There’s no such thing as a “one size fits all” patient engagement strategy. Health system executives should beware – using conventional propensity to pay scoring tools to predict how patients will engage with and pay their healthcare bills is not an effective strategy.
More patients are displaying consumer behaviors in how they seek, access and pay for healthcare. However, paying for healthcare is fundamentally different than purchasing other high-ticket consumer goods.
- Nearly 1 in 3 patients has deferred care due to cost concerns
- 15% have shopped around for a lower-cost provider after receiving a price estimate
Conventional propensity to pay scoring is a poor fit for healthcare:
1. Unlike a car or house, healthcare isn’t always planned for – emergencies happen.
Patients often do not have the luxury to budget for treatment, especially if it’s an urgent, medically-necessary procedure.
2. People budget for healthcare expenses differently, even when they’ve planned to receive care.
Patients often plan and budget for their care based on various factors, including insurance coverage, their remaining annual deductible, out-of-pocket maximum, available HSA or FSA funds and more.
3. If faced with financial hardship, healthcare bills are one of the first expenses consumers defer.
Instead, consumers prioritize expenses such as groceries, car payments and rent or mortgage payments over healthcare costs.
Therefore, traditional consumer scoring tools will always fall short at modeling patient behavior. Patients don’t pay for healthcare expenses the same way they would pay for a car or a house. So, why use scoring models that are based on credit scores, which analyze consumer payment history for expenses like rent, mortgage, utility bills and car loans?
What healthcare leaders should look for instead:
1. Propensity to pay scoring models that use healthcare data.
To accurately predict how patients will engage with and pay their healthcare bills requires healthcare data. Data from HL7 messages, 835 files, and clinical records can be combined to predict payment outcomes. However, conventional scoring methods underutilize these sources of data.
Details such as the patient’s length of stay, discharge disposition, complications and comorbidities, insurance payor activity and their previous relationship with healthcare provider will inform a truly optimal and personalized engagement strategy. For example, insurance activity may reveal that the patient has not met their deductible yet, so they may need more flexible payment options than they would normally.
2. Resources to collect, consolidate and prepare the data.
Outdated and siloed data is useless. That’s why health systems need a dedicated partner or resource to make the data truly actionable. This involves bringing together multiple data sets (from health insurers and providers), along with census and demographic data to match patients to their most up-to-date information. For large health systems with multiple EHR systems, the ability to consolidate data from the different EHR platforms is crucial too.
3. Truly powerful propensity to pay scoring models require patient engagement data.
When collecting data into a consolidated data warehouse, do not forget the most important data source of all. The best predictor of how a patient will engage is how they engaged with patient engagement software in the past. By combining patient engagement data collected during scheduling, pre-service, and point-of-service with the other healthcare data sources we mentioned earlier (insurance, provider, demographic and census data), health systems can predict outcomes much more accurately than if they relied only on a single source of information. This allows the health system to tailor interactions with each patient individually.
Consider this use case:
A partner that can bring together multiple sources of healthcare data, along with patient engagement data can determine that a patient has a high deductible health plan and just received a large bill for their last hospital visit, but has not paid it. However, they have paid for prior bills from their primary care physician. These insights inform a more personalized messaging approach, where that patient may be offered a more flexible, monthly payment arrangement.
How else can health systems make the most of healthcare-based propensity scoring models?
Identify and reduce no shows.
Scoring models also have the potential to reduce no-shows by predicting which patients are not going to show up for their appointment by looking at whether they’re engaging with appointment reminders.
Optimize cost-to-collect and focus the efforts of your EBO.
With data on when, how, how fast, and how much of their medical bill a patient typically pays, these insights inform which patients should receive automated follow-ups or a proactive staff call, as well as who is not worth the staff call to begin with.
Power frictionless interactions at every stage of a patient’s healthcare journey.
Health systems can apply propensity scoring models to various settings to make every patient interaction easy and seamless. For example, even before scheduling, propensity scoring can help optimize referral management and scheduling. By understanding a patient’s preferences and behaviors, your health system can present an upfront price estimate through the patient’s preferred communication channel and offer personalized payment options. This helps foster patient loyalty and minimizes the risk that the patient will shop around for a provider with a lower cost.
Proof that a Scoring Model is a Perfect Fit for Healthcare
With so many propensity to pay tools on the market today, what else should healthcare leaders look for before making an investment?
1. A propensity to pay tool proven to outperform traditional scoring models.
Patientco has compared our propensity scoring model to others in the market. Our scoring model was up to 25% more accurate than industry-standard credit-based scores. We also recently used our scoring model to predict payment rates for one of our largest health system clients and our predictions were accurate over 91% of the time.
2. A solution with strong patient Net Promoter Scores.
Patientco uses Net Promoter Scores (NPS) to determine how likely a patient is to refer their healthcare provider to others based on their billing and payment experience. A higher NPS score means the patient is more likely to refer their friends and family to their provider. For clients using Patientco’s full solution, the average patient NPS score is 59, which is considered excellent. This is more than twice as high as the industry average patient NPS, which is 27. Equally important, our average NPS continues to increase each year. Our patient NPS has risen nearly 5% in less than two years. This shows that our scoring model works – we’re making the patient financial experience better with each interaction.
3. A partner with credibility in the healthcare space.
Choose a propensity solution from an organization that is backed by solid industry recognition and referenceable clients, like Patientco. We’re HFMA Peer Reviewed and received the 2021 Best in KLAS award for patient financial engagement platforms, both of which are based on feedback from our health system clients.
Want to learn more about how propensity to pay scoring models power our Best in KLAS engagement platform? Schedule a brief consultation with one of Patientco’s payment specialists to get your questions answered.