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muneebswabi003/AnalystLab-Africa-Week-4-HealthConnect

Domain:

healthcare

Record type:

dataset
Creator:
mun
Host:
HealthConnect appointment analysis project , AnalystLab Africa Week 4 # AnalystLab Africa – Week 4 HealthConnect ## Project Overview This project is part of the **AnalystLab Africa Experience Lab Internship Programme – Week 4**. The project focuses on **HealthConnect**, a healthcare appointment analysis case study. The main objective is to understand appointment attendance patterns, identify factors that may contribute to missed appointments, and explore opportunities to improve patient support. ## Project Objective The analysis is designed to help HealthConnect: * Understand appointment attendance patterns. * Identify factors associated with missed appointments. * Monitor important appointment performance indicators. * Identify patient groups or appointment situations that may require additional support. * Support data-driven decisions aimed at reducing missed appointments. ## Dataset Overview The HealthConnect appointment dataset contains: * **5,000 appointment records** * **18 variables** * **1,696 unique patients** * Primary identifier: `appointment_id` The dataset includes information related to patient characteristics, appointment scheduling, previous appointment history, reminders, distance to the clinic, waiting time, and appointment outcomes. ## Data Quality An initial data quality assessment identified: * **0 duplicate rows** * **0 duplicate appointment IDs** * **90 missing values** in `distance_to_clinic_km` * **60 missing values** in `waiting_time_minutes` * **1,366 missing values** in `reminder_channel` * **0 missing values** in `appointment_outcome` The missing values in `reminder_channel` appear to be related to appointments where no reminder was sent and therefore require contextual interpretation rather than being automatically treated as errors. ## Key Business Questions The project focuses on questions such as: 1. What percentage of scheduled appointments are missed by patients? 2. Which factors are most strongly associated with missed appointments? 3. Does the time between booking and the appointme …

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