Predicting appointment cancellations
Patients who fail to show up for scheduled appointments or cancel at the last minute - do not give the health center the opportunity to fill the appointment slot, resulting in both a loss of time and money for the health center, as well as disruption of the timely care of the remaining patients. There are many reasons why patients miss their appointments. They may have forgotten it, have problems with transportation due to weather, or may not be able to leave work on time. It is therefore considered to be of major importance that both private doctors and health care units be able to predict the no-shows of their patients, so that they can replace the cancelled appointment in time and no loss of time and money is noted.
Examples: A private physician is interested in investigating the profile of individuals as well as the reasons they are more likely to cancel an appointment.
The problem
Patients who fail to show up for scheduled appointments or cancel at the last minute - do not give the health center the opportunity to fill the appointment slot, resulting in both a loss of time and money for the health center, as well as disruption of the timely care of the remaining patients. There are many reasons why patients miss their appointments. They may have forgotten it, have problems with transportation due to weather, or may not be able to leave work on time. It is therefore considered to be of major importance that both private doctors and health care units be able to predict the no-shows of their patients, so that they can replace the cancelled appointment in time and no loss of time and money is noted. For example, a private physician is interested in investigating the profile of individuals as well as the reasons they are more likely to cancel an appointment.
The purpose
Identifying the profile of patients who tend to cancel appointments with their doctor.
The solution
By using machine learning and artificial intelligence models based on historically recorded and confirmed no-show data, the profile of patients who tend to cancel their doctor's appointments will be identified, making it easier to predict non-appearance of a patient in the future.
The benefit
Both private doctors and health care units will be able to predict patients who may fail to show up for their scheduled appointment resulting to save time and money.
Indicative presentation of the data needed
- The first column must necessarily contain the information about whether the appointment took place or not, according to the retrospective data (1: Yes, 0: No).
- The remaining columns can indicatively contain the following elements:
- Socio-demographic data.
- Medical history.
- Area of residence and distance from health care facility.
- Days that elapsed between the day of the phone call to make the appointment and the day the appointment was made.
Table 1. Indicative table of input data from the application user
|
Realized Appointment (1: Yes, 0: No) |
Age |
Gender |
Days (Phone call until appointment) |
|
0 |
58 |
Male |
30 |
|
0 |
45 |
Female |
15 |
|
1 |
65 |
Female |
10 |
|
1 |
72 |
Female |
7 |
|
1 |
87 |
Male |
60 |
|
0 |
34 |
Male |
8 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The days between the day of the phone call to make the appointment and the day the appointment was made should be calculated based on the date of the phone call and the date of the appointment.
Output:
After the data has been entered by the user and after a short period for the automated analysis to be completed, a report of the results and the statistical methods used is extracted from the system.
Note: For any clarification you need regarding the content of the use case or any information related to the collection or validity of your data please contact us.
