Predicitve Scheduling
The Predictive Scheduling module enriches existing scheduling tools with demand based forecasts and smart balancing helping nurse leaders easily create accurate optimized schedules.
Role
Project
Year
Product Designer
Aya Healthcare - Predictive Scheduling
2024 - Present
02
My Role
User Research
UX/UI Design
Timeline
2024 - Present
Tools Used
Figma
Miro
ChatGPT
Mobbin
Vision
Give healthcare teams the time they need to stay ahead, with real-time insights and forecasting that help act hours in advance, easing the pressure of last minute desicions.
Workforce AI is set to revolutionize Healthcare Staffing with Predictive Intelligence. Our vision is to transform healthcare staffing by integrating advanced predictive analytics seamlessly
into every phase of the scheduling process.
Designed with a user-centric focus for simplicity and efficiency, and scalable to meet the diverse needs of various healthcare facilities, Workforce AI synergizes with existing systems and leverages Aya's extensive product offerings.
We are dedicated to empowering healthcare providers to optimize staff utilization, reduce external staffing dependencies, and elevate patient care. Committed to delivering real-time, actionable insights, Workforce AI fosters a more efficient, responsive, and cost-effective healthcare environment, adaptable to evolving staffing demands.
Unique Value Proposition
Take the guess work out of scheduling and staffing with the most accurate demand forecast. When combined with your staffing model, we can predict exact hiring needs,
automatically optimize schedules and help fill staffing gaps with our industry leading workforce management solutions and staffing network.
Healthcare organizations often grapple with fluctuating patient demand, leading to staffing inefficiencies such as overstaffing or understaffing. Traditional methods of workforce planning and scheduling are frequently reactive, relying on historical data and manual adjustments that may not accurately predict future needs. This reactive approach can result in increased labor costs, employee burnout, and compromised patient care quality.
01
Identifying the Challenges
Job to be done:
The "Inpatient & Float Pool Scheduling Together" flow illustrates an integrated process that begins with input from systems like HRIS, EMR, and UKG for staffing and scheduling. Workforce AI uses historical data and a staffing matrix—adjusted in line with budgeting—to create a demand forecast. This forecast feeds into workload planning and informs a schedule generator that applies shift templates and matrix logic. The resulting schedule is opened for self-scheduling by various staff types (e.g., part-time, PRN), then reviewed to ensure all employees meet their FTEs. Once balanced, the finalized schedule is published, completing the workflow.
06
Final Design
Primary End Users
Target Market/Customer Segment
P1: In-patient health systems with existing MSP Relationships
P2: New potential in-patient health systems customers (MSP or SAAS)
These personas are typically either the main user group or users directly impacted by the tool. Similarly to purchasers, they have distinct needs, priorities ans concerns. Understanding these personas is crucial for ensuring the tool is designed to meet their specific needs and challenges. Examples of design considerations:
- User-Centric Design: Understanding these personas ensures that he software is designed with a user-centric approach, addressing the practical needs and pain points experienced by those who will use the tool daily.
- Enhanced User Experience: Insights into these personas contribute to creating an intuitive and efficient user interface, leading to better adoption and satisfaction among users.
- Feedback Loop for improvement: These personas are a valuable source of feedback for ongoing improvements and updates to the software, ensuring it continues to meet evolving user needs.
Design and Development
To tackle these challenges, Aya Healthcare embarked on creating Workforce AI, an AI-driven solution designed to forecast patient demand and optimize workforce allocation. The development process encompassed several critical steps:
User-Centric Research: Engaging with healthcare administrators, HR personnel, and clinical staff to understand their pain points and requirements in workforce management.
Integration of Advanced Technologies: Leveraging predictive analytics, machine learning, and AI to develop models capable of forecasting patient volumes and corresponding staffing needs months in advance.
Modular Design Approach: Creating three distinct yet interconnected modules—Workforce Planning, Predictive Scheduling, and Predictive Staffing—to address various facets of workforce management.
Seamless System Integration: Ensuring compatibility with existing HRIS, VMS, and scheduling tools to facilitate smooth data flow and user adoption.
Iterative Testing and Feedback: Conducting pilot programs and gathering feedback to refine algorithms and user interfaces, ensuring the solution effectively meets user needs.
02
Schedule Template Prototype
03
Solution Overview
Job to be done:
- Provide a standardized framework for creating staff schedules, aligning shift patterns with patient care needs. Automate and streamline the scheduling process, reduce manual work, ensure compliance with labor rules, and promotes fairness across shifts. This helps nursing managers efficiently balance coverage while adapting to changes like call-offs or varying patient volumes.
New Optimized Schedule Prototype
Job to be done:
- Ensure the right number of nurses with the right skills are assigned to each shift based on patient demand, staff availability, and compliance requirements. It minimizes overstaffing and understaffing, reduces labor costs, and improves patient care and staff satisfaction.
Script:
- Explore Predictive Scheduling and follow Grace, a nurse manager, through the process of using the New Optimized Schedule as needs to resolve staffing issues for the upcoming schedule shift.
New Optimized Schedule Demo
Job to be done:
- Automatically adjust staffing levels to ensure each shift is properly covered based on patient demand, staff availability, and workload distribution. It helps prevent understaffing or overstaffing by redistributing shifts fairly and efficiently, saving time for managers and supporting consistent patient care.
Auto Balance Prototype
Script:
- Follow Grace, a nurse manager, as she show's how to Auto balance a weekly schedule. Self-scheduling just ended, and it's time to to balance the schedule.
Auto Balance Demo
04
Impact and Benefits
Automatically create and balance schedules for improved staffing accuracy.
Predictive scheduling in nursing software uses data and algorithms to forecast staffing needs based on patient volumes, historical trends, and other variables.
Improved Patient Care: Predictive scheduling ensures the right number of nurses are available to meet patient demand, improving care quality and response times.
Lower Labor Costs: It helps avoid overstaffing and excessive overtime by aligning staffing with accurate demand forecasts.
Better Staff Satisfaction & Retention: Nurses benefit from more predictable schedules, reducing burnout and increasing job satisfaction.
Increased Operational Efficiency: Managers spend less time manually scheduling and can proactively adjust staffing based on forecasted needs.
Regulatory Compliance: It helps maintain safe staffing levels and comply with labor laws and healthcare regulations.
Create schedule templates aligned with forecasted demand
Auto-balance schedules with AI
Resolve staffing issues for upcoming scheduling cycles
Let's work
together.