PALO IT

Prize - Internship for Undergraduate & Fresh Graduate in Singapore

1. Hospital throughput prediction

Context: Bottlenecks and increased waiting times at public health institutions is an ongoing issue in most communities.

Some patients have to wait up to 50 hours for beds - Longer waiting times at hospitals with some patients told to wait up to 50 hours for a bed - CNA (channelnewsasia.com)
The average waiting time for Emergency Departments (ED) varies from hospital to hospital but can be as high as 5 hours for non-critical cases https://www.moh.gov.sg/resources-statistics/healthcare-institution-statistics/waiting-time-for-admission-to-ward
Problem Statement:

How might we predict the average waiting time of a hospital based on the patient’s medical history and severity of conditions and simultaneously allocate the most at risk patients to the hospital with optimal waiting times. IE: We want to find a way to dynamically allocate a patient to a hospital that we have predicted will have the lowest waiting time based on historical data according to the patient’s symptoms and previous medical history. Highest risk to be served first
We will need to determine which hospitals are the most efficient
We will need to know which patients should be prioritized
We will need to train and test the model with additional simulated data based on the sample dataset
Datasets:

Sample Admissions Dataset – https://www.kaggle.com/datasets/ashishsahani/hospital-admissions-data
MOH Resources and Stats for mapping hospital admission times - https://www.moh.gov.sg/resources-statistics
Hint: Azure ML

2. Patient Disbursement to community healthcare network

Context:Bottlenecks at hospitals could potentially be reduced through preventative care

As part of Healthier SG there is a drive to connect and strengthen the relationship between local physicians (GPs) and the community with the idea that consistent and continuous healthcare at community level may reduce the occurrence of more severe illnesses – https://www.moh.gov.sg/news-highlights/details/promoting-overall-healthier-living-while-targeting-specific-sub-populations
Problem Statement:

How might we monitor at-home / community sourced vitals and use these insights to autonomously schedule GP visits and generate generic care plans. We can limit this to only diabetes patients at this stage
We will need to create a simple health form for input parameters (Geography, Time Preference, Diet, Medication Adherence etc.)
We will have to use a dataset of illness, diabetes in. this case, to predict onset or pre-diabetes
We will use the information above to schedule appointments with Doctors that suit location and time preferences
We will generate lifestyle and diet changes according to risk of onset diabetes
Datasets :

Diabetes prediction - https://www.kaggle.com/code/ahmetcankaraolan/diabetes-prediction-using-machine-learning/data
Hint: Azure ML, Power Apps