Among the most effective emerging tools for healthcare providers to streamline and optimize processes while maximizing revenue is predictive analytics. A majority (93%) of healthcare payers and providers believe predictive analytics is important for the future of their business, according to a 2017 Predictive Analytics in Healthcare Trend Forecast by the Society of Actuaries. Hospital revenue cycle management experts promote medical predictive analytics as the newest solution to translating massive loads of health data into actionable insight to improve financial performance.
We apply this data in ways that improve financial performance, similar to how other industries have been doing for decades. We can help leaders make smarter decisions. We can:
- Implement predictive analytics to enable you to improve revenue cycle processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks
- Use predictive models to figure out if a charge is missing; a patient will pay his or her part of the bill; a patient will be readmitted, or a diagnosis-related group code was assigned in error
- Predict not only if a patient is likely to pay, but also how much he or she is likely to pay
- Create self-pay predictive models that include a number of criteria, such as self-pay type, patient class (whether outpatient or inpatient), payment history, and debt history
- Analyze the company’s payment habits, provide the probability that the company will pay a trade account in a “severely delinquent” manner (90+ days beyond terms) within the next 12 months through the Dynamic Delinquency Score (DDS), and include comparisons to other companies within the same industry
- Answer even more
- Is there a deterioration in payment habits?
- Will they pay me on time?
- How much credit do suppliers usually extend to them?
- Identify the normal revenue-cycle processes, model them, and then pinpoint the cases that exhibit anomalies
- Use algorithms to examine historical data and make correlations
- Help you gain insight into how to more effectively invest time and efforts
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 allows 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.