One of our goals at Wellpepper is to enable patients to self-manage and we know that if given the right tools, they will do so. While we strive for that exceptional patient experience, we also put a lot of effort into streamlining clinical workflow for onboarding and monitoring patients.
The process for inviting a patient is meant to be simple with a minimal impact to clinical workflow. Capturing basic patient demographics and assigning the appropriate care plan(s); a process that takes 30 seconds to complete. Typically, a scheduler, navigator or health coach will invite patients when they have been identified for an interactive care plan. Ideally, this is done prior to the patient coming into the clinic, but depending on the care plan, this can place at the front desk or in the patient’s room. EMR integration is another way Wellpepper can help streamline this process. You can read a bit more about some of our EMR Integration options in my post about deployment options (here).
Our professional services team will work with your clinical teams to understand all the scenarios where you want to know what your patients are doing when they’re not in the clinic. Using Alerts & Notifications and Machine Learning, we can help make sure that you’re focusing your time on the patients that need help.
Alerting & Notifications
Our sophisticated rules engine enables health systems to build out simple or complex alerting scenarios. These alerting scenarios will generate an alert and notify the care team.
Patients reporting a symptom or side effect is the most commonly used alerting scenario. Our analysis has found that in surgical scenarios, patients that report a symptom or side effect within 3 days after surgery are 3 times as likely to readmit within 30 days. By alerting the care team that a patient is experiencing a symptom or side effect, a care team member can take action and possibly prevent a readmission.
Other Alerting scenarios may include things like patients not doing their exercises, or reporting a blood sugar reading out of the target range.
One of the areas that we apply machine learning to help streamline clinical workflow is in our HIPAA-compliant messaging system, which allows communication between patients and their care team. Our analysis has shown that 98% of the messages that patients send are not urgent, and 70% of them don’t need a response. Our message classifier looks for the 2% that are urgent and escalates those to the care team.
It’s important to understand all of the points where patients may reach out for help and optimize workflow accordingly. This is another area where integrating with the EMR can help.
For more information on how to streamline clinical workflow while still providing a great patient experience, please email me at email@example.com.