Demand-Driven Operating Room Allocations to Improve Surgical Waiting Lists

Customer

Tallaght University Hospital (TUH) is one of Ireland’s major academic teaching hospitals. With 562 beds, 12 on-site operating rooms (OR) and 4 off-site day surgery ORs. The hospital operates on almost 10,000 patients per year.

Problem

  • Deciding operating room allocations for different specialties and consultants was time-intensive and too subjective.
  • Allocations tended to be historical and needed review due to changing cirumstances and waiting list pressures.
  • A more transparent, objective and real-time system of operating room allocation was needed.
  • Over allocating OR time is a waste of resources. Under allocating risks patient health and safety and threatens lengthening waiting lists.
  • Surgical activity was heavily impacted by COVID-19 and the hospital had to plan for a return to more normal activity levels.
  • New surgical consultants were joining TUH and needed OR allocations.

Solution

A waiting list demand driven Operating Room allocation tool

MedModus developed a demand-driven OR allocation model and analytics dashboard that enables dynamic allocation of OR slots to specialties and consultants based on real-time need. 

MedModus assessed the above concerns and challenges, and recognized that a solution needed to consider the following:

  • At minimum, the need for consultants to keep up with waiting list demand.
  • The need to reduce waiting lists, especially for those consultants and specialties with the largest backlogs.
  • Not all surgeries and not all consultants are the same – different procedures, different complexities, different lengths of time required will mean different allocations and approaches.
  • COVID-19 had a substantial impact on surgical activity that may not reflect true demand.

An iterative assessment and analysis process was followed, collaborating with clinical leadership at the hospital. MedModus started with one specialty for basic proof of concept, incorporating emergency and elective surgical activity, waiting list trends, consultant-level procedure times, and other factors across multiple time periods, and then expanded to all other surgical specialties. Validation was sought between all phases of development, both from the expertise of clinical stakeholders and from rigorous data analysis. This contributed to both building trust with TUH and gaining valuable insight into daily operational procedures and challenges that would then result in targeted improvements to the model. This was a collaborative effort, joining numbers and narrative, data, and clinical and administrative practices.

From this, an interactive analytics dashboard was built that showed the ideal operating room allocations for every consultant within every specialty, with variable controls for sensitivity analysis – all the information necessary in one place to make informed decisions.

Results

N

Time to produce each operating room schedule substantially reduced.

N

Fair and transparent allocations process that consultants and directors trust.

N

Reduction of waiting lists for specialties and consultants with longest waiting times.

N

Keeping up with weekly demand across every consultant and specialty.

N

Reduction in theatre operational costs; all open operating rooms being actively utilized to full potential.

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