Patient propensity to pay automates engagement attempts based on demographics, past experiences and patient preferences. Our program tracks time of day, payment options and communication channel preferences. Propensity-to-pay targeting increases the chance of successful payment.

For example, Patient A may be best reached in the evening and prefers text messages. Patient B doesn’t want phone calls and is only available on weekends. Millennia Recover uses this data to deliver customized approaches.

Patient engagement modeling in Millennia Recover works with machine-learning propensity to pay to analyze behaviors and trends. This data identifies the best engagement solutions for each patient, so providers can target patients differently to encourage payment.

For example, Patient A pays their bill faster with a text reminder. Patient B does not need a reminder to pay on time. Targeted engagement modeling gives each patient what they need to pay successfully.

Speech artificial intelligence (AI) provides insights into patient satisfaction by analyzing patient interactions. A machine listens to phone calls between your staff and patients for:

• Quality assurance automation to ensure providers are supporting patients.
• Satisfaction insights to identify engagement levels and trends over time.
• Trigger identification to recognize trigger words, such as “missing payment.”

Speech AI generates a report card for callers to automate quality assurance efforts. Providers can read and listen to recordings of patient interactions.