Physical Therapy Software for Patient Reactivation | Automation To AI
It has been reported that as many as 70% of physical therapy patients drop out before completing their treatment program, that is they self-discharge. Then, there are the patients who complete their plan of care and discharge.
Both segments of patients would clearly be valuable to win back for a practice. According to a study, a business has a 20 percent to 40 percent chance of getting back a past customer (or patient) versus a 5 percent to 20 percent chance of converting a new customer into a paying customer.
Reengaging former patients therefore should be a priority initiative ? to continue from where they left off on their care plan or to treat a new problem that has come up. It would come down to communicating with them in an effective manner. After all they do need to hear from the practice frequently so that when the need arises, they know where to go without having to once again search for a therapist or go back to their physician for a referral.
But therapists are busy treating patients and running their practices. They likely do not have the time to invest in communicating and engaging with past patients. Physical therapy software and automation would provide the answer.
Automation to Step Up Patient Reactivation
Automated physical therapy software platforms would provide the means to send relevant and appropriately timed messages to patients so that the practice remains top of mind, without being obtrusive. Every PT patient?s problem is likely to be unique and therefore personalization would be very helpful. These are several ways to deliver automated, personalized messages.- Preset Messages: Messages could be automated and rendered based on preconfigured message templates that are set up. These preformatted templates would use personalization tokens for adapting message content on the fly based on intelligent rules that could be defined in the platform. These rules could include ?variable? content that would hinge on which therapist was assigned to the patient in the system and what was the patient being treated for.For example, ?You may remember the six-week treatment plan that you were on. How are you doing today? How is the healing coming along?? The specifics about the treatment plan that the patient was on would come off the EMR integration.
- Frequent Communication: Messages would be sent to the patient from their therapist at predefined intervals after discharge. This could potentially provide the patient with secure access to their therapist even after getting discharged. These messages would appear to be personal and interactive messages coming from the therapist but in actuality are automated.
- Reacting to Responses: It is only when the patient responds to an automated message would the therapist need to step into the communication chain. At that point, the therapist could send a personal reply to the patient after they get notified that they have messages waiting for them.
- Ease of Use: It should be intuitive and accessible across mobile and desktop devices and enable quick and easy enrollment via text or email.
- EMR Integration: Through seamless EMR integration, it should offer access to patient records including home exercise routines, save documentation time, and raise patient insight.
- HIPAA Compliance: The platform needs to be secure and HIPAA compliant to ensure that PHI is always protected and access tightly controlled.
AI in Patient Reactivation
When we talk of automation today in healthcare, Artificial Intelligence (AI) typically comes up next on the agenda. In the future, AI or Machine Learning (to be more precise) should help improve patient reactivation in several ways, including:- Algorithmically determining the best time to reach patients and the most effective message content to use. The platform would learn from ongoing patient behavior what are their current communication preferences so as to continually adapt the message attributes for each recipient including send times and message text.
- Responding to patients with empathy and emotional appropriateness again by building these parameters into the machine learning model that algorithmically incorporates empathy to boost reactivation.