AI Rostering at Royal Hobart Hospital

Whitepaper – January 2025

Introduction

At HosPortal, we focus on solving meaningful problems with practical solutions. Royal Hobart Hospital (RHH), a valued customer, had already been using our original automated rostering algorithm to streamline their Consultant and Junior Medical Officer (JMO) rostering…

The results speak for themselves. RHH reduced their rostering time from 4 days to just 4–5 hours…

The Challenge

RHH’s rostering process was complex and time-intensive, requiring significant manual effort…

Types of Rules

1. Shift conflict rules – prevent a single user from being on two specified shifts…
2. Work rules – limit how many shifts a user can do…
3. User conflict rules – specify that certain users must or must not work simultaneously…
4. Leave conflict rules – prevent shifts from being scheduled too close to leave…

Key Challenges

• Complex rules: RHH required implementation of 14 shift conflict rules, 9 work rules, 5 user conflict rules, and 44 patterns…
• Manual testing: Roster administrators spent hours manually checking rosters…
• Last-minute adjustments: Finalising rosters often needed rushed fixes…
• Time-intensive process: Up to 4 days were required to finalise a draft roster…

The Solution

HosPortal’s AI functionality was designed to address these challenges head-on…

Key Elements

1. Custom rule implementation: All required rules and patterns…
2. Collaborative effort: Success was driven by close collaboration…
3. Rapid support: Our team remained on standby throughout…
4. AI-driven efficiency: The AI reduced roster review…

What Worked Well

• Comprehensive rule implementation: All required rules…
• Client alignment: Pre-alignment on roster sequencing…
• Collaborative partnership: A strong working relationship…
• Time savings: Roster review and finalisation time dropped…
• Long-term relationship: RHH’s longstanding trust…

The Results: A 90% Time Reduction

By the end of the project, RHH had a fully functional AI-driven rostering system delivering measurable results:

• Time savings: Rostering time dropped from 4 days to 4–5 hours…
• Accuracy: All rules and patterns were implemented correctly…
• Flexibility: The system allowed adjustments…
• Client satisfaction: RHH was highly satisfied…

Key Takeaways

1. AI delivers significant efficiency gains: When implemented correctly…
2. Collaboration is critical: A strong partnership between the client and development team…
3. Alignment saves time: Pre-alignment on sequencing and expectations…
4. Support drives success: Responsive development teams…

Conclusion

The Royal Hobart Hospital case study highlights the value of HosPortal’s AI functionality…

This project demonstrates how technology, when combined with human expertise, can transform healthcare operations…