Get fresh rosters in three easy steps
Our Rostering Solution
Our automated roster generation software is a valuable tool that can help hospital units improve their efficiency, patient outcomes, and staff satisfaction with the rostering process.
AI-powered patient load prediction
Uniquely, Our AI algorithms analyze historical data, appointment trends, and patient demographics to accurately forecast patient demand, ensuring optimal staffing levels and minimizing patient wait times.
Reduced scheduling overhead
Our system eliminates manual scheduling tasks, saving administrators valuable time and resources that can be better utilized in patient care activities.
Transparent and equal opportunity scheduling
Our system eliminates scheduling biases and ensures that all doctors are given fair and equitable opportunities to work different shifts, specialties, and patient types.
Flexible staffing preferences and scheduling models
Our system can accommodate various scheduling preferences and supports various scheduling models, including on-call rotations, fixed shifts, and flexible schedules.
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01
Upload CSV or spreadsheet of your team's information
Our rostering tool supports a wide range of digital formats, including Excel spreadsheets, and allows users to specify preferred leave days for individual staff.
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02
Specify roster configuration and constraints
The Amathambo AI user-friendly interface allows users to intaractively specify rosters with custom call/shift types. Unlike manually generated rosters, we can generate rosters of up to 6 months or longer far in advance!
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03
Download and distribute
Our generated rosters can be downloaded in your preferred format for easy distribution to the rest of your team, or additional tweaking.
Make quantifiable progress in your staff scheduling process
Adopting automated roster generation software minimizes the likelihood of human error, fosters enhanced communication and collaboration among staff, and enhances flexibility to adapt to evolving requirements. Additionally, the capacity to track and analyze staffing data enables data-driven decision-making to optimize resource allocation and enhance patient care.
Time savings from manual roster making 12000%
Accuracy of generated rosters 98%
Satisfaction of medical staff with better rosters 90%
Cost savings from using generated rostering 54%
Get in touch
Get in touch with our dedicated team of experts today!
About Us
There are fewer and more uneven distributions of African healthcare workers compared to the rest of the world, and adequate staffing was identified as the biggest challenge to health systems in a recent meeting of African epidemiologists. Knowing how many staff are needed and at what times is a particular concern in the emergency centre setting, since there are large temporal fluctuations in patient number and severity. Having too few staff on duty means patient-care could be compromised; having an excess wastes expenditure and personnel hours. In light of this, Amathambo AI is developing an online roster generation platform that aims towards
- Retention of medical staff with improved staff satisfaction and mental well-being.
- Empowering of hospitals and medical teams through data-driven insights and informed decision making.
- Improved patient outcomes and reduced patient waiting times.
By working hand in hand with medical professionals, we aim to improve general medical resource and equipment management through smart digital tools and AI-powered algorithms.
Learn MoreTeam
Our founding team is made up of capable Machine Learning experts and experienced Doctors with consistent exposure to the hospital environment.
Dr. Kira Dusterwald
CEOKira is a South African medical doctor in the NHS and PhD candidate in theoretical neuroscience and machine learning at University College London in the UK. She did her medical training, neuroscience MSc and equivalent of a maths degree at the University of Cape Town, supported by the Mandela Rhodes Foundation.
Simphiwe Zitha
COOSimphiwe is a Senior Machine Learning Scientist from South Africa, currently residing in Germany. He holds an MSc Degree in Radio Astronomy & Machine Learning from Rhodes University, as well as an MSc degree in Mathematical Sciences from the University of Western Cape.
Sicelukwanda Zwane
CTOSicelukwanda is a PhD candidate in Robotics and AI at University College London in the UK. Before his PhD, he worked as a data scientist at Explore AI, a company that trains talented African youth in modern data engineering, data science, and business intelligence.