Telehealth interventions for reducing waiting lists and waiting times for specialist outpatient services: A scoping review

Abstract

We undertook a scoping review of the published literature to identify and summarise key findings on the telehealth interventions that influence waiting times or waiting lists for specialist outpatient services. Searches were conducted to identify relevant articles. Articles were included if the telehealth intervention restructured or made the referral process more efficient. We excluded studies that simply increased capacity. Two categories of interventions were identified – electronic consultations and image-based triage. Electronic consultations are asynchronous, text-based provider-to-provider consultations. Electronic consultations have been reported to obviate the need for face-to-face appointments between the patient and the specialist in between 34–92% of cases. However, it is often reported that electronic consultations are appropriate in less than 10% of referrals for outpatient care. Image-based triage has been used successfully to reduce unnecessary or inappropriate referrals and was used most often in dermatology, ophthalmology and otolaryngology (ENT). Reported reduction rates for face-to-face appointments by specialty were: dermatology 38–88%, ophthalmology 16–48% and ENT 89%. Image–based triage can be twice as effective as non-image based triage in reducing unnecessary appointments. Telehealth interventions can effectively be used to reduce waiting lists and improve the coordination of specialist services, and should be considered in conjunction with clinical requirements.

Keywords

Telemedicine outpatient clinics electronic consultations triage

Introduction

In Australia, specialist outpatient care is provided predominantly by public hospitals via outpatient clinics.¹ Outpatient clinics involve consultations with medical specialists, allied health practitioners or nurse practitioners. Demand for specialist outpatient care is growing, and a review of general practice activity identified that referrals for specialist outpatient care increased from 12.0 to 15.9 per 100 general practice encounters between 2005–2015, resulting in an additional 9.7 million specialist outpatient episodes of care.² Growth in demand for specialist services is not unique to Australia. In the USA, by 2025, the demand for 10 common medical specialties is predicted to exceed current levels by greater than 14%.³

Increasing demand has resulted in long waiting lists for specialist outpatient services in Australia’s public hospital system.⁴ Similar problems exist in many other developed countries – more than half of doctors in Canada, France, Germany, New Zealand and Norway recognised that patients often faced long waiting times to see specialists.⁵ Long waiting times for outpatient services may cause distress among patients, have adverse health consequences and be perceived as inappropriate delivery and planning of healthcare.⁶ Hence, improving the timeliness of outpatient care has been recognised as a priority for improvement in healthcare systems.⁷

Alternatives to the current face-to-face consultations may potentially improve the timeliness of care. One such alternative is telehealth. The aims of this study were to identify telehealth interventions that influence waiting times or waiting lists for specialist outpatient services and to collate and summarise key characteristics of these interventions.

Methods

We performed a scoping review of literature to identify telehealth interventions for reducing waiting times or waiting lists for specialist outpatient services. A scoping review (as opposed to a systematic review) is warranted when synthesis of research findings needs to be exploratory, rapid, pragmatic and is intended to inform subsequent stages of investigation.⁸ In keeping with the pragmatic goal, we summarised research findings pertinent to the policy maker namely: the effectiveness of identified interventions for reducing waiting times and organisational effects (e.g. resources and infrastructure required to implement the intervention). Whilst waiting times or waiting lists were the primary effectiveness measures, we also reported secondary outcome measures (e.g. relative cost) when reported in included articles.

Search strategies

We searched the PubMed, Embase and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using multiple Boolean searches comprised of medical subject heading (MeSH) terms and keyword search terms (Table 1). Searches were constrained to the previous ten years to include only contemporary interventions. Search results were combined and screened for inclusion by two of the study authors. Conflicts were resolved by consensus discussion between all authors.

Table 1.

Search terms.

MeSH terms	Telemedicine, outpatients, ambulatory care, triage, waiting lists
Keywords	Telehealth, electronic consultation, e-consultation, remote consultation, virtual consultation, referral and secondary care

MeSH: medical subject heading.

Inclusion criteria

Articles that reported waiting lists or waiting times as an outcome measure in a telehealth intervention were included in the review. We excluded articles that simply increased the capacity of a specialist outpatient service by adding additional telehealth clinics. Instead, we sought to find interventions that restructured or made the referral or consultation process more efficient, for example – interventions that reduced consultation time, triaged referrals, reduced inappropriate or unnecessary referrals, supported management of patients in primary care or ensured there was definitive care at first outpatient appointment. Prioritisation may lead to patients with urgent medical conditions having reduced waiting times but this is often counterbalanced by increased waiting times for patients with non-urgent medical conditions. Hence, articles that reported simple prioritisation of patients were excluded based on the rationale there is no impact on numbers of patients on the waiting list or net waiting time. Further inclusion and exclusion criteria are listed in Table 2.

Table 2.

Inclusion and exclusion criteria.

Inclusion	Exclusion
Synchronous, asynchronous or hybrid telehealth services	Telephone services
Medical specialty, allied health or nursing services	Dental services
Outpatient services	Admitted patient services
Consultative services	Non-consultative services (e.g. surgical waitlist, emergency department triage, diagnostic procedures, procedural appointments)

Results

The categorisation of included studies elicited two predominant interventions which affect waiting lists or waiting times: electronic consultations and image-based triage of referrals.

Electronic consultations

Electronic consultations are asynchronous, text-based, store-and-forward consultations between a referring clinician and a specialist.⁹ The aim of an electronic consultation is to improve timely access to specialist advice about a clinical question which may obviate the need for a face-to-face visit between the patient and the specialist. Our searches and subsequent filtering of results identified 12 published articles that reported impact on waiting list or waiting times resulting from electronic consultations. Details of included studies are summarised in Table 3.

Table 3.

Summary of electronic consultation studies.

First author, year	Institution, country	Medical speciality	Sample size	Outcome measures	Outcome	Electronic consultations per total referrals
Wrenn,¹⁰ 2016	University of California San Francisco, USA	Cardiology, endocrinology, gastroenterology/hepatology, haematology, infectious diseases, nephrology, pulmonary medicine, rheumatology and sleep medicine	n = 200 (pooled from all medical specialties)	Number of avoided face-to-face appointments in the 6 month time period following electronic consultation.	n = 172 (86%)	8%
Wasfy,¹¹ 2016	Massachusetts General Hospital, USA	Cardiology	n = 165	Number of avoided face-to-face appointment in the 6 month time period following electronic consultation.	n = 124 (76%)	9%
Tejera Segura,¹² 2016	Spain	Rheumatology	n = 938	Number of avoided face-to-face appointments.	n = 500 (53%)	NR
Shehata,¹³ 2016	Champlain BASE, Ottawa, Canada	Obstetrics and gynaecology	n = 394	Number of avoided face-to-face appointments as per primary care provider's opinion.	n = 135 (34%)	NR
Pecina,¹⁴ 2016	Mayo Clinic Rochester, USA	Pulmonary, psychiatry, nephrology, infectious disease, haematology, gastroenterology, endocrinology and cardiology	n = 5115	Number of avoided face-to-face appointments in the 28 days following electronic consultation.	n = 4568 (89%)	NR
Olayiwola,¹⁵ 2016	Connecticut Health Centre Inc., USA	Cardiology	n (intervention) = 229; n (control) = 361	Number of avoided face-to-face appointments. Median waiting times.	1. n = 89 (63%) 2. 5 days intervention electronic consultation; 29 days intervention non-electronic consultations; 25 days control.	52%
Liddy,¹⁶ 2016	Champlain BASE, Ottawa, Canada	Chronic pain	n = 199	Number of avoided face-to-face appointments as per opinion of the primary care provider.	n = 72 (36%)	NR
Foge,¹⁷ 2016	Champlain BASE, Ottawa, Canada	Haematology	n = 436	Number of avoided face-to-face appointments as per opinion of the primary care provider.	n = 288 (66%)	NR
North,¹⁸ 2015	Mayo Clinic Rochester, USA	Pulmonary, psychiatry, nephrology, infectious disease, haematology, gastroenterology, endocrinology and cardiology	n = 5334	1. Number of avoided face-to-face appointments within 30 days. 2. Number of avoided face-to-face appointments within 30 days.	1. n = 4720 (89%) 2. n = 3476 (82%)	NR
Chittle,¹⁹ 2015	Massachusetts General Hospital, USA	Vascular medicine	n = 59	Number of avoided face-to-face appointments in the 90 days following an electronic consultation.	n = 54 (92%)	3.30%
Keely,²⁰ 2013	Champlain BASE, Ottawa, Canada	Multi-specialty	n = 406	Number of avoided face-to-face appointments as per opinion of the primary care provider.	n = 175 (43%)	NR
Palen,²¹ 2012	Kaiser Permanente, Colorado, USA	Multi-specialty	n = 270	Number of avoided face-to-face appointments.	n = 111 (41%)	3.70%

NR: not reported.

The success of electronic consultations is reliant on the referrer providing a well-articulated clinical question.⁹ To facilitate this, specialty-specific templates for the electronic consultations are used. The purpose of these templates is to provide decision supports to help the referrer identify which medical conditions are suitable to electronic consultations and to ensure completeness of referral information. One cardiology service reported that over 50% of referrals were suitable for an electronic consultation.¹⁵ However, most services report that less than 10% of referrals are suitable for electronic consultations (Table 3).

Effectiveness

Waiting lists

The effectiveness of electronic consultations to obviate face-to-face appointments has been investigated in a number of studies and has been reported to be between 34%¹³ and 92%.¹⁹ When stratified by speciality there is not a great variation in the proportion of avoided face-to-face appointments with the exception of infectious diseases. Electronic consultations for infectious diseases resulted in face-to-face appointments being avoided in 92–97% of cases^14,18 which is substantially higher than any other medical specialty.

The use of electronic consultation may have a negative effect on waiting lists. In one study, specialists estimated that 27% of electronic consultations represented new work (i.e. consultations that would not have occurred in the absence of electronic consultations).⁹

Waiting times

Waiting times were the outcome measure in one randomised control trial.¹⁵ Median waiting time for a cardiology consultation was 2.5 days for an electronic consultation, 29 days for an electronic consultation that had been converted to a face-to-face appointment and 25 days for a conventional face-to-face consultation.

Cost analysis

A number of studies have provided comparative cost analysis of electronic consultations versus face-to-face consults from the perspective of the health care provider. For example, Scherpbier-de Haan et al.²² reported the cost of electronic consultations was less than face-to-face consultations (€107 versus €600) and similarly Liddy et al.²³ estimated the cost of an electronic consultation was half that of a face-to-face visit (Can$75 versus Can$150). One study reported Veteran’s Health Administration saved nearly $3 million dollars in travel reimbursements by using electronic consultations.²⁴

Organisation effects

Infrastructure

Contemporary electronic consultation systems are most often implemented using a shared electronic medical record (EMR) or a dedicated web-based platform. Functionality with an EMR allows a referring doctor to order an electronic consultation in a similar way to how they would order radiology or pathology.¹⁴ The use of email for electronic consultations has been previously reported.^25,26 However, due to privacy concerns email may not be appropriate for use as an electronic consultation system. Web-based products may²² or may not²⁷ have integration with a patient’s medical record to automate the inclusion of the patient history as part of the consult question and the specialist’s response in the patient record.

Resources

The time to undertake an electronic consultation has been examined in a number of studies. Times ranged from 8 min to more than 20 min. No studies provided comparison for face-to-face consultations. In one service, specialists recorded the time they spend as part of the consultation – the self-reported consultation times from this study were: less than 10 min in 57% of cases, 10–15 min in 34%, 15–20 min in 9%, and over 20 min in 1%.²⁰

Potentially the biggest impact of electronic consultation is on the referrer. Receiving the specialist’s response generates additional work that may have fallen to the specialist in the case of a face-to-face consultation.⁹ As the patient is not seen by the specialist, the referrer has the responsibility to communicate the specialist’s recommendations. Relative to the specialist, there is limited evaluation of the time taken to prepare an electronic consultation. However, around 10 min has been reported by two authors.^9,28 Electronic consultations are also reported to have educational value for general practitioners.²⁹

Image-based triage

Image-based triage involves the referrer (or an intermediary) packaging images to accompany the referral and the specialist performing either triage or a store-and-forward consultation based on the transmitted information. Our searches and subsequent filtering of results identified 15 published articles on the influence image-based triage has on outpatient service waiting list and waiting times. It was identified that image-based triage is practiced in the visually-oriented specialties of dermatology, ophthalmology, otolaryngology and vascular surgery (wound care). The largest volume of published research was on dermatology (n = 8, 53%).

Effectiveness

Image-based triage has been compared to non-image based triage in a number of studies. In a dermatology randomised control trial, twice as many face-to-face appointments were avoided using image-based triage than non-image based triage.³⁰ Similar proportions of avoided appointments were reported in a controlled ear, nose, and throat (ENT) study.³¹

Waiting lists

An avoided face-to-face appointment was an outcome measure in 10 of the reviewed studies. For dermatology, the reported rate of avoided face-to-face appointments ranged from 38–88% and for ophthalmology ranged from 16–48%. Single studies for ENT³² and vascular surgery/wound care³³ reported an 89% and 18% reduction in face-to-face appointments, respectively.

Avoided face-to-face appointments were most often (93%) achieved by reducing unnecessary or inappropriate referrals – for example, patients who could be managed in primary care. For these cases the triage process included teleconsultation which informed the referrer of a diagnosis and a suggested management plan. Another strategy that helped reduce face-to-face waiting lists was ensuring there was definitive care at first appointment by booking diagnostic procedures before or during face-to-face visit.^33–36

Image-based triage may have a negative effect on waiting lists. In one study, the number of face-to-face appointments increased despite 195 teledermatology referrals.³⁷ This was presumably due to the novelty value of access to new mobile-device dermoscopes or a decreased threshold in referral reasons due to increased convenience of referral.

Waiting times

The subsequent effect that reducing waiting lists had on waiting times was also reported by a number of authors. Median waiting time for ophthalmology outpatient clinics reduced from 14 weeks to four weeks after implementation of retinal imaging-based referrals.³⁸ Waiting times to see an ENT surgeon were reduced from 4.2 months to 2.5 months.³²

All studies and outcome measures are summarised in Table 4.

Table 4.

Summary of image-based triage studies.

First author, year	Medical speciality	Sample size	Outcome measures	Outcome
Borve,³⁹ 2014	Dermatology	n (cases) = 816 n (patients) = 746	Number of patients where no F2F appointment was required.	n = 298 (40%)
Eminovic,³⁰ 2009	Dermatology	Intervention n (patients) = 200 n (GPs) = 46 Control n (patients) = 169 n (GPs) = 39	Number of cases where no F2F appointment was required (intervention/control).	Intervention/control n = 78 (39%)/n = 31 (18.3%)
Ford,³⁷ 2015	Dermatology	n (referrals) = 195; n (intervention practices) = 5; n (control practices) = 18	Change in the number of secondary care referrals per practice population.	Additional secondary care referral per 1389 practice population (unadjusted). Adjusted for age and sex there was a statistically significant increase in secondary care referral.
Lester,⁴⁰ 2014	Dermatology	n (patients) = 63	Number of patients where no F2F appointment was required.	n = 24 (38%)
Lim,³⁵ 2011	Dermatology	n (patients) = 200	Number of patients who could be managed in primary care. Number of patients who were discharged (no treatment necessary). Total number of patients where no F2F appointment was required.	n = 59 (30%) n = 117 (59%) n = 176 (88%)
Moreno-Ramirez,⁴¹ 2007	Dermatology	n (patients) = 2009	Number of patients where no F2F appointment was required.	n = 1028 (51.2%)
Morton,³⁶ 2010	Dermatology	Control/intervention n (patients) = 188 n (patients) = 289	1. Number of patients receiving definitive care at first outpatient visit. 2. Number of patients triaged to non-medical secondary care. 3. Mean waiting time.	1. Definitive care at first visit. Intervention/control n = 263 (91%)/n = 118 (63%) 2. n = 281 (72%) 3. Mean waiting times (intervention/control) Melanoma: 36 days/39 days SCC: 28 days/50 days BCC: 35 days/58 days
Tan,⁴² 2010	Dermatology	n (patients) = 200	Number of patients who could be managed in primary care.	n = 136 (74%)
Hofstetter,³² 2010	ENT	n (new referrals) = 1690	1. Average (range) waiting times for F2F ENT consultation. 2. Number of avoided F2F appointments for new patients.	1. Pre-test/post-test 4.2 months (range 3.7–5.3 months)/2.5 months (range 1.7–3.0 months) 2. n = 1504 (89%)
Kokesh,⁴³ 2009	ENT	n (patients) = 1458	1. Number of patients discharged from service without having to see an otolaryngologist. 2. Number of patients referred to another speciality. 3. Number of cases where travel was avoided.	1. n = 394 (27%) 2. n = 73 (5%) 3. n = 1239 (85%)
Reeve,³¹ 2014	ENT	Control/Intervention: n(patients) = 148/n(patients) = 710	1. Number of patients referred for ENT review. 2. Waiting time for review.	1. Control/Intervention: n = 32 (22%)/n = 66 (9%) 2. Control/Intervention: 141 d (IQR 67–435 d)/22 d (IQR 8–49 d)
Borooah,³⁸ 2012	Ophthalmology	Control/intervention: n (new patients) = 7462/ n (new patients = 8714	1. Median waiting times. 2. Number of avoided F2F appointments.	1. Control/intervention: 14 weeks/4 weeks 2. n = 1359 (16%)
Hanson,³⁴ 2008	Ophthalmology	n (patients) = 171	1. Number of cases where no F2F appointment was required. 2. Number of patients able to undergo treatment during the F2F appointment. 3. Number of patients able to undergo diagnostic testing during the F2F appointment.	1. n = 82 (48%) 2. n = 21 (84%) 3. n = 49 (98%)
Kelly,⁴⁴ 2011	Ophthalmology	n (patients) = 50	Number of patients managed in primary care.	n = 17 (34%)
Hands,³³ 2006	Vascular surgery	n (patients) = 38	1. Number of patients discharged from secondary after initial video consultation/image review.	1. n = 10 (26%)

BCC: basal cell carcinoma; ENT: ear, nose and throat; F2F: face-to-face; IQR: inter-quartile range; SCC: squamous cell carcinoma.

Organisation effects

Dermatology

Nearly all of the contemporary teledermatology triage services used dermoscopic imaging in conjunction with digital photography due to the superior diagnostic accuracy of dermoscopy relative to digital photography.⁴⁵ Commercial teledermoscopy systems were used as the transport mechanism from referrer to the specialist. These were either web client/server^30,35 or mobile device client (e.g. iPhone)/server^37,39 systems. Other transport options include a shared EMR used by Veteran’s Health Administration.⁴⁰

Ophthalmology

Tele-ophthalmology referrals involve digital photography, slit-lamp imaging,⁴⁶ retinal imaging³⁴ and optical coherence tomography,⁴⁴ as well as results from other diagnostic tests including intra-ocular pressure and visual acuity. Transport mechanisms include secure intranet email and commercial web-based tele-ophthalmology systems. Imaging was performed by either the general practitioner (GP) or community-based optometrists.

ENT

The ENT services described in included studies involved a travelling audiologist or nurse packaging diagnostic information including otoscopic images, tympanometry and audiometry for the subsequent store-and-forward review by an ENT surgeon. ENT had the highest reported rate of avoided face-to-face appointments (89%).³²

Discussion

There is some evidence to show that electronic consultations and image-based triage of referrals can be effective in reducing waiting lists and waiting times. Variation in the reported effectiveness of electronic consultation may be explained by the type of electronic consultation system. More comprehensive patient history and referral information being available to the specialist when using and integrated EMR/electronic consultation system may have accounted for the higher number of avoided faced-to-face consultations^14,18 relative to web-based systems.²⁰ Further, variation may occur due to the time of measurements and, for example, two studies from the same provider reported a variation in the number of face-to-face appointments avoided. Around 90% of face-to-face appointments were avoided up to 28 days after the electronic consultation,¹⁴ whereas when the time period was extended to 90 days the number of avoided face-to-face appointments was reported to be around 80%.¹⁸

Most reports of electronic consultations were from integrated healthcare providers in the USA. Favourable reimbursement models resulting from the Affordable Care Act may have accelerated adoption in these organisations. Research on successful electronic consultation systems has identified executive and clinician leadership, and established funding models for specialist clinician reimbursement as integral to the success of electronic consultations.⁴⁷

It is recognised that an image-based, store-and-forward dermatology referral increases the duration of the GP consultation.⁴⁸ It has been reported that teledermatology services in Australia are underutilised by GP referrers.⁴⁹ One possible reason is the economic disincentive for GPs due to the increased time to image the patient and package the referral. General practitioners were the photographers in a number of studies included in this review.^30,37,39 However, these studies originated from countries where general practice is not solely funded by fee-for-service models. A number of studies from New Zealand^35,42 have used an intermediary as the photographer. Potential teledermatology referral services should consider adopting a similar model to enhance compliance to image-based triage.

All telehealth interventions that resulted in a decrease in waiting lists or waiting times used store-and-forward methods. Simple substitution of face-to-face consultations with real-time video consultations may be consultation time neutral or in some circumstance more time consuming.⁵⁰ As a result, real-time consultations can have no effect on waiting lists and waiting times. One study demonstrated that complex reorganisation of services involving coordinating care with community providers may result in decreased video consultation time for the specialist.⁵¹ However, this study did not report on resultant changes to waiting lists for the specialty service.

Limitations

Confidence in some of the reported findings from studies included in this review may be limited by small sample sizes and reporting of results from pilot projects.¹⁹ The limited reporting of negative outcomes raises concerns of publication bias. Multiple publications about the same service^13,16,17,20 may also bias findings.

Conclusion

This review has shown that, in some circumstances, telehealth interventions can be effectively used to reduce waiting lists and waiting times for specialist outpatient services. The reduction in waiting lists can be achieved without increasing the capacity of the service. The identified telehealth interventions almost exclusively use store-and-forward methods. Introducing these interventions has required significant service re-organisation, the implementation of additional infrastructure and consideration of the appropriate context for use. Developing funding models to support store-and-forward consultations may be required as part of the requisite service re-organisation.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by Metro South Health.

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