The Effectiveness of Different Patient Referral Methods on Waiting Times for Adults Needing Elective Surgery - Systematic Review

Background: Long waiting times and lengthy queues for elective surgery are common to many publicly funded health systems. Primary care practitioners play a major role in determining which patients are referred to the consultant surgeon and might represent an opportunity to improve this situation. With conventional methods of referrals, surgery clinics are often overcrowded with non-surgical referrals and surgical patients experience longer waiting times as a consequence. Improving the quality of referral communications should lead to more timely access and better cost-effectiveness for elective surgical care. This review summarises the research evidence for interventions within the scope of primary care referral methods in the surgical care pathway that might shorten waiting time for elective surgeries. Methods: We searched PubMed, EMBASE, SCOPUS, Web of Science and Cochrane Library databases in December 2019 to January 2020, for articles published after 2013. Eligibility criteria included major elective surgery lists of adult patients, excluding cancer and cancer related surgeries. Randomised trials and non-randomised controlled studies were eligible. The main outcome variable of interest was waiting time for the elective surgery. The quality of evidence was assessed using ROBINS-I, AMSTAR 2 and CASP, as appropriate to the study method used. The review presentation was limited to a narrative synthesis because of heterogeneity. The PROSPERO registration number is CRD42019158455. Results: The electronic search yielded 7543 records. Finally, nine articles were considered as eligible after deduplication and full article screening. The eligible research varied widely in design, scope, reported outcomes and overall quality, with one randomised trial, two quasi-experimental studies, two longitudinal follow up studies, three systematic reviews and one observational study. The included research showed that patient triage and prioritisation at the referral stage improved timely access and increased the number of consultations of surgical patients in clinics. Conclusions: The available studies included a variety of interventions and were of medium to high quality, showing that methods to improve the management of primary care referrals of patients for elective surgery can reduce waiting times and shorten waiting lists for elective surgery for adults. Results from the 12-15 months follow-up of these patients that referrals for cataracts were poorly targeted, with almost half of all patients reviewed in the clinic not proceeding to surgery. The standardized referral templates may facilitate the improvement of referral pathways and waiting


Introduction
Awareness of growing waiting times in health care is not a recent phenomenon and the challenges of waiting times and waiting lists has been subject to a wide variety of health service research. Presentation of multiple comorbidities and complex clinical conditions are a direct result of the advancing biological age and increasing life expectancy of populations over recent decades (1,2). As a consequence, demand for surgery is growing and has exceeded the capacity of hospital services (2), leading to long waiting times and lengthy queues in many publicly funded health systems (3)(4)(5)(6).
Lengthy waiting lists cause distress not only to the patients who are waiting, but also to service providers. This has led to waiting times for elective surgeries becoming a major policy concern in many countries, especially those with health systems operated with public funds (7,8).
Referral methods play a major role in providing appropriate care for patients in many health care systems. However, unlike in a private system, choice of medical specialist and public hospital are limited in the public referral systems with most public hospitals preferring to accept patients within their geographical catchment areas. Primary care practitioners are usually the gate keepers who provide access to specialised care in health systems where referral systems are restricted. In a typical surgical care pathway, primary care practitioners have a major role in determining which patients are referred to the particular surgeon or surgical clinic. However, with long waiting times being a common area of complaint for both patients and general practitioners, they sometimes have different perceptions of the value of the referral process (9). Some countries practice fast-track referrals in certain elective surgical care pathways (10,11), but a lack of clear speci city of the referral criteria has been repeatedly highlighted. Long waiting times for surgeries are also associated with higher risks of serious complications and death, especially among adults (12,13). Amongst the research that has been done to improve referrals, positive decision support systems incorporating clinical guidelines and the collaboration of specialists have been found to be effective for general practice (14). Reassurance of the necessity of the referral to the patient is also important, because the nonattendance of some patients at surgery appointments is a major factor in prolonging waiting times for them and for other patients (15).
Waiting times are a key performance indicator for many healthcare systems, used to encourage improved performance in healthcare institutions, with the aim of delivering high-quality care without unnecessary delay (7). Many patients who wait a long time for their surgery are more likely to report with problems, which have been associated with reduced quality of life (16). Prolonged pain, discomfort, anxiety and disability are initial consequences for waiting patents. Alongside these impacts, patients in lower socioeconomic categories have reported worse outcomes in quality of life parameters when they are assigned to long surgical queues (17). Economic evaluations have also found that the negative impact of patient waiting time on cost-effectiveness may be non-reversible (18).
Despite increased funding in recent years, the demand for many elective surgeries exerts a substantial and growing challenge (5). Furthermore, even though many approaches have been attempted to shorten waiting times, these have not led to improvements or reductions in waiting times for elective surgeries (19). The ability of hospitals to improve performance has often been restricted due to resource constrains, with the supply of surgery not accounting for the increase in demand. The COVID-19 pandemic has added considerably to this challenge, leading to pressure on healthcare institutions to move patients out of hospital (20). This is against a background that earlier efforts to reduce waiting times for elective surgeries (21) and the setting of associated targets adversely affected the quality of other healthcare services in some health systems (22).
Waiting lists are considered as a non-price rationing mechanism for coping with excess demand (23). A large amount of literature is available on methods and strategies to reduce patient waiting times for elective surgeries, and health systems interventions are often multifactorial and multidimensional, making it di cult to measure their effect. Recently, research focus has shifted to individual strategies. Studies have been done of referral systems and policies targeted at reducing waiting times for elective surgeries, but rigorous evaluations are limited. A Cochrane Review published more than a decade ago analysed different approaches to improve referral systems to increase the e ciency and effectiveness of patient care (24) . A more recent systematic review of guidelines for elective referrals of adult patients to surgical specialists concluded that these improve the appropriateness of care (25). However, a more up-to-date review is needed to help healthcare systems develop comprehensive protocols to establish effective and e cient referral systems in surgical care pathways (26). The objective of this paper, therefore, is to review and summarise recent research evidence relevant to primary care referral methods as part of the surgical care pathways that aimed to shorten waiting time for elective surgeries in adult patients.

Methods
This paper is based on one of the sub-reviews in a major systematic review which takes a holistic approach to summarise policies, strategies and interventions that might reduce long waiting times for elective surgeries. We have conducted and reported the review according to PRISMA statement (27). The PRISMA ow diagram for the major review is attached as Additional le 1. The broad scope allowed the inclusion of many existing research papers, relevant to various aspects of the surgical care pathway and waiting times. The review was registered in PROSPERO (CRD42019158455).
During the article selection process for the full review, we grouped different methods and strategies as they appear in eligible studies. For this paper, at the nal stage of inclusion, we focused on studies pertaining to the management of referral systems with an intention to reduce long waiting times for elective surgery.
Data sources: The electronic databases of PubMed, EMBASE, SCOPUS, Web of Science and Cochrane Library were selected for the search. Search terms were decided using MeSH headings and keywords for the scope of the full review. Major types of elective surgeries were searched for broader inclusion. After performing pilot searches, a detailed search list was nalised. The search strategy combined with three sets of search terms. The searches were run from 14 th December 2019 to 7 th January 2020 to include relevant articles published from January 2014 to December 2019 without language restriction. The search strategy used for PUBMED is presented in Additional le 2. We also checked the reference lists of included articles for additional relevant citations.
Inclusion and exclusion criteria: All types of literature published as a full article were included if they reported eligible studies. This includes original research published in journals, reports, editorials and literature reviews from the healthcare sector, governments and other related sectors. Where a study was based on outpatient departments, the referral intervention needed to be targeted at reducing the patient waiting time for the elective surgery. We included a range of study designs, with a design-speci c assessment of risk of bias (28), because health system interventions are often tested in quasi-experimental studies, rather than randomised trials, due to the complexity of the approaches being investigated and the diversity of outcome measurements, and some are investigated in observational studies. Our main outcome variable of interest was waiting time and all quantitative and qualitative reporting associated with proxy variables of change (e.g. patient numbers, e ciency, and number of surgeries) were considered for data synthesis. Simulation and modelling studies were excluded because these might not provide a reliable guide to what would happen in real world scenarios.
Eligible participants were adult patients (≥18 years) who had been referred to a surgical clinic for major elective surgery. Patients having emergency surgery or paediatric surgery were excluded, as were those awaiting cancer or cancer-related surgery. Although most eye surgeries are considered as minor surgeries, we included referrals for cataract surgery because this is one of the longer surgical lists commonly reported in many countries (29,30).
Article selection and data extraction: As the rst step, the title and the abstract of the retrieved citations were checked by one reviewer (DR) to select relevant articles. Articles that were potentially eligible based on their title or abstract were retrieved in full and assessed for eligibility and relevance.
Each potentially eligible article was discussed with the second reviewer (MC) and agreement was reached on inclusion or exclusion.
Quality assessment: The validity of the results of any systematic review of a health systems intervention depends on the methods used in the included studies (which may have used different designs), rather than universal experimental criteria (31). Considering the variety of study designs identi ed for this review, we used assessment tools that were relevant to the included study design. The Cochrane ROBINS-I (Risk Of Bias In Nonrandomised Studies -of Interventions) (32) provides a thorough assessment of risk of bias for non-randomized intervention studies. The AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) is a critical appraisal tool for systematic reviews of randomised and non-randomised studies of healthcare interventions (33). Finally, the CASP tool (Critical Appraisal of Skill Programme) was used to evaluate the observational studies (34). Both review authors agreed on the quality rating for each included study.
Synthesis of results: Meta-analysis was not applicable for this review, because of the heterogeneity in study designs and variability in how the outcome of interest was measured. Instead, we did a meta-synthesis with narrative analysis.

Results
The article screening process is shown in the PRISMA ow diagram ( Figure 1) and the PRISMA checklist is available as Additional le 3. The electronic search in the ve bibliographic databases yielded 7543 records for the full review. This was reduced to 5346 after deduplication using EndNote citation management software. During the title and abstract screening process, 362 citations were deemed potentially eligible for the full review. Of these, 196 relevant citations were obtained from full article screening, and, among these, 105 simulation and modelling studies were rejected. After grouping the citations into different strategies for reducing waiting time, three systematic reviews and six original studies (one randomised trial, two quasi-experimental studies, two longitudinal follow up studies and one observational study) published after 2013 were included for the nal analysis of this sub-review, given their focus on interventions relevant to managing patient referrals as a way to reduce waiting time for elective surgery. Three of the original studies were from Canada, with one each from USA, Israel and Australia; ve of these investigated speci c institution-based referrals. The summary of nine studies included is shown in Table 1. Summary of included studies: Nine studies were included in this review. The characteristics of the included studies are summarised below and further details are given in Tables 2 and 3.
Of the three systematic reviews, one was a scoping review to describe strategies to reduce waiting times and the other two summarised existing evidence on increasing patient ow in elective care pathways.
Starting with the original studies; Damani et al. (35) reported a quasi-experimental study comparing a historical cohort (2397 patients recruited from 1 June 2011 to 1 June 2012) and a prospective cohort (2282 patients from 1 September 2013 to 1 September 2014) to assess the effects of a single-entry model (SEM) of referral to the next-available surgeon for total joint replacement surgeries in Canada. The results showed that the variability of waiting times among surgeons was reduced by 3.7 and 4.3 weeks for hip and knee replacements, respectively and there was a 5.6% increase in patients operated within the benchmark period.
Gabbay et al. (36) reported a quasi-experimental study with a historical and prospective study approach to evaluate the performance of a referral triage system through 2015 in Israel. They found that 44.4% of cancelled surgeries could have been prevented by a preoperative clinic visit and concluded that using a pre-operative triage system in referral letters for scheduling surgery could minimize both patient time and physician time prior to surgery.
Coyle et al. (37) reported a pragmatic, blinded, randomized trials with 227 consecutive eligible participants with an elective lumbar condition who were referred for consultation with a spine surgeon in Canada. Reprioritizing patients with a questionnaire reduced wait times for consultation appointments for patients who were eventually deemed to be surgical candidates. The odds of seeing a surgical candidate within the acceptable time frame of 3 months were 5.4 times greater for the intervention group. The authors concluded that it may be worth adding simple questionnaires to clinical care practices to better triage these patients on waiting lists. Risk of bias in included studies: All included studies complied with the eligibility criteria for this review. The quality of evidence in the included articles were measured using the most appropriate of three tools: ROBINS-I Cochrane risk of bias tool (5 studies); AMSTAR 2 (3 systematic reviews) and CASP for cohort studies (1 study).

ROBINS-I:
Of the ve studies, lower risk of bias in overall was found for the randomised trial (37). The other four studies were assessed as having medium risk of bias overall (35,36,39,40). The details of the ROBINS-I evaluation are shown in Figure 2 and Figure 3.
One study used randomisation of participants (37). The remaining studies had low to moderate bias due to confounding for baseline characteristics of the two groups (35,36,39,40). Since all patients in the selected cohort were included in all studies (without sampling), none of the studies had signi cant bias in their selection of participants for the study. In most studies, patient data were extracted from regular administrative records, except for one study which was rated with moderate risk of bias due to missing data (40). Bias in classi cation of the intervention was low in all studies, since the interventions were implemented as pre-test post-test design methods, where participants were not aware of the prioritisation scoring at the clinics. The outcome variable included in many studies is a time measurement associated with the waiting time, which was considered to be unbiased.
AMSTAR 2: One of three includes reviews was rated as high quality overall (43) and the other two were rated as moderate (35,41). All review protocols had been registered and the reviews presented adequate searches for literature in relevant databases. All three reviews had assessed risk of bias in their included studies and considered this when interpreting the results. Estimates of meta-analysis and assessment of publication bias was not applicable for any of the reviews. Our ratings for each domain for the AMSTAR 2 tool are shown in Table 4.
CASP tool: A single study was assessed using the CASP checklist and the authors had reported important confounding factors for the study (38). Certain types of patients should be identi ed early in the referral process.

ROBINS-I Moderate
*Risk of bias in intervention studies were assessed using ROBINS-I tool and observational study was assessed with relevant CASP checklists. Overall quality measurement was reported considering the all risk of bias domains for the particular research.   Referral guidelines should be su ciently standardised to allow those involved in making decisions about the referred patient to judge the appropriateness of the referral and to undertake a more detailed objective analysis of their needs. Referral letters which were unclear or not su ciently informative led to dissatisfaction at surgery clinics (36,39). Tracing potential patients who required surgical neurosurgical procedures with a simple questionnaire was effective at the referral stage (37). It also improved timely access for surgical patients, while allowing non-surgical patients to be consulted at other clinics. The bene ts of more accessible services for patients needing elective surgery through direct referrals have also been noted in the two systematic reviews (41,43). Increasing patient opportunities with minimal referral restrictions were also effective in reducing waiting times for individual elective patients (45).
There is uncertainty about whether the effects of these methods are similar for all specialties. For example, although preferential scheduling to prioritise low-risk patients for bariatric surgery was effective in fast-access for the surgery (46), it is uncertain if this approach would be effective for other conditions and types of surgery.
The proper reporting and adequate clinical assessment of the patient at their last consultation prior to surgery is important for informing the decision about whether to perform or cancel the elective surgery (47) and can prevent late cancellations of patients who have become unsuitable for surgery.
Similarly, triaging patients for cataract surgeries at the referral stage with an informative referral note reduced cancellations on the day of the surgery (36). The identi cation of patients who need longer pre-op optimisation is important (46), to avoid allocating them a space that could be used for another surgery patient and to improve the e ciency of patient ow. There is supportive evidence to conclude that primary care physicians were at least as knowledgeable about most perioperative preparations for potential surgical patients as anaesthesiology residents, and this knowledge could be invested in achieving appropriate patient referrals (48).
In addition to clinical reporting, adding patient preferences regarding a particular surgeon or surgery schedule to their referral notes was associated with improved access of patients to surgical care (38), but this comes with the challenge that patient preferences are variable due to many reasons.
Identifying self-reported patient concerns were important and were associated with the willingness to undergo surgery for arthroplasty in elderly patients (49). Furthermore, a systematic reviews reported a myriad of factors that patients consider when choosing their surgeons and when deciding where to have their surgery (50).
Where there are multiple queues for surgical lists at the same surgery clinic, targeted patient referrals have had variable impacts on waiting times across different specialists. The application of single entry models (SEM) while pooling referrals enabled patients to see the next-available surgeon for their procedure and improved timeliness and e cient patient ow (35). The systematic review for SEM also showed a consistently positive impact on access-related variables for referral of patients to surgical clinics (51). Recently, the combination of SEM and team-based care has been recommended as one way to confront the COVID-19 surgery crisis for e cient, fair, and ethical approaches in surgical care pathways (52). However, although this review has found promising results in regard to balancing the variation of total waiting time with SEM, it can be di cult to implement SEM into a referral system due to inadequate stakeholder readiness and participation (42).
Streamlining the triage of patients during referrals with standardized referral templates and resources enhances timely access to surgical services (39). Good cooperation between primary care practitioners and surgeons is important for a good referral system (53). A substantial proportion of referrals could be dealt with through simple communication between the general practitioner and the consultant surgeon and, in some settings, general practitioners and specialists have worked together to produce guidelines for the types of patients that should be referred (25). Not limiting to that, higher quality referral communications has instigated improved cost-effectiveness in surgical care (54).
Although the studies included in this review showed variable interventions with medium-high quality evidence, our ndings have con rmed a consistently positive outcome with improvements to the timeliness of referrals at the primary care level and Table 5 summarises the relevant information on referral practices noted in the included studies.
Coyle et al., Do et al.,

Limitations
This review is focused solely on the effects of referral methods that were intended to reduce waiting times for elective surgeries and which were reported in research studies published since 2013. It does not provide insights into the effects on waiting times of other forms of referral management that might be implemented for other purposes or on the research literature from before 2014. However, most literature suggests that the primary intent of referral management should be on prioritising care for patients most in need. Our limiting of the search to articles published in 2014-2019 may mean that we have failed to include some studies that would provide useful information. However, the use of systematic reviews published in this time window provides some insight into the older literature and our focus on recent evidence should increase the applicability of the results for contemporary practice in health systems. Our inclusion of observational studies helps to triangulate the constructs of interventional studies but brings with it concerns about their higher risk of bias. Finally, we have not been able to present meta-analyses of the effects of the interventions or determine if publication bias has impacted on our conclusions.

Conclusions
On the basis of available evidence, managing referrals by using triage and prioritisation of surgical patients is likely to reduce the waiting times for elective surgeries, by avoiding the overcrowding of surgical clinics with non-surgical referrals. Explicit and standard referral guidelines are more likely to be effective in selecting potential surgical patients if structured referral formats are used. In addition, using non-clinical information on a patient's preferences for scheduling and switching their surgeon should also increase the timeliness of elective surgical care, and timeliness of patient ow may be increased with single entry models (SEM) for elective surgical services. Implementation of these interventions should mean that primary care practitioners and surgeons experience more streamlined approach to elective care referrals. In summary, this review has identi ed some interventions that could be implemented in the referral process for adult elective surgery that might shorten waiting times and reduce the length of waiting lists.