Medical Appointment No-Shows with 110,527 Records and 14 Variables

110,527 medical appointment records with 14 associated variables, including patient demographics, appointment scheduling details, and health conditions. The dataset was contributed by Joni Hoppen and Aquarela Advanced Analytics. It is intended to help predict whether a patient will miss their scheduled appointment.

Use Cases

• Predict patient no-show likelihood based on demographic and appointment scheduling features.
• Analyze the correlation between receiving SMS reminders and appointment attendance.
• Study the relationship between socioeconomic factors, indicated by the scholarship variable, and patient no-show rates.
• Investigate patterns in no-shows across different neighborhoods where appointments take place.

Strengths

• Contains 110,527 individual appointment records, providing a substantial sample size for analysis.
• Includes 14 variables covering scheduling, demographics, health conditions, and socioeconomic status.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.
• Data may reflect geographic or temporal bias inherent to its original collection.

Provenance

Source

Joni Hoppen and Aquarela Advanced Analytics

Collection Method

Likely collected from a healthcare appointment scheduling system.