No-show-Medical-Appointments

Question(s) for Analysis

The following questions were addressed:

1. What is the age distribution of patients?
2. What is the distribution of patients by hospital location?
3. What is the distribution of patients by the day they booked their appointment
4. What is the distribution of patients by their appointment dates
5. What is the distribution of patients that showed up and did not show up for appointments based on the following factors: age, whether or not they are diabetic, gender, whether or not they are enrolled in the welfare program, whether or not they are hypertensive, hospital location, whether or not they take alcohol, whether or not they recieved sms notifications
6. Does waiting time between scheduling and appointment day predict whether a patient will come for appointment
7. What day of the week has the highest and lowest appointment show ups and the highest and lowest failed appointment show ups
8. Are there any patients who have both diabetes and hypertension? If yes, what is the age distribution of patients that showed up and those that did not show up for their appointment?Dataset Description

Observations and Conclusions

During my analysis, I made the following observations:

1. 25% of patients are less than 18 years, 50% are less than 37 years, 75% are less than 55 years, and the maximum age is 115.

2. Toddlers showed up the most for their appointment, followed by middle-aged patients, then the number of show ups declined afterwards as age increased from 58 upwards.

3. Number of patients that did not show up generally declined as age increased.

4. Generally, number of medical appointments reduced as age increased (from 59 upwards), and younger people have higher number of medical appointments.

5. Patients whose hospital is located in Jardim Camburi showed up the most to their appointment, followed by Patients whose hospital is located in Maria Ortiz.

6. Number of patients that showed up to their appointment reduced as waiting period increased. Therefore, a patient would most likely show up if the waiting period is short.

7. The highest number of appointment showups was observed among patients that had their appointments on a Wednesday, the lowest number of showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday.

8. The highest number of failed appointment showups was observed among patients that had their appointments on a Tuesday, the lowest number of failed showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday.

9. 3rd May, 2016 had the highest number of patients booking appointments

10. 6th June, 2016 was the highest booked date for appointment

11. Most of the patients had their hospital located in JARDIM CAMBURI and the least appointed hospital location was PARQUE INDUSTRIAL

12. Patients aged 0 had the highest number of appointments, while patients aged 99 had the lowest number of appointments

13. Appointment scheduling started 10th, November 2015 and ended 8thJune, 2016

14. Medical appointment started on 29th April 2016, and ended 8th June 2016

15. 5345 patients had both diabetes and hypertension, and showed up to their appointment

16. 1141 patients had both diabetes and hypertension, but did not show up to their appointment

17. There are more female than male patients

18. Majority of the patients are not enrolled in the welfare program, and did not recieve sms notification

19. Majority of the patients do not have diabetes and hypertension

20. Majority of the patients donot consume alcohol

21. Histogram charts of gender, diabetes, scholarship, hypertension, alcohol, sms is not sufficient to predict their influence on whether ot not patients showed up for their appointment

LIMITATION:

1. I could not analyze and make inferences on the handicap data because the meaning of each of its values were not clear.
2. The fact that some patients had multiple appointments, while others had fewer or just 1 appointment made it difficult to draw analyze and draw inferences on the distribution of patients.

References: https://www.shanelynn.ie/pandas-drop-delete-dataframe-rows-columns/ https://stackoverflow.com/questions/36786722/how-to-display-full-output-in-jupyter-not-only-last-result