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Evidence Based Revenue Cycle: Using Predictive Analytics in Healthcare

Given the current economic climate and continued scrutiny on the state of the healthcare industry, hospitals and physician practices must learn to adapt their business practices or continue to fall behind the curve in information technology and analytics.

Additional regulations, declining reimbursements, and the increase in patient out-of-pocket expenses are squeezing margins that were already razor thin. Healthcare executives are left to wonder, “How can I do more with less?”

In an effort to answer this question, healthcare financial executives are taking a page from their clinical counterparts. In the clinical field, the practice is called evidence-based medicine, which is “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients.”

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Healthcare financial executives are just now leveraging best practices from other industries and applying those to ensure the effective management of their operations. Those decisions include how to best leverage multiple data sources using analytics to determine where to apply and maximize their finite resources, with the end result being improved financial health of their organizations.

The use of predictive analytics is commonplace in other industries. Financial organizations, insurance companies and marketing firms rely heavily on consumer credit data and other third-party data assets to segment customers, limit risk and predict outcomes. Identifying account-level characteristics allows an organization to predict outcomes and appropriately allocate resources to customers that will generate the greatest return.

To date, the healthcare industry has been reticent to utilize consumer credit data for predictive analytics due, in part, to industry misconceptions about its effect on a patient’s credit. However, now more than ever, is the time to set the record straight.

Misconception #1: Pulling a credit report will negatively impact my patient’s credit score.
Fact: Credit reporting agencies are able to utilize a “soft inquiry” when a credit report is accessed by a healthcare organization. A soft inquiry does not affect a consumer’s credit score and the inquiry itself is only viewable by the consumer. The inquiry is merely a footprint detailing who viewed the consumer’s credit file.

Misconception #2: Patients are required to give consent each time a credit report is pulled.
Fact: A healthcare organization is not required to get a patient’s consent each time a credit report is pulled as long as it has a “permissible purpose” to access credit reports under the Fair Credit Reporting Act.

Misconception #3: Financially screening patients, using credit data, does not support the mission to treat the community as a whole.
Fact: Designing a comprehensive financial screening process will assist in identifying those patients with the means and ability to pay, ultimately increasing the financial health of the organization. In addition, the process helps to identity those who cannot meet their financial obligations. Organizations can then use this information to accurately identify the “true” charity care patients and provide greater benefits to the communities they serve.

Applying predictive analytics to your business processes will ultimately yield both quantitative and qualitative results. The quantitative results are easily measured by increased point-of-service collections, a reduction in bad debt and the cost to collect, all while maintaining or lowering operating expenses. The qualitative results come in the form of employee and patient satisfaction.

Information empowers employees to work smarter, not harder. Many times front-line employees are asked to use a one-size fits all approach to processing patient accounts because they have no insight into the patient’s ability to pay. Consumer credit data can provide employees with a clear indication of a patient’s capacity to pay, therefore allowing the accounts to be processed in a more efficient manner.

Moreover, predictive analytics allow hospital employees to prioritize actions with respect to open account balances by helping to identify the accounts with the highest likelihood of collections. The result is an increase in productivity, an improvement in financial results and satisfied employees.

Increases in patient satisfaction manifest themselves in two forms, patient experience and community benefit.

Healthcare organizations have been focused on delivering the highest quality care possible and rightfully so. But the patient experience does not end after the patient leaves the facility.

Clinicians have spent days and weeks building good will with the patient and the business office then spends months possibly destroying that good will with patient statements and phone calls for collections. Using credit data to drive predictive analytics allows a business office to tailor its message based on the individual patient.

Ultimately, this approach yields an increase in collections, while appropriately identifying and categorizing charity care accounts. An increase in charity care reduces bad debt expense and supports the mission of the organization.

In summary, the data itself is not a silver bullet, but utilizing data to build predictive analytics into your process will allow you to efficiently allocate your resources. The result will allow you to do more with less. In today’s environment, you can’t afford not to.

James C. Bohnsack is Vice President of TransUnion Healthcare.