Petal is helping to shape healthcare by aggregating data that, when coupled with artificial intelligence developments, can help to automate processes and predict outcomes.
The potential of Petal's Data and AI
The use of artificial intelligence (AI) in healthcare is still in its infancy, though the applications of its use are endless. AI relies on the analysis of large data sets in order to provide a solution to complex problems.
Petal’s orchestration platform helps to aggregate data that has traditionally been isolated to separate systems, enabling the platform to provide real-time solutions to real-time problems.
Optimization of Physician Scheduling
The Petal platform helps to automate physician scheduling through a complex algorithm. The Scheduling for Physician solution takes into account a multitude of constraints in order to create the most optimized schedule possible.
Through further AI enhancements, the platform aim to analyze previous schedules and learn allocation trends specific to each healthcare department or organization. These trends could then help define best practices in scheduling for different healthcare specialties.
Predictive Patient Appointment Scheduling
Petal’s Patient Self-Scheduling module helps patients efficiently book the most convenient appointments for their unique circumstances. With access to the totality of patient demand data, it would be possible for AI to learn and ultimately predict the best appointment for each patient based on their demographics and existing relationship with a physician or healthcare organization.
Improving the efficiency of accessing care for patients ultimately ends with improved outcomes, as they adhere to scheduled appointments and treatment plans more closely.
Forecast of Clinical Capacity
Petal’s Orchestrator platform is able to aggregate patient demand and healthcare supply data. These data sets are essential for predicting seasonal fluctuations of healthcare demand and highlight shortfalls or abundances in the healthcare offering.
Government agencies or healthcare network administrators could leverage AI modelling to make data-driven decisions to improve the access of care for all.
