Sep 08, 2023
No image
Completed
Allmedica: genetic algorithms as a key to the happiness of doctors and patients
$25,000+
2-3 months
Poland
2-5
Service categories
Service Lines
Artificial Intelligence
Big Data
IT Services
Domain focus
Healthcare
Other
Technology
Programming language
Ruby
Frameworks
Ruby on Rails
TensorFlow
Subcategories
Artificial Intelligence
Machine Learning
Challenge
The client came to us with a demanding task: to design a system of active patient allocation that works based on their preferences.
The idea for the functionality of the system was as follows: with genetic algorithms, data mining and analysis of data related to patient visits in real time, the system will be able to use doctors' time more effectively and eliminate "empty slots."
Based on patients' preferences, the system, in real time, moves appointments to earlier available dates, suggests control visits or applies price promotions according to strictly defined algorithms, with the aim of filling all visit time slots.
This way everyone benefits: patients wait for appointments less, doctors have their schedules arranged better and the clinic becomes more competitive. Win-win-win.
The client came to us with a demanding task: to design a system of active patient allocation that works based on their preferences.
The idea for the functionality of the system was as follows: with genetic algorithms, data mining and analysis of data related to patient visits in real time, the system will be able to use doctors' time more effectively and eliminate "empty slots."
Based on patients' preferences, the system, in real time, moves appointments to earlier available dates, suggests control visits or applies price promotions according to strictly defined algorithms, with the aim of filling all visit time slots.
This way everyone benefits: patients wait for appointments less, doctors have their schedules arranged better and the clinic becomes more competitive. Win-win-win.
Solution
Our team started by collecting the client's business requirements. Important: our system was not meant to replace the current system.
The second step was to establish KPIs and deadlines. We developed the software in weekly sprints, but during the work, it turned out that due to parallel projects on the client's side and the involvement of the technical team, the schedule had to be changed.
The third step was to design the system architecture and communication between systems. We used the Swagger tool to establish the APIs.
We decided on a set of serverless services from Google: Google DataStudio, BigQuery and TensorFlow, which allowed us to implement data analysis faster and cheaper.
Our team started by collecting the client's business requirements. Important: our system was not meant to replace the current system.
The second step was to establish KPIs and deadlines. We developed the software in weekly sprints, but during the work, it turned out that due to parallel projects on the client's side and the involvement of the technical team, the schedule had to be changed.
The third step was to design the system architecture and communication between systems. We used the Swagger tool to establish the APIs.
We decided on a set of serverless services from Google: Google DataStudio, BigQuery and TensorFlow, which allowed us to implement data analysis faster and cheaper.
Results
The result of the project was a prototype of a heuristic system that actively allocated patients and shortened the waiting time for an appointment.
Thanks to the system, the efficiency of the clinic was increased and "empty slots" were minimized, allowing the clinic to serve more patients and achieve higher profits.
Doctors were also satisfied with the new system: they no longer had to wait for absent patients and are more productive.
The system also implemented control visits and promotions, which helped to fill the clinic calendar.
The result of the project was a prototype of a heuristic system that actively allocated patients and shortened the waiting time for an appointment.
Thanks to the system, the efficiency of the clinic was increased and "empty slots" were minimized, allowing the clinic to serve more patients and achieve higher profits.
Doctors were also satisfied with the new system: they no longer had to wait for absent patients and are more productive.
The system also implemented control visits and promotions, which helped to fill the clinic calendar.