ZAN Project Overview
Our project sponsor is a medical consultant working for Hospital X. He specialises in tending to younger patients from the age of 18 years old and below. He hopes to tap into the under-utilised administrative data that is collected by the hospital daily.
Patients are usually referred to Hospital X by other medical institutions or they booked an appointment directly. Currently, Hospital X experiences high no-show appointments rate of about 21% for first visits and 19% for review visits. Our project sponsor is keen on improving productivity for the doctors and psychologists as missed appointments lead to longer appointment lead times, idle time and overall lower quality of care.
Freeing up the time wasted by patients’ no-show would improve utilisation of slots, and even reduce appointment wait time for other patients.
Hospital X is a pioneer tertiary hospital that provides a comprehensive range of medical and rehabilitative services for children, adolescents, adults and the elderly. This project plans to make use of the dataset provided by our project sponsor to analyse if there is any relationship between the variables and to create a predictive model for likelihood of a patient in defaulting appointments.
The objectives of the project would be the following:
- Analysis of Hospital X's inpatients data
- To understand the data domains
- To understand the workflow of scheduling a patient’s consultation process
- To identify the contributing factors that lead patients to defaulting appointments
- To conduct what-if analyses to understand changes in appointment rates if the patient is referred to a medical professional nearer to them
- To evaluate the feasibility of creating a predictive model
- Recommendations based on findings
- To help stakeholders understand the analysis of the findings
- To consider the feasibility of a visual aid such as dashboard to aid in the stakeholders' future reference
The data is withheld until the NDA agreement is signed. The dataset that will be given to us is based on our project sponsor’s department inpatient records. The inpatient records are processed by the hospital staff working on the front desk.
As we have not obtained the data until the NDA is signed, we will only share our initial thought process of how we will tackle the project. We shall adopt closely to the Data Analytics Lifecycle approach.
Prior to obtaining the actual data, we have been researching on the project topic to familiarise ourselves with the field domain and to understand the perspectives of the various stakeholders. We identified doctors, hospital frontline staff, patients who defaulted their appointments, patients who are unable to book an appointment due to no slots and Hospital X as the relevant stakeholders in this project.
At this phase of the project, we will focus on understanding the given dataset and clean the data. Concurrently, we will decide on the analytical model and prepare the data accordingly.
While the project will revolve around the above objectives, our project sponsor is flexible to allow us to explore other possible relevant analytical tools or techniques that would enhance the findings.
- The dataset is limited to records from 2015 to 2016, which prevent any seasonal or yearly analysis
- The dataset only pertains to our project sponsor’s department, which administers only younger patients of ages 18 years and below.
