How Predictive Analytics Sharpens Your Competitive Edge in Healthcare

August 19, 2019

The past decade has brought tremendous change to healthcare providers, health system financial leaders, hospital administrators and consumers. The shift from volume- to value-based care is driving healthcare organizations to operate more like traditional businesses to stay competitive.

 

Adding to the financial challenges for providers, patient payment responsibility has skyrocketed. In 2018 alone, out-of-pocket costs rose by 12 percent. Nationally, hospitals report more than $55 billion of bad debt expense attributable to self-pay accounts.

 

 

Harnessing the power of data

 

How can organizations stay solvent and gain a competitive advantage in this challenging market? While data analytics are an essential ingredient to remaining competitive, predictive analytics is key to achieving a competitive edge. For years, other consumer-oriented businesses have used data analytics to anticipate future needs and costs and to help make better decisions.   

 

The adoption of electronic health records in the medical industry has led to the collection of enormous amounts of data. Hidden in that data are patterns that reveal meaningful insights. Used effectively, these insights can have a significant impact on the bottom line.

 

More and more healthcare providers are harnessing the power of their data to predict patient utilization patterns, hospital readmission risk, staffing needs, and more. In fact, the Society of Actuaries (SOA) reports that 89 percent of healthcare payers and providers are using or plan to use predictive analytics in the next five years.

 

When it comes to healthcare revenue cycle performance, predictive analytics is a game changer. Tapping into the predictive power of data enables providers to forecast revenue, correct issues that negatively impact revenue, and create proactive strategies to improve collections.

 

Here are four ways healthcare organizations can boost their competitive edge with the insights provided by predictive analytics. 

 

  1. Higher collection rates – By leveraging actionable insights from their own patient pay data, providers have a more intelligent and cost-effective way to drive self-service collections. Predictive models can forecast a patient’s propensity to pay and even how much they are likely to pay. Patient payment analytics empower providers to initiate strategies based on individual consumer needs, such as point-of-service collections, financial counseling, identification of alternate insurance eligibility and multiple payment options.

     

  2. Improved operational efficiencies – Advanced data analytics can be used to pinpoint and correct revenue cycle inefficiencies. Real-time data simplifies financial staffs’ ability to find missing charges, identify diagnosis-related group (DRG) code errors, submit accurate claims, and collect outstanding payments. As a result, providers experience better cash flow and less administrative time and expense, while avoiding missing out on potential earnings.

     

  3. Better patient experience – As healthcare has shifted to value and patients bear a heavier financial responsibility, the industry is now more consumer-oriented. Satisfaction is paramount throughout the patient journey, from scheduling an appointment to receiving care to paying the medical bill. Predictive analytics reveal opportunities to deliver excellent customer service. By accurately understanding each patient’s needs, personalized payment strategies can be employed that boost financial engagement and patient experience. In the SOA survey, 42 percent of health providers and payers report higher patient satisfaction as a direct result of predictive analytics.

     

  4. More robust financial risk management – Healthcare organizations and providers are taking on increased financial risk as performance-based payment models become more prevalent. It takes much longer to determine patient outcomes and payments under these models. Providers are also facing lower reimbursements and larger outstanding patient balances. Healthcare organizations can mitigate these risks by using big data analytics to optimize cash flow. For example, based on historical data analysis, financial staff identify which outstanding accounts are most likely to be paid and focus their collection efforts on these.  

 

Predictive analytics is not just the future, it is the now of healthcare. When it comes to financial performance, meaningful data insights give healthcare organizations a competitive advantage by improving patient payment collections, advancing workflow efficiencies, enhancing patient satisfaction, and reducing financial risk.

HealthPay24 helps healthcare providers solve their revenue cycle challenges with predictive analytics and patient financial engagement. To learn more, contact us today.

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