fbpx

Using Cognitive Learning and Process Automation to reduce missed appointments & improve patient experience

Medical Doctors’ office managers spend considerable amount of time to schedule appointments and remind the patients about their appointments. In a managed care setup, office managers also need to keep a track of a patient that skip multiple appointments. The process gets more complicated in a group practice or independent physician associations where number of patients can go up to 100,000. Reminding and keeping a track of 100,000 patients requires a lot of manpower and is a high-risk situation. To solve this, we connected our web-based application (WebRem) with the patient information portal, CareTracker. This enabled WebRem to identify the appointments and other events associated with a patient. WebRem sends a text message and email address to the patient 12 hours before every event to remind and 6 hours after the event to collect feedback. This system also detects multiple missed appointments and sends notifications to the patient’s doctor and office staff. 

The system solves the following problems using systematic design thinking – 

  1. Saves time on phone calls of a doctors’ office manager whose reminding patients of their appointments. 
  2. Ensure quality by engaging with the patients after the visit.
  3. Mitigates risk by raising flags in case of a poor patient experience minimising lawsuits.

Application workflow

Use Case: Sending Appointment Reminders

  1. The time before an appointment, when the appointment reminder will be sent, will be set. This value can range between 12-36 hours. The functionality to send multiple reminders (at different times) for the same appointment is also provided. Similarly, the time after an appointment when the feedback webpage link is sent is also set.
  2. A CRON job (continuously running server sided command) runs on the Server to monitor any upcoming appointments using the CareTracker API (this is a one time instantiation server side process which will keep running until shut down). If a CRON job is undesirable, the following steps can also be carried out manually by logging into to the Admin panel of the web application some time before the appointment. 
  3. When an appointment is due based on the time duration(s) set in Step 1, the CareTracker API is called to get the patient’s name, contact information and appointment details (doctor name, appointment time and location).
  4. Using the information provided in Step 3, a text message / email is sent out notifying the patient. The functionality of choosing whether to send an email or a text message or both are  provided.
  5. Structure of feedback, reminder messages can be customised from the backend and personalised with webhooks like |*PATIENT NAME*| |*DOCTORNNAME*|

Use Case: Sending other event reminders

  1. The time before an event, when the event reminder will be sent, will be set. This value can range between 12-36 hours. The functionality to send multiple reminders (at different times) for the same event is provided. Similarly, the time after an event when the feedback webpage link is sent is also set.
  2. A CRON job (continuously running server sided command) will be run on the client Server to monitor any upcoming events.
  3. When an event is due based on the time duration(s) set in Step 1, the List of invitees for the event is  called. This list contains the emails of the people who have signed up to be notified about the events from the subscribe page or people who have agreed to sign up to receive notifications via the patient feedback form.

Using the information provided in Step 3, a text message/ email will be sent out notifying the patient. The functionality of choosing whether to send an email or a text message or both will be provided.

Ensuring HIPAA Compliance/ Data Protection

Patient data in the USA is protected by HIPAA Compliance Laws. Any violation can lead to losses in millions. WebRem is hosted on a HIPPA compliant Google Cloud server. All calls to the CareTracker API will be monitored and is secured by OAuth 2.0. At any given point, no patient information, obtained from the CareTracker API, is stored on the server. Accessing the web application will be through the means of an encrypted user ID and password. This will ensure that we know who is using the Web Application at any given time to perform any operation. This will in turn monitor all calls to the CareTracker API and other API’s (which will be used to send text messages and emails) All sensitive data stored in the database will use 32-bit encryption(hashing) techniques. 

User Compliance

For all use cases the end user will be  given an option to opt out of receiving notifications. This list is maintained on the server and before sending out any notification, the respective email id / phone number will be checked against this list to prevent any compliance issues.

Conclusion

WebRem is expected to save at least 1,200 hours per year for a medical practice of 100,000 patients. It will also expected to minimise legal issues, improve quality of care and increase patient engagement thereby aiding in improving overall brand value and trust.

Due to the open nature of WebRem, the system can be employed in any sector where reminders are essential to business practice. It can be extended to Sales, Hospitality, Research. Since, the system rests on a cloud, scalability is inbuilt. It can be used for 10 users or 10 million.