To ensure timely reimbursement is captured for services and patient satisfaction during collection efforts remains as high as possible, healthcare organizations have explored solutions that indicate the likelihood of patient payments using a blend of data. The application of a program such as this can have numerous benefits—from reducing bad debt and improving collections to eliminating needless follow-up. Hence, vendors offering propensity-to-pay scoring have witnessed a growth in the number of providers interested in their services. To help guide healthcare organizations, we will review some of the best practices and essential qualities of these resources in addition to traits that may be prudent to avoid.
Regular and Fundamental Features
While no two propensity-to-pay scoring systems are identical, healthcare providers examining the rewards these initiatives offer might benefit from understanding some of the recurring traits in these solutions. One of the principles that consistently emerges is the analysis of information from multiple sources. Probing several pieces of data—both proprietary and public—may lead to the most effective score available, much like measuring twice and cutting once. Integrating tax returns, check stubs, bank statements, and credit scores, among additional points, can trend towards the most accurate appraisal of patients.
However, providers using a system such as this might also benefit from dedicating and developing their own teams to work closely with the solution (audit, leadership, etc.). These teams cover regular audits reviewing information accuracy, the ability to segment patients into five categories (“one” indicating most likely to pay and “five” being least likely), advanced address verification, in addition to many more. Designating a group of staff to monitor the scoring model may prove advantageous. Beyond assessing data that’s provided, evaluating vendor performance, and employing these initiatives on both the back- and front-end, flexibility in follow-up activities and instituting the best fit for your organization are crucial. The healthcare market experiences many changes—with patient payment patterns affected—and providers can benefit from identifying optimum strategies.
Avoiding Certain Attributes
Just as important as establishing key features within a propensity-to-pay scoring system, determining what could be detrimental may be just as instrumental. Healthcare organizations want to circumvent a “one-size-fits-all” solution or in regards to self-pay accounts, as these processes can lead to premature outsourcing and unnecessary vendor fees. Additionally, limited options for settling balances (e.g., minimum payments or rigid payment plans) could also affect efficiency and diminish outcomes. Not all patients share the same financial predicaments and, therefore, should be viewed individually.
Much like restricted choices, confining the amount of analytics examined when constructing a model might inhibit a solution’s success. Some providers solely use credit scores and exclude patients without credit who could have qualified for charity care, or they avoided credit scoring entirely, resulting in incomplete predictions. The final trend—and oftentimes the most influential—is unneeded patient contact. The purpose of these scoring systems is to deliver the probability of payments and reveal the level of follow-up needed. Sending redundant requests to patients with a high likelihood of settlement could lead to complaints or impact the payment itself.
Final Thoughts
While none of these tactics may necessarily ensure a successful propensity-to-pay scoring system or guarantee that payments will increase, utilizing a solution that integrates several strategies might prove beneficial and provide insight into one of the organization’s most crucial sources of reimbursement—the patient. Having a full picture of a patient’s financial situation through various sources, assessing selected vendors for performance while also avoiding a uniform mentality for outsourcing all self-pay accounts, excluding “one-size-fits-all” solutions, and preventing needless patient contact may be helpful considerations when examining the integration of a scoring model. But, much like one of the most important suggestions for an initiative such as this, it is on the individual leader or leadership teams to identify the best path for each organization.
