Sustainable waiting time reductions after introducing the STAT model for access and triage: 12-month follow up of a stepped wedge cluster randomised controlled trial

Insights into how universal, tax-funded, single payer health systems manage their waiting lists: A review of the literature

Background: A conspicuous consequence of gatekeeping arrangements in universal, tax-funded, single-payer health care systems is the long waiting times. Besides limiting equal access to care, long waiting times can have a negative impact on health outcomes. Long waiting times can create obstacles in a patient's care pathway. Organization for Economic Co-operation and Development (OECD) countries have implemented various strategies to tackle this issue, but there is little evidence for which approach is the most effective. This literature review examined waiting times for ambulatory care. Objective: The aim was to identify the main policies or combinations of policies universal, tax-funded, and single-payer healthcare systems have implemented to improve the governance of outpatient waiting times. Methods: Starting from 1040 potentially eligible articles, a total of 41 studies were identified via a 2-step selection process. Findings: Despite the relevance of the issue, the literature is limited. A set of 15 policies for the governance of ambulatory waiting time was identified and categorized by the type of intervention: generation of supply capacity, control of demand, and mixed interventions. Even if a primary intervention was always identifiable, rarely a policy was implemented solo. The most frequent primary strategies were: guidelines implementation and/or clinical pathways, including triage, guidelines for referral and maxim waiting times (14 studies), task shifting (9 studies), and telemedicine (6 studies). Most studies were observational, with no data on costs of intervention and impact on clinical outcomes.

Sustainability of an intervention to reduce waiting for access to an epilepsy outpatient clinic

Purpose

Delays in outpatient specialist neurologist care for people with epilepsy are common despite recommendations for prompt access. There is evidence to suggest that there are interventions that can minimise waitlists and waiting time. However, little is known about whether such interventions can result in sustained improvements in waiting. The aim of this study was to determine the extent to which an intervention to reduce waiting in an epilepsy specialist outpatient clinic demonstrated sustained outcomes two years after the intervention was implemented.

Methods

This observational study analysed routinely collected epilepsy clinic data over three study periods: pre-intervention, post-intervention and at two-year follow-up. The intervention, Specific Timely Assessment and Triage (STAT), combined a short-term backlog reduction strategy and creation of protected appointments for new referrals based on analysis of demand. After the initial intervention, there was no further active intervention in the following two years. The primary outcome was waiting measured by 1.) waiting time for access to a clinic appointment, defined as the number of days between referral and first appointment for all patients referred to the epilepsy clinic during the three study periods; and 2.) a snapshot of the number of patients on the waitlist at two time points for each of the three study periods.

Results

Two years after implementing the STAT model in an epilepsy clinic, median waiting time from post-intervention to two-year follow-up was stable (52-51 days) and the interquartile range of days waited reduced from 37 to 77 days post-intervention to 45-57 days at two-year follow-up, with a reduction in the most lengthy wait times observed. After a dramatic reduction of the total number of patients on the waitlist immediately following the intervention, a small rise was seen at two years (n = 69) which remained well below the pre-intervention level (n = 582).

Conclusion

The STAT model is a promising intervention for reducing waiting in an epilepsy clinic. While there was a small increase in the waitlist after two years, the median waiting time was sustained.

Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial

INTRODUCTION

Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.

METHODS

We developed an artificial intelligence (AI)-assisted program named Smart-doctor. A randomized controlled trial was conducted at Shanghai Children's Medical Center. Participants were randomly divided into an AI-assisted and conventional group. Smart-doctor was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used.

RESULTS

We enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group (p < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, p < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, p < 0.01). The overall satisfaction score was increased by 17.53% (p < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group (p < 0.01), and missing the turn (p < 0.01).

CONCLUSIONS

Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

Return on investment of a model of access combining triage with initial management: an economic analysis

OBJECTIVES

Timely access to outpatient services is a major issue for public health systems. To address this issue, we aimed to establish the return on investment to the health system of the implementation of an alternative model for access and triage (Specific Timely Appointments for Triage: STAT) compared with a traditional waitlist model.

DESIGN

Using a prospective pre-post design, an economic analysis was completed comparing the health system costs for participants who were referred for community outpatient services post-implementation of STAT with a traditional waitlist comparison group.

SETTING

Eight community outpatient services of a health network in Melbourne, Australia.

PARTICIPANTS

Adults and children referred to community outpatient services.

INTERVENTIONS

STAT combined targeted activities to reduce the existing waiting list and direct booking of patients into protected assessment appointments. STAT was compared with usual care, in which new patients were placed on a waiting list and offered appointments as space became available.

OUTCOMES

Health system costs included STAT implementation costs, outpatient health service use, emergency department presentations and hospital admissions 3 months before and after initial outpatient appointment. Waiting time was the primary outcome. Incremental cost-effectiveness ratios (ICERs) were estimated from the health system perspective.

RESULTS

Data from 557 participants showed a 16.9 days or 29% (p<0.001) reduction in waiting time for first appointment with STAT compared with traditional waitlist. The ICER showed a cost of $A10 (95% CI -19 to 39) per day reduction in waiting time with STAT compared with traditional waitlist. Modelling showed the cost reduced to $A4 (95% CI -25 to 32) per day of reduction in waiting, if reduction in waiting times is sustained for 12 months.

CONCLUSIONS

There was a significant reduction in waiting time with the introduction of STAT at minimal cost to the health system.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ACTRN12615001016527).

A Multi-Faceted Strategy for Evidence Translation Reduces Healthcare Waiting Time: A Mixed Methods Study Using the RE-AIM Framework

Background: Waiting lists are often thought to be inevitable in healthcare, but strategies that address patient flow by reducing complexity, combining triage with initial management, and/or actively managing the relationship between supply and demand can work. One such model, Specific Timely Appointments for Triage (STAT), brings these elements together and has been found in multiple trials to reduce waiting times by 30–40%. The next challenge is to translate this knowledge into practice.

Method: A multi-faceted knowledge translation strategy, including workshops, resources, dissemination of research findings and a community of practice (CoP) was implemented. A mixed methods evaluation of the strategy was conducted based on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework, drawing on an internal database and a survey of workshop and CoP participants.

Results: Demonstrating reach, at July 2020 an internal database held details of 342 clinicians and managers from 64 health services who had participated in the workshop program (n = 308) and/or elected to join an online CoP (n = 227). 40 of 69 (58%) respondents to a survey of this population reported they had adopted the model, with some providing data demonstrating that the STAT model had been efficacious in reducing waiting time. Perceived barriers to implementation included an overwhelming existing waiting list, an imbalance between supply and demand and lack of resources.

Conclusion: There is high quality evidence from trials that STAT reduces waiting time. Using the RE-AIM framework, this evaluation of a translation strategy demonstrates uptake of evidence to reduce waiting time in health services.