Application of the STAT model for demand management to reduce specialist clinic waiting times: protocol for the WaitLESS stepped wedge cluster randomised controlled trial.

Abstract

Patients frequently face excessive waiting times for specialist medical outpatient clinics, negatively impacting health outcomes. Previous research has demonstrated that translation of demand-driven strategies incorporated within the evidence-based Specific Timely Assessment and Triage (STAT) model effectively reduces wait times in a range of healthcare settings. We outline the study protocol for a hybrid type II implementation effectiveness study, using a stepped wedge cluster randomised controlled trial design, to evaluate whether the STAT model can be implemented in specialist medical clinics and reduce waiting times. The Waiting List Evidence to Support Specialist clinics (WaitLESS) trial will implement STAT in eight clinical specialties (clusters) offering outpatient medical care at a metropolitan health network in Melbourne, Australia. After a 6 month pre-implementation period, clusters will be randomised to implement the STAT model, two clusters at a time at 3-month intervals, with a minimum 6-month follow-up period, following the STAT model's five-step implementation process: (i) analyse historical clinic demand data, (ii) model supply/demand, (iii) implement target interventions to address existing backlogs, (iv) protect capacity for new patients to align with demand and (v) implement tailored strategies to enhance patient flow. Strategies will be implemented collaboratively by researchers and clinical teams. The trial will measure both effectiveness (reductions in waiting time and patients on the waiting list) and implementation (fidelity, adoption, acceptability, feasibility, appropriateness and cost) outcomes, using quantitative healthcare data and qualitative data from staff and patients. Linear mixed-effects models will be used for analysis of the primary effectiveness outcome of waiting time from referral to first appointment, with time point and time since intervention as factor variables. Implementation outcomes will be analysed descriptively, with qualitative data mapped deductively to the Consolidated Framework for Implementation Research. A cost of implementation and cost-effectiveness analysis will be completed from the health service perspective. ISRCTN15820171.