A Data Visualisation Approach in Understanding the Obstetrics Patients Conversion.

As the leading maternal and child hospital in Singapore, it is important to understand the current hospital standing and maintain our competitiveness by monitoring the population movement. Through the use of data visualization techniques, the team processed historical data from 2012 to 2020 and presented new data insights for the hospital management to identify potential areas for improvement to increase the delivery rate in the hospital.

Daily Hospital Emerging Infectious Disease(EID) Workflow & Data Processing Automation.

Following the onset of the covid pandemic two years ago, the Ministry of Health(MOH)'s required all health care cluster groups to provide daily reporting of the emergency department as well as the inpatient situation in the respective healthcare institutions for oversight of the covid situation. In view of the improvements in the data availability and relief of the tedious manual collation, DAO was entrusted with the task to help enable and setup the standardized report and generation process moving forward.

Re-Engineering Data Processing Workflow to Automate Abbreviations Audit of Clinical Documentation.

In KK Women's and Children's Hospital (KKH), clinical case notes audits are conducted quarterly for compliance of approved acronym usage. Existing process involves the retrieval of mixed hardcopy and electronic case notes for referencing manually to the list of approved abbreviations by clinical coder. Through the use of process re-engineering and excel application, audit coverage can thus be expanded with reduction in human dependency and errors with significant resultant savings in time spent.

Cluster analysis and visualisation of electronic health records data to identify undiagnosed patients with rare genetic diseases.

Rare genetic diseases affect 5-8% of the population but are often undiagnosed or misdiagnosed. Electronic health records (EHR) contain large amounts of data, which provide opportunities for analysing and mining. Data mining, in the form of cluster analysis and visualisation, was performed on a database containing deidentified health records of 1.28 million patients across 3 major hospitals in Singapore, in a bid to improve the diagnostic process for patients who are living with an undiagnosed rare disease, specifically focusing on Fabry Disease and Familial Hypercholesterolaemia (FH). On a baseline of 4 patients, we identified 2 additional patients with potential diagnosis of Fabry disease, suggesting a potential 50% increase in diagnosis. Similarly, we identified > 12,000 individuals who fulfil the clinical and laboratory criteria for FH but had not been diagnosed previously. This proof-of-concept study showed that it is possible to perform mining on EHR data albeit with some challenges and limitations.