Medical Appointment No-Shows Analysis Dashboard

Data Resource: https://www.kaggle.com/datasets/joniarroba/noshowappointments

Tools & Technologies:

Python, Pandas, Plotly, Dash, Flask, HTML, CSS.

Key Questions Explored

1. What is the delay between appointment request and actual appointment, and how does it contribute to no-shows?
2. Which age segments have the highest no-shows, and what are the gender dynamics within these groups?
3. What percentage does each age segment contribute to overall no-shows?
4. Which neighborhoods have the highest no-show rates
5. How scholarship affect no-shows?
6. What is the contribution of different medical conditions to no-show behavior?
7. What percentage of patients fail to attend despite receiving SMS reminders? and what percentage could have been rescued by an SMS?
8. Are no-shows more common among first-time patients or follow-up appointments?

Insights

• Communication (SMS):

  • Less than 50% of no-shows reported receiving an SMS reminder, suggesting a major gap in outreach.

• Neighborhoods:

  • The highest absence rates are concentrated in ITARARÉ and SANTA CECÍLIA.

• Age & Demographics:

  • Young adults and middle-aged patients account for 55%+ of all no-shows.
  • Around 60% of patients in these groups face delays of 10+ days between booking and appointment, contributing significantly to absences.

• Hypertension:

  • Hypertension is strongly correlated with absences, responsible for an average of 60% of no-shows, particularly among patients older than teenagers.
  • Around 70% of no-shows are from follow-up patients, suggesting dissatisfaction or scheduling fatigue rather than first-time disengagement.
  • Highest percentages (+75%) of absent and hypertensive customers are in ENSIADA DE SUA, SANTA HELENA, ARIOVALDO, VILAROBIM, and ILHA DE SANTA MARIA

• Accessibility:

  • More than 40% of handicapped patients are lost in certain neighborhoods due to accessibility issues.
  • These certain neighbourhoods include: SÃO BENEDITO, SANTA LUCIA, and JUCUTIQUARA

Recommendations

• For SMS Communication:

  • Increase SMS coverage and reminders, as improved outreach could reduce absences by up to 50%.
  • Explore alternative reminders (e.g., WhatsApp, automated calls) for segments with low SMS response rates.

• For Young & Middle-Aged Patients:

  • Offer appointment slots outside of traditional work hours.
  • Reduce delays between booking and appointment to under 10 days wherever possible.

• For Hypertensive Patients:

  • Prioritize hypertensive patients in scheduling to reduce delays.
  • Reduce wait time.
  • Simplify appointment registration and minimize unnecessary tests.
  • Ensure effective and empathetic patient-provider communication.

• For Handicapped Patients:

  • Invest in accessible, handicap-friendly facilities, especially in high-risk neighborhoods (e.g., SÃO BENEDITO, SANTA LUCIA, JUCUTIQUARA).

Conclusion

No-shows are concentrated among young and middle-aged hypertensive patients in specific neighborhoods and are strongly influenced by appointment delays and communication gaps. By addressing scheduling delays, enhancing accessibility, and improving reminder systems, the organization can significantly reduce no-show rates, improve patient care, optimize resource utilization, and ensure effective patient-provider communication.

Live Dashboard link:

https://momourad.pythonanywhere.com/