Using data to determine the types of patients likely to miss their hospital appointments
Data storytelling using Tableau
Hospitals usually require that patients make appointments when they want to see a doctor or specialist. However, we sometimes have situations where patients miss their appointments their appointments and do not give reasons.
Suppose we want to identify regular ‘no-show-ers’, based on recorded information, can we determine the characteristics of patients who miss appointments. Thereby suggesting alternatives for such patients and ensure that doctors do not waste time waiting for them.
In this article we will use the Missed Appointments dataset from Kaggle. It contains information about 100K medical appointments made by patients in Brazil, their characteristics and appointment information, and whether they attended the appointment or not (a no-show). A description of the features is given below:
So, let us dive in….
Did we have more patients missing appointments?
We can see that we had fewer patients missing their appointments, approximately 22 319 of them out of a 100K appointments.
Our interest is in the patients who missed their appointments hence for the rest of this task we will filter the data and concentrate on those who did not show up for the appointments.
Did they receive SMS’s?
Out of the 22 319 ‘no-show-ers’, less than 50% of them actually received SMS’s for the appointment. That could be the reason why they did not show up. Most probably they forgot like we all do.
Which gender makes up most of our ‘no-show-ers’?
Our ‘no-show-ers’ seem to be mostly women, approximately 65% of the ‘no-showers’.
Average age of ‘no-show-ers’?
Most of the patients who missed appointments were around the age of 35 years.
In which month did we record more ‘no-show-ers’?
The month of May seems to be a favorite among our ‘no-show-ers’ (something worth investigating). 16 804 missed appointments in the month of May alone.
In which neighborhood were most of the missed appointments recorded?
The neighborhood of Jardim Camburi recorded 1 465 missed appointments, the highest compared to other neighborhoods. Could there be a reason why?🤔 .
Are our ‘no-show-ers’ handicapped? Do they have a government scholarship?
It is quite evident that just a few of our ‘no-show-ers’ are handicapped. Hence that could not have prevented them from attending the appointment. In addition, just a few of the ‘no-show-ers’ are on the welfare scholarship? A plausible reason could be that they did not find money to pay for the appointment.
Are our ‘no-show-ers’ ill?
Clearly some but not most of ‘no-show-ers’ suffer from Hypertension, Diabetes and Alcoholism. These make up just small fractions of those who missed appointments. These illnesses could not have prevented them from showing up for the appointments.
Conclusions
The characteristics of patients who miss appointments are:
1. Mostly female patients with an average age of 35 years.
2. Patients who seemingly miss appointments particularly in the month of May.
3. Patients who visit the hospital in Jardim Camburi.
4. Patients who are not mostly handicapped.
5. Patients who mostly do not have a welfare scholarship.
6. Patients who are not mostly hypertensive or diabetic or alcoholics.
Now we definitely have an idea of the kind of patients who miss their appointments. Hopefully this information can help hospitals to suggest alternatives for this group of people.
Link to visual story is here .
