SAP Accelerator Tool For Large Financial Institution
At AI Australia, we hold the future in our hands. Machine learning methods are now our reality – one we can apply to any business to transform the day-to-day operations and use of resources. We worked with a large financial institution with worldwide locations. They needed a solution to manage the thousands of reports and queries received with Oracle Business Object SAP. The client wanted to migrate these reports from Oracle to a Microsoft solution, while also moving the Oracle Solution to a SQL Server solution.
CC4Skype A Winner With Leading Recycling Company
As proud supporters of recycling, AI Australia were overjoyed to work with the world leader in the field of reverse vending with their Return and Earn container deposit scheme in NSW. The start-up initiative involves placing kiosks around the state so the consumer can earn 10 cents per bottle or can when placed in the collection kiosks. Since starting the operation in November 2017, this recycle company has placed over 50 collection kiosks across the state. The initiative has been received with incredible popularity and success, collecting over 100,000,000 cans and bottles. As a fast-growing established global organisation, the company faced challenges with having no visibility and reporting on how their calls were being answered and managed.
Chronic Respiratory Diseases
Chronic Respiratory Diseases (CRDs) affects an estimate of over a quarter of the Australian population. CRDs can be present in variations across all age groups such as asthma, allergies, hayfever, and Chronic Obstructive Pulmonary Disease (COPD). These variations are attributable to factors including air quality, weather changes, patient health condition, patient lifestyle, and patient self-prevention and self-protection measures. A hospital in a NSW rural health district wanted a solution to analyse patient data to identify patients at-risk of developing CRDs and help doctors distinguish the factors related to respiratory issues. Development of CRDs in returning patients is a pre-existing problem that doctors struggle to predict, and results in a less efficient use of money and resources. The solution developed by our data superstars would assist doctors to identify patients at-risk of developing a CRDs and the factors that catalyse these.
Emergency Department Daily Visiting Volume Forecast
AI Australia lives and breathes data. It’s the correlations and the aggregations, which, combined with factors like the average stiletto height, can provide the most powerful insights out of everyday information. We turn our heels to the Emergency Department, where the patient visit volume on a good day can make you, and on a bad day will break you. It’s difficult to predict the changing demands of hospital resources when the visit volume can be impacted by the day of the year, holidays, weather conditions, local events, and every mosh pit gone wrong at Coachella.
Using Machine Learning To Predict Hospital Readmission
At AI Australia, we believe everyone needs their daily dose of predictive analytics, so we took our data skills and machine learning methods to a rural health district in NSW battling the high rates of unexpected hospital readmissions. Although doctors and clinicians review patient information to the best of their ability before discharge, there are often little to no signs that patients are of risk of readmission. This pre-existing problem costs enormous amounts of money and resources to manage. The health district needed a solution to reduce hospital readmissions by automatically detecting potential risks and identifying groups of patients with a high rate of readmission.