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Workforce Planning
The Workforce Planning Module analyzes market trends and uses workforce data to forecast hiring needs up to 12 months in advance plus push jobs to your VMS to fill open role quickly.
Role
Project
Year
Product Designer
Aya Healthcare - Workforce Planning
2024 - Present
01
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.
01
Identifying the Challenges
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.
Job to be done:
The Model Creation process begins with the user providing input data and model parameters in the User Plane, which are then validated by the API. Upon successful validation, the data is sent to the Backend for processing. The Backend then determines if model training is necessary. If so, it proceeds with training and evaluation, looping back to training if the model is not acceptable. Once an acceptable model is trained or if training is not required, the model is deployed and becomes available to the user in the User Plane as a ready model.
06
Final Design
Let's work
together.
Primary End Users
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.
Target Market/Customer Segment
P1: In-patient health systems with existing MSP Relationships
P2: New potential in-patient health systems customers (MSP or SAAS)
02
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.
Workforce AI Dashboard
The Dashboard is the entry point into every module and specifc to each users needs set by super user permissions.
Job to be done:
- Monitor and evaluate alerts (tasks) proactively to identify and prioritize necessary actions for seamless healthcare operations and patient care continuity.
Job to be done:
- Examine and validate the staffing guide (census view) to ensure its accuracy and comprehensibility regarding patient care ratios and census frequency, facilitating optimal staffing decisions.
- Browse and comprehend all staffing guides to gain an understanding of workforce deployment strategies and ensure alignment with healthcare service requirements.
Staffing Guide
Job to be done:
- Develop a new staffing guide to address evolving healthcare demands ensuring a balanced patient-to-staff ratio and adapting to changes in patient census. This will be completed in the scheduling system and change in Workforce AI, unless one did not exist prior, or the user need to use ratio/activities for a non-traditional department.
Hiring Model
Job to be done:
- Initiate and facilitate the authorization process for the draft staffing guide to secure approval, ensuring compliance with healthcare standards and alignment with with organizational staffing policies, if staffing guide in manually entered or changed.
- Analyze the annual hiring plan to ensure it aligns with projected healthcare demands optimizing staffing levels for quality patient care and operational efficiency.
- Initiate and facilitate the authorization process for the draft hiring plan to secure approval to ensure this is the primary model to hire from.
Hiring Dashboard - Unit Level
Job to be done:
- Execute forecasted hiring actions for a Full-Time Equivalent (FTE) position by approving and posting hiring requisitions to the HRIS or talent acquisition system, ensuring a timely and efficient recruitment process in line with strategic staffing objectives.
- Execute forecasted hiring actions for contractors efficiently by retrieving and submitting a request page from Aya VMS, ensuring timely augmentation of workforce to meet healthcare service demands.
Hiring Dashboard - Requisition Slide-out
Job to be done:
- Asses and track the productivity of models across worker types to ensure key metrics of patient care and operational efficiency over time
Productivity Model
Solution Overview
Workforce Planning Prototype
03
04
Impact and Benefits
Enhanced Resource Utilization: Accurate demand forecasting has led to better alignment of staff levels with patient needs, maximizing full-time equivalent (FTE) utilization.
Cost Reduction: Optimized scheduling and staffing have resulted in decreased reliance on premium labor and overtime, contributing to overall cost savings.
Time Efficiency: Automation of scheduling processes has freed administrative staff from time-consuming manual tasks, allowing them to focus on strategic initiatives.
Improved Patient Care: Consistent and adequate staffing levels have enhanced patient care quality and employee satisfaction.
In summary, Aya Healthcare's Workforce AI stands as a testament to the effective integration of AI and machine learning in solving complex workforce management challenges in healthcare. By focusing on predictive analytics and user-centered design, Workforce AI delivers a solution that not only addresses current operational hurdles but also positions healthcare organizations for future success.
Take the guesswork out of hiring and anticipate future needs months in advance.
Model what-if scenarios to prevent under-and over-hiring
Get guidance on budgeting for core and contingent labor
Anticipate hiring needs 12 months in advance
Easily push job requisitions to LotusOne to fill open roles quickly
