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

Abstract

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.

Keywords

waiting lists waiting time management outpatient

Background

Waiting times and waiting lists result from an imbalance between the demand for and the supply of healthcare services.¹ Thus, when the demand exceeds the supply, waiting lists ensure making patients wait.

Nowadays, public systems can offer only a limited range and volume of services due to the financial constraints they face. Moreover, a growing ageing population accompanied by the epidemiological transition is widening the gap between the demand for services and the opportunity to pay by public administrations.² To date, chronic oncological and neurodegenerative disorders are the principal expenditure voice for the healthcare sector. Failure to anticipate such challenges means less accessibility, longer waiting times, and loss of quality of the healthcare system. Waiting time is a critical item both for patient satisfaction³ and for the healthcare chain:

• Poor clinical outcomes. Time-to-treatment initiation is a determinant of clinical outcomes, especially in cancer care, while there are mixed results for elective care.^1,4–6

• Low productivity. Long waiting times cause poor clinical outcomes and stress, which lead to reduced productivity. An empirical analysis of the Fraser Institute estimated the cost of waiting for medical treatment in Canada at $2.1 billion in 2019.⁷

• Inequality. Waiting lists are one of the main reasons for unmet needs in healthcare by limiting access to services and care.^8,9 In its report on inequalities in access to healthcare, the European Social Policy Network (ESPN) stated that, in many countries, patients bypass waiting lists in the public sector by consulting private specialists or offering informal payments.⁸ These practices result in inequalities, unmet needs, and lower clinical outcomes, especially for patients of lower socioeconomic status.

In such a context, governments must consider (and solve) this problem to allow citizens to have a quality care experience and to be compliant with internal laws and European legislation, too. Many Constitutions recognize accessibility to healthcare as a right of the population and not just citizens.¹⁰ European Union (EU) also recognized the right to “timely access health care” in the 16th article of the European Pillar of Social Rights.¹¹ Furthermore, undue delay for treatment in the country of affiliation is a formal condition for the application of the EU Cross Border Healthcare Directive.¹²

As mentioned above, long waiting times are common issues for public administration, particularly for universal, tax-funded, single-payer healthcare systems that operate gatekeeping arrangements.^13,14 Gatekeeping for secondary care could be both a resource and a disadvantage: although it can be a way to control the use of resources, it can become a means to ration the delivery of services¹⁵ to the detriment of lower-income patients. Many OECD countries.^1,5 face these situations.

This study investigated the waiting times in specific contexts in the OECD area for non-urgent ambulatory care. In particular, the authors selected counties based on specific characteristics and elements such as:

• Universal, tax-funded healthcare systems with gatekeeping arrangements.

• General practitioners (GP) provide gatekeeping arrangements for outpatient services.¹⁶

• Ambulatory cares are the principal source of health care. The increasing burden of chronic disorders in the ageing population progressively pushes from hospital settings to ambulatory ones. Besides, prevention medicine and risk factor management entail more follow-up and monitoring consultations, thus increasing the demand for outpatient care. Digital health technologies support this shift.

Examples of health systems that do not correspond to the characteristics highlighted above are the Bismarck model system (Germany, France, and the Netherlands) and private and not statutory ones (USA).

To conclude, this analysis aims to identify the principal effective policies or their combination in universal, tax-funded, and single-payer healthcare systems in OECD countries that improve the governance of outpatient waiting times. Furthermore, the authors want to fill a gap in the literature on waiting times strategies through a literature review and to expand current knowledge on policies implemented in those contexts with a focus on outpatient care. In fact, to date, none of the literature reviews^17–19 nor OECD reports^5,20,21 published on PubMed examine universal, tax-funded, single-payer systems.

Review of the literature: Scoping review

The authors of this manuscript have chosen the scoping review to conduct their analysis among the numerous possible literature reviews. The authors aim to map and summarise the qualitative and quantitative research’s evidence concerning reducing lists and waiting times.

Given the methodological choice of the scoping review, the authors did not conduct a quality assessment of the studies. Moreover, PubMed is the only database where the research was carried out.

The research question under review can be stated as follows.

A waiting list is made up of the list that expresses the number of requests registered for service in ratio to delivery time. This time is primarily measured as the interval between the time of registration by a supplier and the time of performance of the service. Which active governance or operational policies, both within and outside healthcare structures, can improve the management of waiting lists for non-urgent outpatients? Which contextual elements affect the management of waiting lists for the same population target?

Methods: Search string and inclusion/exclusion criteria

In October and November 2022, we searched the PubMed electronic database for studies on health policies and health-related issues using two research strings:

1. (ambulatory)AND(waiting)AND(time*)

2. (outpatient)AND(waiting)AND(time*)

Before entering search terms into the database, the authors pre-set the following search filters:

• Publication date: from 2000/01/01 to 2022/09/30

• Species: Human

• Text availability: free full text

The authors used a 2-step process to evaluate which studies to include in the review.

1. Selection by keywords: only articles including specific keywords in their titles or abstracts were selected. The searched terms (both singular and plural) were “waiting time”, “waiting list”, “process improvement”, “management”, ”reduction”, “accessibility”, “outpatient”, and “ambulatory”.

2. Selection by content. The authors decided on ex-ante and ex-post inclusion and exclusion criteria which articles include in the review. In particular, the inclusion criteria are:

a. Studies that take place in one or more OECD countries with healthcare systems primarily based on universal, tax-funded, single-payer models. For example, Beveridge-type systems (United Kingdom, Italy, Spain, Portugal, Norway, Sweden) and National Health Insurance (NHI) ones (Australia, Canada, New Zealand).

b. Studies describing the impact of a policy or combination of policies on waiting time in outpatient/ambulatory care. These included, among others: medical appointments with specialists, minor surgeries that do not require hospitalisation or inpatient care, outpatient medical examinations, etcetera.

c. Comparative interventional studies describe the scenario before and after the introduction of a policy. Studies had to present actual cases (not only planned but also expected or simulated) and report quantitative data on the effects of the interventions implemented.

d. Publication in English.

On the other hand, the exclusion criteria are:

a. Studies in progress, not yet concluded, or protocols only.

Once the selection ended, the authors summarised the articles' content in seven points to highlight the principal characteristics of the studies:

1. Country

2. Type of Study

3. Main policy implemented

4. Level of implementation

a. Local (micro) if policies are implemented in a single hospital/clinic.

b. Regional (meso) when the implementation contest is a provincial/regional network of hospitals or clinics.

c. National (macro) when the entire national territory was the subject of the new policies implementation.

5. Main outcomes of the policy/policies implemented on waiting times.

6. Cost analysis. Although the authors have not conducted an analysis of the articles' quality, they have verified the presence or absence of a cost analysis of the intervention. It is because the presence of this analysis allows more complete considerations concerning the implemented policies.

7. Clinical outcomes analysis. While reviewing the articles, the authors realised that not all contributions analysed the impact of new policies on clinical outcomes. Although the lack of this analysis is not sufficient to exclude the article from the review, its presence is relevant for any considerations on the real impacts of new policies.

Results

The result of the 2-steps analysis, as described in paragraph 2.1, allows the author to select which articles to include in the final review. The analysis has been carried out on both strings of research.

First, starting from the 479 articles found through the first string of research (Table 1) in PubMed, the authors excluded 448 of them. Specifically, 285 titles or abstracts do not include relevant keywords and so they have been excluded. The other 163 articles have been rejected as inconsistent with inclusion and exclusion criteria. Notably, 87 out of 163 studies were not conducted in one of the OECD countries with a universal, tax-funded, and single-payer healthcare system; the target of 53 papers was inconsistent with the target of the present review; 23 out of 163 don’t imply tangible intervention. In conclusion, the authors include only 31 articles out of 479 initial results according to the 2-steps evaluation.^{4–7,12,15,17,22–44}

Table 1.

Chronological search sequence 1.

Keywords	Number of articles
(ambulatory)	38.293
(ambulatory)AND(waiting)	665
(ambulatory)AND(waiting)AND(time*)	479

Secondly, (outpatient)AND(waiting)AND(time*) take out 561 results (Table 2) in PubMed. The authors excluded 553 articles. In particular, the absence of keywords or the non-relevance for the review makes 490 be deleted. The other 63 articles have been rejected as inconsistent with inclusion and exclusion criteria. Notably, 39 out of 65 studies were not conducted in one of the OECD countries with a universal, tax-funded, and single-payer healthcare system; the target of 11 papers was inconsistent with the target of the present review; 13 out of 65 don’t imply tangible intervention. In conclusion, the authors include only eight articles^9,45–51 out of 561 initial results according to the 2-steps evaluation.

Table 2.

Chronological search sequence 2.

Keywords	Number of articles
(outpatient)	54.378
(outpatient)AND(waiting)	842
(outpatient)AND(waiting)AND(time*)	561

This allowed for two additional articles to be included in the review.^42,44 Hence, the number of articles included in the review is 41.

To conclude, the authors also searched other scientific literature for the discussion of the results Figure 1.

Figure 1.

PRISMA Flowchart for literature review.

Findings

Table 3 presents the characteristics of the articles included in the review as described in the authors’ methods (Chapter 2.1).

Table 3.

Characteristics of the articles included in the review.

Reference	Country	Type of study	Main policy implemented	Level	Relevant outcome	Cost analysis	Clinical outcomes analysis
Aiken et al. (2021)	UK	Retrospective cohort study (case-control)	Telemedicine	Macro	Waiting time to treatment declined from 10.7 days (SD 19.9) to 6.5 days (SD 13.5) due to telemedicine approach (p < 0.001)	No	Yes
Álvarez-Dobaño et al. (2022)	Spain	Prospective cohort study (before-after)	Telemedicine	Micro	E-consultations prevent unnecessary actions, foster the management of health problems at the appropriate level of healthcare, and could reduce costs	Yes	No
Appleby et al. (2001)	UK	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Waiting list reduced from 57 to 13 weeks	No	No
Azogil et al. (2019)	Spain	Randomized controlled trial	Telemedicine	Micro	Significant reduction between the different study groups (specialist consultation, bookings, etc.) of waiting times	No	No
Baker et al. (2005)	UK	Randomized controlled trial	Task shifting	Micro	Waiting time to the appointment decline from 51 days (IQR 40–69 days) to 43 days (IQR 34–58 days) in practice-based clinics (p = 0.001)	No	Yes
Belthur et al. (2003)	UK	Prospective cohort study (before-after)	Task shifting	Micro	Reduction in median waiting time for non-urgent simple conditions from 72 weeks to 5 weeks in 3 years. Reduction in median waiting time for orthopaedic consultations from 17 weeks to 7.5 weeks in 1 year	Yes	No
Brennen et al. (2020)	Australia	Prospective cohort study (before-after)	Patient-focussed bookings approach	Meso	Not keeping initial appointment reduced from 23.64%, to 13.04%, with a 14.74% reduction in waiting times	No	No
Bungard et al. (2009)	Canada	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Relevant reduction of both the time to initial consultation (from 71+/−45 days to 33+/−19 days) and the time to definitive diagnosis (from 120+/−86 days to 51+/−58 days) (p < 0.0001)	No	No
Davies et al. (2011)	Australia	Prospective cohort study (before-after)	Patient training programs	Micro	Waiting times reduced for both pain units: From 105.6 to 16.1 weeks at the first and from 37.3 to 15.2 weeks at the second	Yes	Yes
Deslauriers et al. (2017)	Canada	Cross sectional survey (descriptive)	Guidelines implementation and/or clinical pathways change	Meso	Shorter waiting times (p = 0.008)	No	No
Devbhandari et al. (2007)	UK	Case report	Guidelines implementation and/or clinical pathways change	Micro	Most patients received outpatient consultation in the recommended time (2 weeks, IQR 0–5) even though there was a delay in starting all forms of treatment	No	No
Harding et al. (2013)	Australia	Prospective cohort study (before-after)	STAT	Micro	Triage ensured rapid service for a small number of urgent referrals but made little difference in the waiting time for most patients	No	Yes
Harding et al. (2017)	Australia	Prospective cohort study (before-after)	Task shifting	Meso	93% reduction in waiting days from referral to appointment for a specialist visit	Yes	Yes
Harding et al. (2018)	Australia	Randomized controlled trial	STAT	Meso	Intervention reduced waiting time to first appointment by 33.8% (p = 0.001)	Yes	Yes
Harding et al. (2020)	Australia	Randomized controlled trial	STAT	Meso	Reduction of median waiting time from 42 days (IQR 19–86) before surgery to 31 days (IQR 14–55) after surgery	No	No
Harding K.E. and Bottrell J. (2016)	Australia	Prospective cohort study (before-after)	STAT	Micro	Reduction of median waiting time from 18 days to 14 days (p < 0.01)	No	No
Hawtin et al. (2010)	UK	Prospective cohort study (before-after)	Open access model	Micro	Reduction of median outpatient appointment waiting time from 14 to 9 days (p < 0.0001)	No	No
Henry et al. (2021)	UK	Cross sectional study	Lean approach	Micro	Reduction of median waiting time from 78.9 days (IQR 37–163) to 36.2 days (IQR 8–175) (p < 0.001)	Yes	No
Hernández et al. (2020)	Spain	Retrospective cohort study (case-control)	Telemedicine	Micro	Significant reduction in days between receipt of assessment request and first outpatient visit from 180 to 68 days	No	Yes
Johannessen et al. (2018)	Norway	Retrospective cohort study (case-control)	Lean approach	Meso	Reduction of median waiting time from 162±69 days (IQR 74–312) at baseline to 52±10 days (IQR 41–74) with p < 0.01	No	No
Khan et al. (2015)	UK	Prospective cohort study (before-after)	Telemedicine	Meso	Reduction waiting times reduced from a median of 14 to 4 weeks	Yes	Yes
Kirkwood et al. (2006)	Australia	Prospective cohort study (before-after)	Task shifting	Micro	Reduction of median waiting from 115 days (IQR 23–268) to 21 days (IQR 9–43) for initial clinic appointment	Yes	Yes
Lee et al. (2011)	UK	Retrospective cohort study (case-control)	Task shifting	Micro	Reduction of median waiting time for rehabilitation from 21 to 15 weeks	No	No
McGeoch et al. (2015)	New Zeland	Retrospective cohort study (case-control)	Task shifting	Meso	The decreased demand for secondary care treatment has resulted in a marked reduction in waiting times, from 11–12 to 3–4 months in 6 years	No	No
Morris et al. (2011)	Australia	Prospective cohort study (before-after)	Telemedicine	Micro	Significant decrease in no-shows, inappropriate referrals, and waiting list length	Yes	No
Nisar et al. (2019)	UK	Retrospective cohort study (case-control)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of median waiting time from 12 to 2.5 weeks	Yes	Yes
Payten et al. (2020)	Australia	Prospective cohort study (before-after)	Task shifting	Micro	Reduction of waiting times by an average of 277 days (CI = −299.1522; −255.2901) compared with the usual pathway	Yes	Yes
Rochester et al. (2008)	UK	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of median waiting time from 22 to 7 weeks	No	No
Rymaszewski et al. (2005)	UK	Cross sectional study	Task shifting	Micro	Reduction of waiting time from 182 to 90 days	No	No
Sakowska et al. (2019)	Australia	Prospective cohort study (before-after)	Public-private partnership	Meso	Reduction of waiting time from 92 to 73 days	No	No
Salam et al. (2006)	UK	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of median waiting time from 121 to 47 days	No	Yes
Saxon et al. (2018)	Australia	Prospective cohort study (before-after)	Task shifting	Micro	Reduction of median waiting time from 82 to 35 days	No	No
Schachter et al. (2013)	Canada	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Meso	Reduction of median waiting time from 98 to 64 days (p =<0.001)	No	No
Schoch ft ar. (2012)	Australia	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of patients waiting for their first appointment by 66% and of waiting time from referral to first appointment by 87%	No	No
Sekhri et al. (2006)	UK	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of median waiting time from 97 to 43 days	No	No
Simões et al. (2017)	Portugal	Case report	Freedom of choice	Macro	Mixed results (waiting times decreased in some hospitals but increased in others)	No	No
Stafford et al. (2020)	UK	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Mixed results (high variability of the average number of days from referral into the pathway among the teams included into the analysis)	No	Yes
Stainkey et al. (2010)	Australia	Prospective cohort study (before-after)	Guidelines implementation and/or clinical pathways change	Micro	Reduction of median waiting time to make a specialist appointment from 8 to 2 years	Yes	No
Stuart et al. (2005)	UK	Retrospective cohort study (case-control)	Guidelines implementation and/or clinical pathways change	Meso	Reduction of waiting time from a peak of 35 to an average of 3 weeks	No	Yes
Thomas et al. (2003)	UK	Randomized controlled trial	Guidelines implementation and/or clinical pathways change	Meso	Reduction of median waiting time for referrals from 24.3 to 13.3 weeks (p < 0.05)	Yes	Yes
Willis et al. (2011)	UK	Prospective cohort study (before-after)	Open access model	Micro	Reduction of median waiting time for first appointment from 54 to 9 days	No	No

The main policies identified were: guidelines implementation and/or clinical pathways change (14), Task shifting (9), Telemedicine (6), STAT¹ (4), Lean approach (2), Open access (2), Public-private partnership (1), Patient training programs (1), Patients-focused booking approach (1) and Freedom of choice (1).

The majority (24 out of 41, 58,5%) of the articles included in this review were based on prospective cohort studies. Only 5 studies (12%) analysed the impact of interventions through randomised controlled trials. This represents a key-limitation in collecting evidence on policies having a positive effect in reducing waiting times. 26 studies (63%) described interventions implemented at micro level, thus in a single hospital or clinic. Only 2 articles (5%) focused on policies introduced at macro-level. Firstly, this may reflect a great heterogeneity and variability among providers in enacting interventions on waiting lists, delegated to local organisations. Secondly, this may suggest that results of policies implemented at national level are poorly documented in the literature. As discussed below, extension of the review to national reports may potentially enrich the findings of this study.

The prevalence of articles from anglo-saxon countries, such as Australia, Canada, New Zealand and the UK (36 out of 41 studies) may suggest that these are particularly sensitive to the issue of long waiting times for outpatient care.

The majority of studies (28 out of 41) did not include data on intervention-related costs. Similarly, 26 out 41 articles did not illustrate any clinical variable to show the impact of the policies on clinical outcomes.

Discussion

This literature review highlighted several key areas and policies that health systems have focused on to improve the management of waiting lists. It is difficult to prove the causality of the relationship between the implemented policies and the reduction in waiting time as in none of the articles was performed a multifactorial analysis. Indeed, the poor utilisation of randomised case–control studies besides the difficulty in isolating other variables potentially affecting waiting times hindered the identification of clear causation between a given policy and its effects on waiting times. The complexity of the interactions and the potential feedback effects between supply and demand make it particularly challenging to isolate and control the multiple variables affecting waiting times. For this reason, most studies on waiting time governance draw on pre-post observations and consider a given policy as the only independent variable. Despite its practicality, this approach may be misleading and generate biased results. Considering the limitations and difficulties of generalisation, it is possible to hypothesise the existence of a causal relationship between the policies identified and waiting times.

Furthermore, there was large variability in the impact measures the studies applied, which makes a comparison of their effectiveness difficult. This reflects the lack of homogeneity in the definition and measurement systems for waiting time across OECD countries.^42,52

Although the implementation of a single policy, rather than a group of combined measures, was rarely observable in the articles included in the review, in most cases a main intervention for each article was clearly identifiable. When this was not possible, the authors decided to group some interventions in a macro-policy called “guidelines implementation and/or clinical pathways change”. The difference with respect to the other policies highlighted in this study lies in the fact that, in the studies classified with “guidelines implementation and/or clinical pathways change”, it was not possible to identify a main policy impacting the system. All the measures implemented have been contextual and not consequential. In particular, the interventions considered into this macro-policy are maximum waiting time, waiting list update, triage, guidelines for triage, no-show policy, and rapid access clinic. Studies classified under this macro-policy included 2 or more measures listed above.

Nonetheless, the authors identified 15 policies for the governance of ambulatory waiting time and grouped them in three main categories (Table 4):

1. Generation of supply capacity. Policies that may strengthen or expand the supply of health services, such as:

o Lean-type approach

o Maximum waiting time

o Task shifting

o Public-private partnership (PPP)

2. Control of demand. Policies that may reduce, control, or manage the demand for health services, such as:

o Specific Timely Appointment for Triage (STAT)

o Waiting list update

o Triage

o Guidelines for triage

o No-show policy

o Patient-focussed bookings approach

o Patient training programs

o Freedom of choice

3. Mixed interventions. Policies that may impact on both the demand for and the supply of health services, such as:

o Rapid access clinics

o Open access

o Telemedicine

Table 4.

Policies, main characteristics, and mechanism of intervention.

Policy	Description	Intervention mechanism
Lean-type approach	A set of management approaches that optimize resources, people, and energy.	Supply capacity
Rapid access clinics	The creation of rapid-access clinics for conditions in which time-to-treatment initiation seems to be crucial (e.g., cancer) may reduce waiting time for at-risk patients. The implementation of specific clinical paths involves expanding the supply. Rapid access clinics should filter the demand for health services and allow fast accessibility only to those patients who really need timely care.	Mixed
Specific timely appointment for triage (STAT)	A scheduling model for access and triage, implemented in some Australian healthcare services.⁹ This approach is indicated for services providing multiple appointments for the same patient, such as rehabilitative care.	Demand control
Waiting list update	Periodical updates may be an effective way to remove redundant appointments from waiting lists, reduce the probability of no-shows, and monitor the demand.	Demand control
Triage	Prescribers have a significant role in controlling the demand. They filter the demand for healthcare services by prescribing only necessary treatments. Priority classes are aimed at addressing the demand and granting faster access to patients needing urgent care. Prioritization can be considered as an equity-based approach, as it reduces waiting times for patients needing an urgent procedure. Different waiting times are allocated to patients with dissimilar needs. Priority classes are included in GP referrals or are directly assigned by specialists when they receive details of the referrals.	Demand control
Guidelines for triage	Inappropriate referrals increase the demand for unnecessary services. Referral and triage systems may be supported by common guidelines to standardize the referral process and to minimize excessive variability in the assignment of priority classes. This measure may reduce inappropriate referrals, potentially lowering the demand for unnecessary procedures.	Demand control
Maximum waiting time	Public and accredited private hospitals/clinics are mandated to deliver services within the maximum waiting time. This measure may incentivize providers to expand the supply of healthcare services to fulfil this mandate. A maximum waiting time is defined by health authorities for all or some procedures. When patients are not treated within the maximum waiting time, they have the right to access another hospital or private provider. This policy is often coupled with prioritization measures to set different maximum waiting times for each priority class. Health authorities may set targets for providers (e.g., 90% of services must be provided within the defined maximum waiting time). Some countries levy penalties on hospitals that fail to fulfil the targets, while others have defined no sanctions.	Supply capacity
No-show policy	Patients who fail to keep an appointment generate further demand, as they may seek a new consultation. Moreover, provider productivity may decrease due to unutilized slots. Specific policies can be implemented to mitigate no-shows. For example, penalties can be charged to patients who do not keep their appointment. Also, automatic reminders can be sent to patients before the date of the planned procedure.	Demand control
Role substitution/task shifting	Non-medical health professionals may carry out clinical services usually managed by physicians. Task shifting might expand the supply of healthcare services, but quality of care remains a concern.	Supply capacity
Open access	Management strategy first described and implemented by Murray and Berwick;⁵³ it is based on the principle “do today’s work today”. Open access is a scheduling approach in which all patients receive an appointment on the day they call (or in the next few days), not considering triage classes and prioritization. Open access strikes a balance between demand and supply, avoiding the creation of a backlog. It is based on supply expansion and demand control.	Mixed
Telemedicine	A set of initiatives such as eReferrals, tele-consultations, and eTriage. Technological innovations might reduce waste and delays in preparation times, referral management, and booking procedures. Also, they can potentially control the demand and minimize unnecessary treatment.	Mixed
Public-private partnership	The public sector can contract with the private sector to increase the supply of services offered by private providers on behalf of the public system. This can be done through public-private-partnerships (PPPs) or accreditation.	Supply capacity
Freedom of choice	Patients are free to choose the provider to whom they seek health care, inducing a potential competition among providers, based on quality and accessibility to services.	Demand control
Patients-focused booking approach	A booking system where patients have freedom of choice on providers, day and time for their outpatient appointments may reduce not attendance rate.	Demand control
Patient training programme	The improvement of health literacy may induce patients to self-manage their conditions, potentially reducing the demand for health services.	Demand control

Among the policies identified in this review, guidelines implementation and/or clinical pathways change is the most employed strategy described in 14 out of 41 studies (34% of articles). This can certainly be explained in how difficult it is to apply a policy without changing the overall picture. Furthermore, observing the results of the studies, the choice to modify various aspects of taking care of patients appears to be efficient from an organisational point of view and with significant effects on the reduction of waiting time. However, the authors underline that the prevailing absence of cost and health outcome analysis leads to strong limitations in the evaluation of such interventions.

Task shifting was described as the main intervention in 9 studies (22%). GPs or allied healthcare professionals (nurses, physiotherapists, speech therapists etc.) participated in tasks traditionally performed by specialists. There are two, often combined, main approaches to task shifting:

1. Non-specialist health professionals triage the patients and filter the demand.

2. Non specialist health professionals provide care or perform examination.

Change in the task mix may allow specialists to focus on more complex cases, while allied healthcare professionals filter the demand and provide care to less severe patients. This is particularly relevant in a scenario of physician shortage, as time savings for specialists lead to potential supply expansion.

In most cases, the active engagement of all professional groups is emphasised and described in detail, highlighting the relevance of solid cooperation and change management in task shifting interventions.

Task shifting approach seems to be common in Anglo-Saxon countries. However, this may be not easily applied in other countries, where legal barriers, cultural and professional silos hinder a less rigid task distribution.

The implementation of telemedicine to shorten waits for outpatient care was described as the main intervention in 6 studies (14,5%). Telemedicine, per se, is a mode of care delivery. For its peculiar characteristics, the authors have decided to consider it as a separate policy. Indeed, it may have a more direct impact on waiting lists by speeding up processes for cases that do not require in-person care, improving accessibility and, thus, reducing the rate of non-attendance. Moreover, it can be combined with other policies, such as triage, list audit and task shifting. Telemedicine provides an innovative tool for supply expansion, particularly in ambulatory services where physical examinations or instrumental diagnostic procedures are not performed. The dramatic increase in the adoption of telemedicine since the beginning of the COVID-19 pandemic has demonstrated its potential capability to substitute face-to-face outpatient consultations. It will likely become even more a part of routine practice as healthcare organisations seek to recover the backlog of services postponed during the current pandemic. In the identified papers, telemedicine include:

• Doctor to patient e-consultations:^13,53 a service usually provided in-person by a specialist with the patient and now delivered from remote.

• E-referrals:⁵³ clinical data gathered by allied health professionals and referred by specialists through an online data sharing platform.

• Doctor to doctor e-consultations:⁵⁴ a remote consultation between a specialist, the GP and, if necessary, the patient, to provide guidance to the GP and avoid unnecessary demand to in-person specialist care.

• E-triage: (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3417931/pdf/prom-2-151.pdf) remote filtering and auditing of demand conducted by a healthcare professional with a patient.

These approaches have diverse levels of technological complexity. Digital literacy of patients and healthcare professionals is a key-point to be considered when implementing various kinds of telemedicine solutions.

Specific Timely Appointments for Triage (STAT) is an approach described in four out of 41 articles. As mentioned above, this is a method implemented in Australia, which includes measures such as new scheduling system, minimization of touch points and triage. Given its precise protocol and the findings in literature, the authors have decided to distinguish it from guidelines implementation and/or clinical pathways change. STAT, as extensively described in the handbook published by the initiator of the method may be effective in reducing waiting times in services provided to most patients over more than a single occasion of service, so that there is some flexibility in how the service is delivered. However, “It is unlikely to be the answer to long waiting lists in a diagnostic service, for example, where every patient attends for a single standard 30-min appointment”.¹⁵

An additional point to be considered is the limited scenario of implementation. Indeed, STAT has been introduced only in Australia. To further investigate its impact on waiting time, this method should be implemented also in different contexts.

Lean is described as the main intervention in 2 articles (5%). Lean methodology is a well-known approach, aimed at optimising people, resources, effort, and energy of an organization. Its implementation in the healthcare sector is not a novelty.⁵⁴ Despite its promising impact, the effects on waiting times should be further investigated.

Open-access model was the main intervention in 2 articles (5%). By approach, open access has a significant positive effect on waiting time. However, the implementation of a “same-day” service is a challenge for many healthcare organisations. Main risks include the economic sustainability of the service and the potential unpredictability of demand, which may generate unexpected requests able to break the delicate balance between daily demand and daily supply.

Other policies described in one article each are: freedom of choice, public-private partnership (PPP), patient-training programme and patient-focused booking approach. The limited findings for the impact of these interventions on waiting time do not allow the authors to make specific hypotheses. The authors believe that, among these policies, the idea of reducing demand by increasing patient health literacy seems particularly interesting. Further investigations should be pursued to better understand the relationship between health literacy, demand and waiting time for health services.

Only 13 (32%) papers included cost analysis, mainly specifying the economic effort in the scenarios before and or after the intervention. Major savings are obtained in some measures involving task shifting. This showed that, assuming an adequate level of care provision, the employment of allied healthcare professionals in tasks traditionally performed by specialists, may represent a cost-effective solution to reduce waiting times. In papers presenting interventions that might lead to cost increase, such as open and rapid access services, costs are not analysed. When considering interventions to reduce waiting times, costs should be given more attention, to guarantee long-term sustainability of the policies. This is even more relevant in periods of budget constraints and shortage of resources.

Data on clinical outcome are analysed in 15 (36,5%) papers. In most cases, adverse events were monitored to understand whether the intervention decreased the quality of care, in comparison with the former scenario. This was particularly important in those interventions involving task shifting, where the maintenance of adequate standard of care, while less experienced professionals are employed, represents a major concern.

In a limited group of cases, interventions did not have a negative impact and improved both the waiting time and the clinical outcome.

Conclusions

Despite the issue’s relevance in many countries, the current literature on ambulatory waiting time governance is scarce and mainly based on observational studies. Randomised controlled studies are rare. Most articles in the present review focused on local interventions implemented in a single hospital or a group of clinics. Limited data transparency and availability may have hindered the identification of policies and the analysis of critical cases. Moreover, the decision of the authors to include only articles written in English could have affected the range of approaches to the governance of ambulatory waiting times identified.

Some policies may have been implemented, even at macro level, without a study being conducted and published in scientific journals. More information on the implementation of policies for the governance of ambulatory waiting times may be obtained from national or regional health authorities either directly through interviews and surveys or indirectly through consultation of official websites and reports. Moreover, the distribution by country may reflect a particular sensitivity to the issue of long waits for outpatient care. In-depth research with a focus on countries such as Australia and the United Kingdom may be warranted. Furthermore, greater transparency of data on clinical outcomes and costs is desirable, to assess the actual cost effectiveness of different policies.

The limited results from this literature review suggest the need for future research extended to other sources and methods. It is desirable that more policies are analysed in studies to allow the academic community and decision makers a more accessible and transparent comparison of different governance approaches through real-world data collected on a large scale.

The limitations of this review notwithstanding, a set of policies for the governance of ambulatory waiting times implemented in OECD countries with universal, tax-funded healthcare systems was identified. This provides a starting point for further analysis and a useful tool for academics, healthcare managers, and policymakers.^18,55–66

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Francesco Amigoni

Elena Maggion

Note

Appendix

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