It is well-known that healthcare providers are facing extreme pressures to reduce the cost of healthcare while also improving the quality of care for patients. Although a wide array of technological initiatives are being taken across the healthcare industry, “healthcare revenue cycle management”, which encompasses the process of creating, submitting, analyzing and ultimately paying for patient medical bills, remains a challenge and the focus area of improvement in 2016.
In this perspective, it is necessary to understand the underlying challenges with the healthcare revenue cycle management and how advanced analytical solutions can help the providers to overcome them.
Healthcare revenue cycle management activities from a provider’s perspective can be broadly categorized as shown below:
With the infusion of software systems like Electronic Health Records, Practice Management systems and claims management systems, the healthcare facilities have become warehouses for medical and billing related information stored in digital format. But, This abundance of information has not been leveraged to maximize business outputs because of the disparate manner in which data is available within the organization. Overcoming this challenge and integrating data sources into a single data lake is the first and the necessary step towards implementing analytics to provide data-driven solutions.
Transformation in the care delivery model from Fee-for-service to Value-based-care has had a negative impact on the Revenue Cycle for the providers. The cash flows are diminishing because of the increasing pressure to deliver more for less price. Value based payments have resulted in an increase in the at-risk payments. This change in reality makes it important to efficiently evaluate insurance eligibility, assess the likelihood to pay and reduce costs and time involved in the reimbursement of claims.
What can the solution possibly be?
Predictive analytics can help tackle the challenges of disparate data systems and diminishing operating margins by pinpointing areas of improvement so that the proactive efforts can be channelized in directions that will lead to an optimal revenue cycle for the organization.
Statistical modelling solutions that predict the probability of denial or underpayment for every claim being submitted can help separate out the risky claims from the ones that are highly likely to reimbursed completely. Accordingly more efforts can be channelized to dig deeper into the high risk claims to identify the issues with them. Predictive Analytics can also help identify the probable causes that flagged a particular claim as risky, providing with a priority list of areas to check and correct. This would greatly reduce the cost of repeated submissions of claims being denied or underpaid.
With the increase in healthcare costs, demand for self-payment option is on the rise. But, self-payments are problematic to the organizations because of the longer collection cycles and high chances of being written off as bad-debt. Analytics can be useful in this regard by evaluating each patient’s credit history to predict his or her propensity to pay. Such models can greatly help the healthcare providers to reduce the risk of non-payment and devise specific payment collection strategies for different patient segments based on the level of risk.
To sum it up, Healthcare provider essentially needs is a complete integration of data systems. Once that is achieved, Analytics can be deployed to generate insights to identify inefficiencies and gaps in the revenue cycle. Lastly, a predictive solution can take revenue optimization to a step further by identifying opportunities to minimize denials and underpayments