Machine learning system and method for attendance risk mitigation

A computer-implemented method of increasing appointment attendance, comprising:

• providing at least one processor;
• and at least one non-transitory memory including computer program code for one or more programs, the at least one non-transitory memory and the computer program code configured to, with the at least one processor, perform steps comprising:
• obtaining a set of electronic medical record (EMR) data related to an attendee in at least two different data formats;
• standardizing the at least two different data formats of the set of EMR data;
• obtaining a set of past appointment data structures, each comprising an attendee identity, a past appointment, and an attendance status of the past appointment;
• obtaining a set of external data related to the attendees in the set of past appointment data structures;
• obtaining a set of regional environmental data related to a region around a location of the appointment or the attendees in the set of past appointment data structures from a time of each past appointment;
• training a machine learning algorithm with the standardized EMR data, the set of past appointment data structures, the set of external data, and the set of environmental data;
• at a first time:
• obtaining a pending appointment data structure comprising the attendee and a corresponding appointment for the attendee;
• obtaining a set of appointment data related to the appointment;
• obtaining environmental data related to the region around the location of the appointment or the attendee in the pending appointment data structure at the first time;
• inferring, using the trained machine learning algorithm, a probability that the attendee will attend the appointment;
• and
• when the probability of attendance is below a threshold, performing a first mitigation step selected from a first group of mitigation steps to increase the probability that the attendee will attend the appointment;
• at a second time after the first time:
• obtaining the pending appointment data structure comprising the attendee and the corresponding appointment for the attendee;
• obtaining a set of appointment data related to the appointment;
• obtaining second environmental data related to the region around the location of the appointment or the attendee in the pending appointment data structure at the second time;
• inferring, using the trained machine learning algorithm, a second probability that the attendee will attend the appointment;
• and
• when the second probability of attendance is below a second threshold, performing a second mitigation step selected from a second group of mitigation steps different from the first group of mitigation steps, to increase the probability that the attendee will attend the appointment.

A system for increasing appointment attendance, comprising:

• a non-transitory computer-readable storage medium with instructions stored thereon, which when executed by a processor, perform steps comprising:
• obtaining an appointment data structure, comprising an attendee and a corresponding appointment for the attendee;
• obtaining a set of electronic medical record (EMR) data in at least two different formats related to the attendee;
• standardizing the at least two different formats of the set of EMR data;
• at a first time:
• obtaining a first set of data related to the appointment;
• obtaining a first set of external data;
• obtaining first environmental data related to the region around a location of the appointment or the attendee at the first time;
• inferring, using the standardized EMR data, the first set of data related to the appointment, the first set of external data, and the first environmental data in a machine learning algorithm, a first probability that the attendee will attend the appointment;
• and
• when the first probability of attendance is below a first threshold, recommending a first mitigation step selected from a first group of mitigation steps to increase the probability that the attendee will attend the appointment at a second time after the first time:
• obtaining a second set of data related to the appointment;
• obtaining a second set of external data;
• obtaining second environmental data related to the region around a location of the appointment or the attendee at the second time;
• inferring, using the standardized EMR data, the second set of data related to the appointment, the second set of external data, and the second environmental data in the machine learning algorithm, a second probability that the attendee will attend the appointment;
• and
• when the second probability of attendance is below a second threshold, recommending a second mitigation step selected from a second group of mitigation steps different from the first group of mitigation steps to increase the probability that the attendee will attend the appointment.

A non-transitory computer-readable storage medium having stored thereon one or more program instructions which, when executed by one or more processors, cause an apparatus to at least perform the following operations:

• obtain an appointment data structure, comprising an attendee and a corresponding appointment for the attendee;
• obtain a set of electronic medical record (EMR) data related to the attendee, wherein the set of EMR data is in at least two different formats;
• standardize the at least two different formats of the set of EMR data;
• at a first time:
• obtain a first set of population data related to the attendee;
• obtain a first set of appointment data related to the appointment;
• obtain a first set of external data;
• obtain first environmental data related to the region around a location of the appointment or the attendee at first time;
• infer, using the standardized EMR data, the first set of population data, the first set of appointment data, the first set of external data, and the first environmental data in a machine learning algorithm, a first probability that the attendee will attend the appointment;
• and
• when the first probability of attendance is below a first threshold, recommending a first mitigation step selected from a first group of mitigation steps to increase the probability that the attendee will attend the appointment, at a second time after the first time:
• obtain a second set of population data related to the attendee;
• obtain a second set of appointment data related to the appointment;
• obtain a second set of external data;
• obtain second environmental data related to the region around a location of the appointment or the attendee at the second time;
• infer, using the standardized EMR data, the second set of population data, the second set of appointment data, the second set of external data, and the second environmental data in the machine learning algorithm, a second probability that the attendee will attend the appointment;
• and
• when the second probability of attendance is below a second threshold, recommending a second mitigation step selected from a second group of mitigation steps different from the first group of mitigation steps to increase the probability that the attendee will attend the appointment.