a collection of complex and overwhelming reports and involved a lot of manual interventions to identify trends and causes of denials. The introduction of analytics tools to the claims allows staff to more easily analyze denials patterns and identify areas where they can improve.

Contract compliance and analysis: On top of helping reduce denial rates and providing better visibility of claims revenue, analytics tools are capable of detecting health plan providers that are not reimbursing a healthcare group according to agreed-upon contractual fees. This gives providers security in knowing they are receiving the appropriate reimbursements and eliminates the possibility of being underpaid.

Self-service capabilities: While the claims processing and collections capabilities of analytics platforms are advantageous, many of today's users are attracted to these products because of how easy they are to operate. Self-service empowers back office staff members to build their own reports and perform analysis without needing deep technical knowledge or having to hire an outside consultant.

Real-time insights: In addition to all the previously listed capabilities, some of today's analytics platforms can quickly deliver meaningful insights and key performance indicators to end users. Individuals responsible for claims management can log onto an interactive dashboard through their mobile devices or desktop computers and interact with the findings of their analytics systems.

Whether a hospital is using machine learning to predict denials or reimbursement levels or deploying self-service business intelligence to empower its billing staff, many of today's analytics tools are proving to be valuable in the healthcare financial management arena. As with any new product rollout, an organization must pre-assess and plan the deployment of a business intelligence product to ensure it will be helpful and well-received by employees.

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