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Background Many people wait long periods for community outpatient services. However little is known about the impact of waiting from referral to first visit on patient outcomes. The aim of this systematic review is to investigate whether waiting for community outpatient services is associated with adverse effects on patient outcomes. Methods Medline, Embase, Psych Info and CINAHL databases were searched, combining the key concepts of waiting for healthcare and patient outcomes. Studies were included if they reported data comparing health outcomes for patients with different waiting times for the same period. Three reviewers applied inclusion and exclusion criteria to identified studies and assessed quality using the McMaster Critical Review Forms. Levels of evidence were assessed using National Health and Medical Research Council guidelines. Included studies were analysed using a descriptive synthesis, and summarised according to levels of evidence and clinical significance for key outcomes. Results Fourteen studies that included 69,606 adult patients were selected. Selected studies included patients referred for treatment for musculoskeletal disorders ( n  = 28,722) or to cardiac rehabilitation ( n  = 40,884). There was low-level evidence that reduced wait time is associated with moderate improvement in workplace participation for patients seeking care for musculoskeletal conditions; and moderate improvement in exercise tolerance for patients referred to cardiac rehabilitation. There was inconsistent evidence that improvements in quality of life, patient satisfaction and psychological symptoms may be associated with shorter wait times. Pain, function and physical activity outcomes were not associated with wait time. Conclusions This review found low-level evidence suggesting an association between early access to community outpatient services and improvement of some patient outcomes. Specifically, shorter wait times from referral to first visit for musculoskeletal pain services may improve patient work participation. Shorter wait times for cardiac rehabilitation may improve patient exercise capacity. The effects of a short wait time for other patient conditions and patient outcomes, including quality of life, psychological symptoms and patient experience, are inconclusive. The modest benefits in health outcomes observed in reducing wait time for community outpatient services suggest that other possible benefits such as increasing patient flow should be explored. Trial registration PROSPERO registration no: CRD42016047003

Background Delayed access to outpatient care may negatively impact on health outcomes. We aimed to evaluate implementation of the Specific Timely Appointments for Triage (STAT) model of access in an epilepsy clinic to reduce a long waitlist and waiting time. Methods This study is an intervention study using pre-post comparison and an interrupted time series analysis to measure the effect of implementation of the STAT model to an epilepsy clinic. Data were collected over 28 months to observe the number of patients on the waitlist and the waiting time over three time periods: 12 months prior to implementation of STAT, ten months during implementation and six months post-intervention. STAT combines one-off backlog reduction with responsive scheduling that protects time for new appointments based on historical data. The primary outcomes were the number of patients on the waitlist and the waiting time across the three time periods. Secondary outcomes evaluated pre- and post-intervention changes in number of appointments offered weekly, non-arrival and discharge rates. Results A total of 938 patients were offered a first appointment over the study period. The long waitlist was almost eliminated, reducing from 616 during the pre-intervention period to 11 post-intervention ( p  = 0.002), but the hypothesis that waiting time would decrease was not supported. The interrupted time series analysis indicated a temporary increase in waiting time during the implementation period but no significant change in slope or level in the post- compared to the pre-intervention period. Direct comparison of the cohort of patients seen in the pre- and post-intervention periods suggested an increase in median waiting time following the intervention (34 [IQR 25–86] to 46 [IQR 36–61] days ( p  = 0.001)), but the interquartile range reduced indicating less variability in days waited and more timely access for the longest waiters. Conclusions The STAT model was implemented in a specialist epilepsy outpatient clinic and reduced a large waitlist. Reductions in the waitlist were achieved with little or no increase in waiting time. The STAT model provides a framework for an alternative way to operate outpatient clinics that can help to ensure that all people referred are offered an appointment in a timely manner.

Multidisciplinary tracheostomy teams have been implemented in acute hospitals over the past 10 years. This systematic review of the literature and meta-analysis aimed to assess the effect of tracheostomy teams on patient outcomes. We conducted an electronic search of the literature in the following databases: MEDLINE, CINAHL, EMBASE, and AMED. Inclusion/exclusion criteria were applied, and included articles were assessed against quality criteria. Qualitative synthesis and meta-analysis were completed. Seven studies were included. The studies were all pre-post cohort designs of low-moderate quality. Meta-analysis showed that tracheostomy teams were associated with reductions in total tracheostomy time (4 studies; mean difference, 8 days; 95% confidence interval, 6-11; P < .01; I2 = 0%) and hospital length of stay (LOS) (3 studies; mean difference, −14 days; 95% confidence interval, −39 to 9; P = .23; I2 = 50%). Reductions in intensive care unit LOS (3 studies) and increases in speaking valve (3 studies) use were also reported with tracheostomy teams. There is low-quality evidence that multidisciplinary tracheostomy care contributes to a reduction in total tracheostomy time and increase speaking valve use for patients leading to improved quality of life. There is insufficient evidence to determine that multidisciplinary tracheostomy teams reduce hospital or intensive care unit LOS.

Access to exercise for cancer survivors is poor despite global recognition of its benefits. Telerehabilitation may overcome barriers to exercise for cancer survivors but is not routinely offered. Following the rapid implementation of an exercise-based telerehabilitation program in response to COVID-19, a process evaluation was conducted to understand the impact on patients, staff, and the health service with the aim of informing future program development. A mixed methods evaluation was completed for a telerehabilitation program for cancer survivors admitted between March and December 2020. Interviews were conducted with patients and staff involved in implementation. Routinely collected hospital data (adverse events, referrals, admissions, wait time, attendance, physical activity, and quality of life) were also assessed. Patients received an 8-week telerehabilitation intervention including one-on-one health coaching via telehealth, online group exercise and education, information portal, and home exercise prescription. Quantitative data were reported descriptively, and qualitative interview data were coded and mapped to the Proctor model for implementation research. The telerehabilitation program received 175 new referrals over 8 months. Of those eligible, 123 of 150 (82%) commenced the study. There were no major adverse events. Adherence to health coaching was high (674/843, 80% of scheduled sessions), but participation in online group exercise classes was low (n=36, 29%). Patients improved their self-reported physical activity levels by a median of 110 minutes per week (IQR 90-401) by program completion. Patients were satisfied with telerehabilitation, but clinicians reported a mixed experience of pride in rapid care delivery contrasting with loss of personal connections. The average health service cost per patient was Aus $1104 (US $790). Telerehabilitation is safe, feasible, and improved outcomes for cancer survivors. Learnings from this study may inform the ongoing implementation of cancer telerehabilitation.

AimThe primary aim of this systematic review of the literature was to determine whether interventions to reduce waiting time in outpatient and community health services can be sustained. The secondary aim was to describe associations between sustainability and features of waiting time interventions and the settings in which they have been implemented.MethodsCINAHL, Medline, Embase and Psych Info databases were searched, combining the search concepts ‘waiting time or waiting lists’, ‘outpatient or community care’ and ‘sustainability’. Studies were included if they tested a service-level intervention that aimed to reduce waiting in an outpatient or community setting and reported data with a minimum 12-month follow-up period. Data were extracted and analysed using a descriptive synthesis. Methodological quality was evaluated using the mixed-methods appraisal tool (MMAT). Waiting interventions were rated as sustained, partially sustained or not sustained using predetermined criteria. The Grading of Recommendation, Assessment, Development and Evaluation was used to describe certainty of evidence for different intervention approaches.ResultsScreening of 7770 studies yielded 22 papers investigating the sustainability of waiting interventions for approximately 150 000 clients. Many were of lesser quality, with 14 not meeting more than 3 of 5 criteria on the MMAT checklist. Intervention types were categorised as referral entry, open access and substitution, used either alone or in combination. There was low certainty evidence that all interventions were associated with sustained reductions in waiting time, often with large effect sizes, but the findings are limited by low methodological quality of many studies and the risk of publication bias.ConclusionReductions in wait times and waiting lists for health services can be achieved and sustained following interventions, but further high-quality research would better inform service providers about what interventions are most effective and provide the greatest return on investment.

Background Managing demand for services is a problem in many areas of healthcare, including specialist medical outpatient clinics. Some of these clinics have long waiting lists with variation in access for referred people. A model of triage and appointment allocation has been developed and tested that has reduced waiting times by about a third in community outpatient services. This study aims to determine whether the model can be applied in the setting of a specialist medical outpatient clinic to reduce wait time from referral to first appointment. Methods A pre-post study will collect data before and after implementing the Specific Timely Appointments for Triage (STAT) model of access and triage. The study will incorporate a pre-implementation period of 12 months, an implementation period of up to 6 months and a post STAT-implementation period of 6 months. The setting will be the epilepsy clinic at a metropolitan health service in Melbourne. Included will be all people referred to the clinic, or currently waiting, during the allocated periods of data collection (total sample estimated n  = 975). Data routinely collected by the health service and qualitative data from staff will be analysed to determine the effects of introducing the STAT model. The primary outcome will be wait time, measured by number of patients on the wait list at monthly time points and the mean number of days waited from referral to first appointment. Secondary outcomes will include patient outcomes, such as admission to hospital while waiting, and service outcomes, including rate of discharge. Analysis of the primary outcome will include interrupted time series analysis and simple comparisons of the pre and post-implementation periods. Process evaluation will include investigation of the fidelity of the intervention, adaptations required and qualitative analysis of the experiences of clinic staff. Discussion Prompt access to service and optimum patient flow is important for patients and service providers. Testing the STAT model in a specialist medical outpatient clinic will add to the evidence informing service providers and policy makers about how the active management of supply and demand in health care can influence wait times. The results from this study may be applicable to other specialist medical outpatient clinics, potentially improving access to care for many people.

Background Long waiting times are associated with public community outpatient health services. This trial aimed to determine if a new model of care based on evidence-based strategies that improved patient flow in two small pilot trials could be used to reduce waiting time across a variety of services. The key principle of the Specific Timely Appointments for Triage (STAT) model is that patients are booked directly into protected assessment appointments and triage is combined with initial management as an alternative to a waiting list and triage system. Methods A stepped wedge cluster randomised controlled trial was conducted between October 2015 and March 2017, involving 3116 patients at eight sites across a major Australian metropolitan health network. Results The intervention reduced waiting time to first appointment by 33.8% (IRR = 0.663, 95% CI 0.516 to 0.852, P  = 0.001). Median waiting time decreased from a median of 42 days (IQR 19 to 86) in the control period to a median of 24 days (IQR 13 to 48) in the intervention period. A substantial reduction in variability was also noted. The model did not impact on most secondary outcomes, including time to second appointment, likelihood of discharge by 12 weeks and number of appointments provided, but was associated with a small increase in the rate of missed appointments. Conclusions Broad-scale implementation of a model of access and triage that combined triage with initial management and actively managed the relationship between supply and demand achieved substantial reductions in waiting time without adversely impacting on other aspects of care. The reductions in waiting time are likely to have been driven, primarily, by substantial reductions for those patients previously considered low priority. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12615001016527 registration date: 29/09/2015.

Introduction There is growing interest in developing research culture and opportunities for allied health professionals working in medical imaging. However, little attention has been given to identifying the research interest and needs of this group relative to the other allied health professions. We aimed to measure self‐reported research participation, interest, experience and confidence of allied health professionals working in medical imaging and compare the findings to clinicians working in allied health therapies. Methods A cross‐sectional survey of allied health professionals from medical imaging (radiographers, sonographers and nuclear medicine technologists) was conducted. The primary outcome, the Research Spider survey, measures 10 domains of research interest/experience/confidence on a 5‐point Likert scale. Results were compared to allied health therapy data. Results Responses were received by 82 medical imaging allied health professionals (65% response rate). Overall, medical imaging professionals rated themselves as having ‘some interest’ and ‘little experience or confidence’ in research. There was no difference in interest, experience and confidence among different imaging professions (interest P = 0.099, experience P = 0.380, confidence P = 0.212) or allied health therapists (interest P = 0.137, experience P = 0.363, confidence P = 0.791). Participants reported greatest interest in finding and reviewing literature and lowest interest in applying for funding. Conclusion There are strong similarities between medical imaging allied health professionals and allied health therapy professionals. Therefore, strategies used to promote research culture in allied health therapy professions could be leveraged to provide opportunities for medical imaging allied health professionals. Allied health professionals working in medical imaging have low levels of participation in research. They report having some research interest, but low levels of experience or confidence. Therefore, medical imaging professionals who are interested in research may benefit from training and support in clinical research that have been proven successful in other allied health professions

Background Timely access is a challenge for providers of outpatient and community-based health services, as seen by the often lengthy waiting lists to manage demand. The Specific Timely Appointments for Triage (STAT) model, an alternative approach for managing access and triage, reduced waiting time by 34% in a stepped wedge cluster randomised controlled trial involving 8 services and more than 3000 participants. Follow up periods ranged from 3 to 10 months across the participating services in accordance with the stepped wedge design. This study aimed to determine whether outcomes were sustained for a full 12 months after implementation of the STAT model at each site. Methods Routinely collected service data were obtained for a total of 12 months following implementation of the STAT model at each of the 8 services that participated in a stepped wedge cluster randomised controlled trial. The primary outcome was time to first appointment. Secondary outcomes included non-attendance rates, time to second appointment and service use over 12 weeks. Outcomes were compared to pre-intervention data from the original trial, modelled using generalised linear mixed effects models accounting for clustering of sites. Results A 29% reduction in waiting time could be attributed to STAT over 12 months, compared to 34% in the original trial. A reduction in variability in waiting time was sustained. There were no significant changes in time to second appointment or in the number of missed appointments in the extended follow up period. Conclusions STAT is an effective strategy for reducing waiting time in community-based outpatient services. At 12 months, small reductions in the overall effect are apparent, but reductions in variability are sustained, suggesting that people who previously waited the longest benefit most from the STAT model. Trial registration This is a 12-month follow up of a stepped wedge cluster randomised controlled trial that was registered with the Australia and New Zealand Clinical Trials Registry ( ACTRN12615001016527 ).

Background Specific Timely Appointments for Triage (STAT) is an intervention designed to reduce waiting time in community outpatient health services, shown to be effective in a large stepped wedge cluster randomised controlled trial. STAT combines initial strategies to reduce existing wait lists with creation of a specific number of protected appointments for new patients based on demand. It offers an alternative to the more traditional methods of demand management for these services using waiting lists with triage systems. This study aimed to explore perceptions of clinicians and administrative staff involved in implementing the model. Method Semi-structured interviews with 20 staff members who experienced the change to STAT were conducted by an independent interviewer. All eight sites involved in the original trial and all professional disciplines were represented in the sample. Data were coded and analysed thematically. Results Participants agreed that shorter waiting time for patients was the main advantage of the STAT model, and that ongoing management of caseloads was challenging. However, there was variation in the overall weight placed on these factors, and therefore the participants’ preference for the new or previous model of care. Perceptions of whether the advantages outweighed the disadvantages were influenced by five sub-themes: staff perception of how much waiting matters to the patient, prior exposure to the management of waiting list, caseload complexity, approach and attitude to the implementation of STAT and organisational factors. Conclusions The STAT model has clear benefits but also presents challenges for staff members. The findings of this study suggest that careful preparation and management of change and active planning for known fluctuations in supply and demand are likely to help to mitigate sources of stress and improve the likelihood of successful implementation of the STAT model for improving waiting times for patients referred to community outpatient services.